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Dmytriv V, Bembenek M, Banha V, Dmytriv I, Dzienniak D, Nurkusheva S. Modeling of the Efficiency of the Centrifugal Conical Disk Dispenser of Bulk Materials. Materials (Basel) 2024; 17:1815. [PMID: 38673171 PMCID: PMC11051175 DOI: 10.3390/ma17081815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
Centrifugal disk dispensers are widely used in various tasks of dosing bulk, dispersed materials. The design of the disk depends on the physical and mechanical characteristics of the dosing medium. The work discusses the development of an analytical model of the movement of a material particle along a conical centrifugal disk depending on the kinematic characteristics of the dosing process and the characteristics of the dosing material, as well as experimental confirmation of the theoretical model, which is relevant for the calculation and design of working elements of this type. The obtained system of differential equations is solved using the Runge-Kutta numerical method. Experimental studies were carried out using the method of a planned factorial experiment. The experiment was conducted for three factors at three levels. The feedback criterion was the performance of a centrifugal conical disk dispenser for bulk materials. The disk cone angle was set at 10, 20, and 30°. The disk diameter was 130, 150, and 170 mm, the gap between the disk and the edge of the hopper neck was 6, 8, and 10 mm, and the rotational speed of the conical disk was 0.65, 1.02, and 1.39 rad/s. The dispensing rate of the dispenser ranged from 15 to 770 g/s, depending on the values of the experimental factors. For use in the regression equation of the natural values of the factors, a method of transforming the terms of the equation from coded values to natural ones is provided. The obtained experimental correlation dependencies were checked for reproducibility with Cochrane's test, and the adequacy of the model was checked using Fisher's test. The significance of the coefficients in the correlation equation was evaluated using the Student's t-test. The difference between the experimental data and the results of the theoretical modeling does not exceed 5%. The obtained system of differential equations makes it possible to model the radial velocity of the ascent of bulk material from the conical rotating disk depending on the rotation frequency, disk diameter, and the height of the annular gap between the discharge throat of the hopper and the conical disk. The analytical model enables the modeling of the productivity of the conical dispenser for bulk materials for arbitrary parameters of rotation frequency, disk diameter, and the size of the annular gap between the discharge throat of the hopper and the conical disk.
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Affiliation(s)
- Vasyl Dmytriv
- Department of Design Machine and Automotive Engineering, Lviv Polytechnic National University, 79013 Lviv, Ukraine;
| | - Michał Bembenek
- Department of Manufacturing Systems, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, A. Mickiewicza 30, 30-059 Krakow, Poland;
| | - Vasyl Banha
- Department of Design Machine and Automotive Engineering, Lviv Polytechnic National University, 79013 Lviv, Ukraine;
| | - Ihor Dmytriv
- Department of Motor Vehicle Transport, Lviv Polytechnic National University, 79013 Lviv, Ukraine;
| | - Damian Dzienniak
- Department of Manufacturing Systems, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, A. Mickiewicza 30, 30-059 Krakow, Poland;
| | - Saltanat Nurkusheva
- Department of Organization of Transport, Traffic and Transport Operations, L. N. Gumilyov Eurasian National University, Satbaev 2, Astana 010000, Kazakhstan;
- Department of Transport Equipment and Technologies, S. Seifullin Kazakh Agrotechnical Research University, Zhenis 62B, Astana 010011, Kazakhstan
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Borrus DS, Stettler MK, Grover CJ, Kalajian EJ, Gu J, Conradi Smith GD, Del Negro CA. Inspiratory and sigh breathing rhythms depend on distinct cellular signalling mechanisms in the preBötzinger complex. J Physiol 2024; 602:809-834. [PMID: 38353596 PMCID: PMC10940220 DOI: 10.1113/jp285582] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/21/2023] [Indexed: 02/21/2024] Open
Abstract
Breathing behaviour involves the generation of normal breaths (eupnoea) on a timescale of seconds and sigh breaths on the order of minutes. Both rhythms emerge in tandem from a single brainstem site, but whether and how a single cell population can generate two disparate rhythms remains unclear. We posit that recurrent synaptic excitation in concert with synaptic depression and cellular refractoriness gives rise to the eupnoea rhythm, whereas an intracellular calcium oscillation that is slower by orders of magnitude gives rise to the sigh rhythm. A mathematical model capturing these dynamics simultaneously generates eupnoea and sigh rhythms with disparate frequencies, which can be separately regulated by physiological parameters. We experimentally validated key model predictions regarding intracellular calcium signalling. All vertebrate brains feature a network oscillator that drives the breathing pump for regular respiration. However, in air-breathing mammals with compliant lungs susceptible to collapse, the breathing rhythmogenic network may have refashioned ubiquitous intracellular signalling systems to produce a second slower rhythm (for sighs) that prevents atelectasis without impeding eupnoea. KEY POINTS: A simplified activity-based model of the preBötC generates inspiratory and sigh rhythms from a single neuron population. Inspiration is attributable to a canonical excitatory network oscillator mechanism. Sigh emerges from intracellular calcium signalling. The model predicts that perturbations of calcium uptake and release across the endoplasmic reticulum counterintuitively accelerate and decelerate sigh rhythmicity, respectively, which was experimentally validated. Vertebrate evolution may have adapted existing intracellular signalling mechanisms to produce slow oscillations needed to optimize pulmonary function in mammals.
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Affiliation(s)
- Daniel S. Borrus
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
| | - Marco K. Stettler
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
| | - Cameron J. Grover
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
| | - Eva J. Kalajian
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
| | - Jeffrey Gu
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
| | - Gregory D. Conradi Smith
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
- Conradi Smith and Del Negro contributed equally
| | - Christopher A. Del Negro
- Applied Science and Neuroscience, William & Mary, Williamsburg, VA 23185
- Conradi Smith and Del Negro contributed equally
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Bhat R, Khanday MA. Traveling wave solution and the stability of critical points of an enzyme-inhibitor system under diffusion effects: with special reference to dimer molecule. Comput Methods Biomech Biomed Engin 2024:1-7. [PMID: 38321971 DOI: 10.1080/10255842.2024.2311321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/21/2024] [Indexed: 02/08/2024]
Abstract
Enzymes are absolutely essential biological catalysts in human body that catalyze all cellular processes in physiological network. However, there are certain low molecular weight chemical compounds known as inhibitors, that reduce or completely inhibit the enzyme catalytic activity. Mathematical modeling plays a key role in the control and stability of metabolic enzyme inhibition. Enzyme stability is an important issue for protein engineers, because of its great importance and impact on optimal utility of material in biological tissues. In this outlook, we have first determined the existence of traveling wave solution for the enzyme-inhibitor system and then emphasized the stability of critical points that arise in the reactions. The study of traveling wave solution of an enzyme-inhibitor system with reaction diffusion equations involve quite complex mathematical analysis. The results obtained in this model indicate that the traveling wave solution may give a well explained method for improving enzyme kinetic stability. The present study will be helpful in understanding the stability of critical points of an enzyme-inhibitor system to give an idea about the inhibition of less stable enzymes. Moreover, the role of diffusion on the enzyme activity has been exhaustively discussed using mathematical tools related to eigen values and eigen function analysis.
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Affiliation(s)
- Roohi Bhat
- Department of Mathematics, University of Kashmir, Srinagar, India
- Department of Mathematics, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab
| | - M A Khanday
- Department of Mathematics, University of Kashmir, Srinagar, India
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Alarid-Escudero F, Andrews JR, Goldhaber-Fiebert JD. Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics. Med Decis Making 2024; 44:5-17. [PMID: 37953597 DOI: 10.1177/0272989x231205565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
BACKGROUND Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. DESIGN We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. RESULTS Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. CONCLUSIONS ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. HIGHLIGHTS Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).
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Affiliation(s)
- Fernando Alarid-Escudero
- Department of Health Policy, School of Medicine, Stanford University, Stanford, CA, USA
- Center for Health Policy, Freeman Spogli Institute, Stanford University, Stanford, CA, USA
| | - Jason R Andrews
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Jeremy D Goldhaber-Fiebert
- Department of Health Policy, School of Medicine, Stanford University, Stanford, CA, USA
- Center for Health Policy, Freeman Spogli Institute, Stanford University, Stanford, CA, USA
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Wang Y, Bergman DR, Trujillo E, Pearson AT, Sweis RF, Jackson TL. Mathematical model predicts tumor control patterns induced by fast and slow cytotoxic T lymphocyte killing mechanisms. Sci Rep 2023; 13:22541. [PMID: 38110479 PMCID: PMC10728095 DOI: 10.1038/s41598-023-49467-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/08/2023] [Indexed: 12/20/2023] Open
Abstract
Immunotherapy has dramatically transformed the cancer treatment landscape largely due to the efficacy of immune checkpoint inhibitors (ICIs). Although ICIs have shown promising results for many patients, the low response rates in many cancers highlight the ongoing challenges in cancer treatment. Cytotoxic T lymphocytes (CTLs) execute their cell-killing function via two distinct mechanisms: a fast-acting, perforin-mediated process and a slower, Fas ligand (FasL)-driven pathway. Evidence also suggests that the preferred killing mechanism of CTLs depends on the antigenicity of tumor cells. To determine the critical factors affecting responses to ICIs, we construct an ordinary differential equation model describing in vivo tumor-immune dynamics in the presence of active or blocked PD-1/PD-L1 immune checkpoint. Specifically, we identify important aspects of the tumor-immune landscape that affect tumor size and composition in the short and long term. We also generate a virtual cohort of mice with diverse tumor and immune attributes to simulate the outcomes of immune checkpoint blockade in a heterogeneous population. By identifying key tumor and immune characteristics associated with tumor elimination, dormancy, and escape, we predict which fraction of a population potentially responds well to ICIs and ways to enhance therapeutic outcomes with combination therapy.
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Affiliation(s)
- Yixuan Wang
- Department of Mathematics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Daniel R Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Erica Trujillo
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, 60637, USA
| | - Alexander T Pearson
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, 60637, USA
| | - Randy F Sweis
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, 60637, USA.
| | - Trachette L Jackson
- Department of Mathematics, University of Michigan, Ann Arbor, MI, 48109, USA.
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Lacy A, Igoe M, Das P, Farthing T, Lloyd AL, Lanzas C, Odoi A, Lenhart S. Modeling impact of vaccination on COVID-19 dynamics in St. Louis. J Biol Dyn 2023; 17:2287084. [PMID: 38053251 DOI: 10.1080/17513758.2023.2287084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023]
Abstract
The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St. Louis area. A COVID-19 model with a system of ordinary differential equations was developed to illustrate the dynamics with a fully vaccinated class. Using the weekly number of vaccinations, cases, and hospitalization data from five counties in the greater St. Louis area in 2021, parameter estimation for the model was completed. The transmission coefficients for each county changed four times in that year to fit the model and the changing behaviour. We predicted the changes in disease spread under scenarios with increased vaccination coverage. SafeGraph local movement data were used to connect the forces of infection across various counties.
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Affiliation(s)
- Alexanderia Lacy
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Morganne Igoe
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Praachi Das
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Trevor Farthing
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Alun L Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Cristina Lanzas
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostics Sciences, University of Tennessee, Knoxville, TN, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
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7
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Laurie M, Lu J. Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE. NPJ Syst Biol Appl 2023; 9:58. [PMID: 37980358 PMCID: PMC10657412 DOI: 10.1038/s41540-023-00317-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/23/2023] [Indexed: 11/20/2023] Open
Abstract
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.
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Affiliation(s)
- Mark Laurie
- Modeling & Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - James Lu
- Modeling & Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA, USA.
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8
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Wüstner D, Dupont Juhl A, Egebjerg JM, Werner S, McNally J, Schneider G. Kinetic modelling of sterol transport between plasma membrane and endo-lysosomes based on quantitative fluorescence and X-ray imaging data. Front Cell Dev Biol 2023; 11:1144936. [PMID: 38020900 PMCID: PMC10644255 DOI: 10.3389/fcell.2023.1144936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Niemann Pick type C1 and C2 (NPC1 and NPC2) are two sterol-binding proteins which, together, orchestrate cholesterol transport through late endosomes and lysosomes (LE/LYSs). NPC2 can facilitate sterol exchange between model membranes severalfold, but how this is connected to its function in cells is poorly understood. Using fluorescent analogs of cholesterol and quantitative fluorescence microscopy, we have recently measured the transport kinetics of sterol between plasma membrane (PM), recycling endosomes (REs) and LE/LYSs in control and NPC2 deficient fibroblasts. Here, we use kinetic modeling of this data to determine rate constants for sterol transport between intracellular compartments. Our model predicts that sterol is trapped in intraluminal vesicles (ILVs) of LE/LYSs in the absence of NPC2, causing delayed sterol export from LE/LYSs in NPC2 deficient fibroblasts. Using soft X-ray tomography, we confirm, that LE/LYSs of NPC2 deficient cells but not of control cells contain enlarged, carbon-rich intraluminal vesicular structures, supporting our model prediction of lipid accumulation in ILVs. By including sterol export via exocytosis of ILVs as exosomes and by release of vesicles-ectosomes-from the PM, we can reconcile measured sterol efflux kinetics and show that both pathways can be reciprocally regulated by the intraluminal sterol transfer activity of NPC2 inside LE/LYSs. Our results thereby connect the in vitro function of NPC2 as sterol transfer protein between membranes with its in vivo function.
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Affiliation(s)
- Daniel Wüstner
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Alice Dupont Juhl
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Jacob Marcus Egebjerg
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Stephan Werner
- Department of X-Ray Microscopy, Helmholtz-Zentrum Berlin, Berlin, Germany
| | - James McNally
- Department of X-Ray Microscopy, Helmholtz-Zentrum Berlin, Berlin, Germany
| | - Gerd Schneider
- Department of X-Ray Microscopy, Helmholtz-Zentrum Berlin, Berlin, Germany
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9
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Policicchio BB, Cardozo-Ojeda EF, Xu C, Ma D, He T, Raehtz KD, Sivanandham R, Kleinman AJ, Perelson AS, Apetrei C, Pandrea I, Ribeiro RM. CD8 + T cells control SIV infection using both cytolytic effects and non-cytolytic suppression of virus production. Nat Commun 2023; 14:6657. [PMID: 37863982 PMCID: PMC10589330 DOI: 10.1038/s41467-023-42435-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/11/2023] [Indexed: 10/22/2023] Open
Abstract
Whether CD8+ T lymphocytes control human immunodeficiency virus infection by cytopathic or non-cytopathic mechanisms is not fully understood. Multiple studies highlighted non-cytopathic effects, but one hypothesis is that cytopathic effects of CD8+ T cells occur before viral production. Here, to examine the role of CD8+ T cells prior to virus production, we treated SIVmac251-infected macaques with an integrase inhibitor combined with a CD8-depleting antibody, or with either reagent alone. We analyzed the ensuing viral dynamics using a mathematical model that included infected cells pre- and post- viral DNA integration to compare different immune effector mechanisms. Macaques receiving the integrase inhibitor alone experienced greater viral load decays, reaching lower nadirs on treatment, than those treated also with the CD8-depleting antibody. Models including CD8+ cell-mediated reduction of viral production (non-cytolytic) were found to best explain the viral profiles across all macaques, in addition an effect in killing infected cells pre-integration (cytolytic) was supported in some of the best models. Our results suggest that CD8+ T cells have both a cytolytic effect on infected cells before viral integration, and a direct, non-cytolytic effect by suppressing viral production.
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Affiliation(s)
- Benjamin B Policicchio
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | | | - Cuiling Xu
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Dongzhu Ma
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Tianyu He
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Kevin D Raehtz
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ranjit Sivanandham
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Adam J Kleinman
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Alan S Perelson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Cristian Apetrei
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ivona Pandrea
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
- Laboratório de Biomatemática, Faculdade de Medicina da Universidade de Lisboa (previous address), Lisboa, Portugal.
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10
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Flores-Garza E, Hernández-Pando R, García-Zárate I, Aguirre P, Domínguez-Hüttinger E. Bifurcation analysis of a tuberculosis progression model for drug target identification. Sci Rep 2023; 13:17567. [PMID: 37845271 PMCID: PMC10579266 DOI: 10.1038/s41598-023-44569-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023] Open
Abstract
Tuberculosis (TB) is a major cause of morbidity and mortality worldwide. The emergence and rapid spread of drug-resistant M. tuberculosis strains urge us to develop novel treatments. Experimental trials are constrained by laboratory capacity, insufficient funds, low number of laboratory animals and obsolete technology. Systems-level approaches to quantitatively study TB can overcome these limitations. Previously, we proposed a mathematical model describing the key regulatory mechanisms underlying the pathological progression of TB. Here, we systematically explore the effect of parameter variations on disease outcome. We find five bifurcation parameters that steer the clinical outcome of TB: number of bacteria phagocytosed per macrophage, macrophages death, macrophage killing by bacteria, macrophage recruitment, and phagocytosis of bacteria. The corresponding bifurcation diagrams show all-or-nothing dose-response curves with parameter regions mapping onto bacterial clearance, persistent infection, or history-dependent clearance or infection. Importantly, the pathogenic stage strongly affects the sensitivity of the host to these parameter variations. We identify parameter values corresponding to a latent-infection model of TB, where disease progression occurs significantly slower than in progressive TB. Two-dimensional bifurcation analyses uncovered synergistic parameter pairs that could act as efficient compound therapeutic approaches. Through bifurcation analysis, we reveal how modulation of specific regulatory mechanisms could steer the clinical outcome of TB.
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Affiliation(s)
- Eliezer Flores-Garza
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510, Mexico, Mexico
| | - Rogelio Hernández-Pando
- Sección de Patología Experimental, Departamento de Patología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga 15, Belisario Domínguez Secc. 16, Tlalpan, 14080, Mexico City, Mexico
| | - Ibrahim García-Zárate
- Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, 04510, Mexico City, Mexico
| | - Pablo Aguirre
- Departamento de Matemática, Universidad Técnica Federico Santa María, Casilla 110-V, Valparaíso, Chile
| | - Elisa Domínguez-Hüttinger
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510, Mexico, Mexico.
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11
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Riesle AJ, Gao M, Rosenblatt M, Hermes J, Hass H, Gebhard A, Veil M, Grüning B, Timmer J, Onichtchouk D. Activator-blocker model of transcriptional regulation by pioneer-like factors. Nat Commun 2023; 14:5677. [PMID: 37709752 PMCID: PMC10502082 DOI: 10.1038/s41467-023-41507-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
Zygotic genome activation (ZGA) in the development of flies, fish, frogs and mammals depends on pioneer-like transcription factors (TFs). Those TFs create open chromatin regions, promote histone acetylation on enhancers, and activate transcription. Here, we use the panel of single, double and triple mutants for zebrafish genome activators Pou5f3, Sox19b and Nanog, multi-omics and mathematical modeling to investigate the combinatorial mechanisms of genome activation. We show that Pou5f3 and Nanog act differently on synergistic and antagonistic enhancer types. Pou5f3 and Nanog both bind as pioneer-like TFs on synergistic enhancers, promote histone acetylation and activate transcription. Antagonistic enhancers are activated by binding of one of these factors. The other TF binds as non-pioneer-like TF, competes with the activator and blocks all its effects, partially or completely. This activator-blocker mechanism mutually restricts widespread transcriptional activation by Pou5f3 and Nanog and prevents premature expression of late developmental regulators in the early embryo.
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Affiliation(s)
- Aileen Julia Riesle
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Epigenetics and Neurobiology Unit, European Molecular Biology Laboratory, EMBL Rome, Adriano Buzzati-Traverso Campus, Via Ramarini 32, 00015, Monterotondo, RM, Italy
| | - Meijiang Gao
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Signalling Research centers BIOSS and CIBSS, 79104, Freiburg, Germany
| | - Marcus Rosenblatt
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany
| | - Jacques Hermes
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany
| | - Helge Hass
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany
| | - Anna Gebhard
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
| | - Marina Veil
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany
| | - Björn Grüning
- Department of Computer Science, University of Freiburg, 79110, Freiburg, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, 79104, Freiburg, Germany
| | - Jens Timmer
- Signalling Research centers BIOSS and CIBSS, 79104, Freiburg, Germany.
- Institute of Physics, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany.
- Freiburg Center for Data Analysis and Modelling (FDM), 79104, Freiburg, Germany.
| | - Daria Onichtchouk
- Department of Developmental Biology, Albert-Ludwigs-University of Freiburg, 79104, Freiburg, Germany.
- Signalling Research centers BIOSS and CIBSS, 79104, Freiburg, Germany.
- Institute of Developmental Biology RAS, 119991, Moscow, Russia.
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12
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Ohtsu H, Hase K, Sakoda K, Aoi S, Kita S, Ogaya S. A powered simple walking model explains the decline in propulsive force and hip flexion torque compensation in human gait. Sci Rep 2023; 13:14770. [PMID: 37679376 PMCID: PMC10485060 DOI: 10.1038/s41598-023-41706-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 08/30/2023] [Indexed: 09/09/2023] Open
Abstract
Excessive hip flexion torque to prioritize leg swings in the elderly is likely to be a factor that reduces their propulsive force and gait stability, but the mechanism is not clear. To understand the mechanism, we investigated how propulsive force, hip flexion torque, and margin of stability (MoS) change when only the hip spring stiffness is increased without changing the walking speed in the simple walking model, and verified whether the relationship holds in human walking. The results showed that at walking speeds between 0.50 and 1.75 m/s, increasing hip spring stiffness increased hip flexion torque and decreased the propulsive force and MoS in both the model and human walking. Furthermore, it was found that the increase in hip flexion torque was explained by the increase in spring stiffness, and the decreases in the propulsive force and MoS were explained by the increase in step frequency associated with the increase in spring stiffness. Therefore, the increase in hip flexion torque likely decreased the propulsive force and MoS, and this mechanism was explained by the intervening hip spring stiffness. Our findings may help in the control design of walking assistance devices, and in improving our understanding of elderly walking strategies.
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Affiliation(s)
- Hajime Ohtsu
- Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka, 560-8531, Japan.
- Japan Society for the Promotion of Science, Tokyo, Japan.
| | - Kazunori Hase
- Department of Mechanical Systems Engineering, Faculty of Systems Design, Tokyo Metropolitan University, Tokyo, Japan
| | - Kouta Sakoda
- Department of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan
| | - Shinya Aoi
- Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka, 560-8531, Japan
| | - Shunsuke Kita
- Department of Health and Social Services, Graduate School of Saitama Prefectural University, Saitama, Japan
- Department of Physical Therapy, Touto Rehabilitation College, Tokyo, Japan
- Department of Rehabilitation, Soka Orthopedics Internal Medicine, Saitama, Japan
| | - Shinya Ogaya
- Department of Physical Therapy, Saitama Prefectural University, Saitama, Japan
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13
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Klocek A, Premus J, Řiháček T. Applying dynamic systems theory and complexity theory methods in psychotherapy research: A systematic literature review. Psychother Res 2023:1-17. [PMID: 37652751 DOI: 10.1080/10503307.2023.2252169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023] Open
Abstract
OBJECTIVE Dynamic systems theory and complexity theory (DST/CT) is a framework explaining how complex systems change and adapt over time. In psychotherapy, DST/CT can be used to understand how a person's mental and emotional state changes during therapy incorporating higher levels of complexity. This study aimed to systematically review the variability of DST/CT methods applied in psychotherapy research. METHODS A primary studies search was conducted in the EBSCO and Web of Knowledge databases, extracting information about the analyzed DST/CT phenomena, employed mathematical methods to investigate these phenomena, descriptions of specified dynamic models, psychotherapy phenomena, and other information regarding studies with empirical data (e.g., measurement granularity). RESULTS After screening 38,216 abstracts and 4,194 full texts, N = 41 studies published from 1990 to 2021 were identified. The employed methods typically included measures of dynamic complexity or chaoticity. Computational and simulation studies most often employed first-order ordinary differential equations and typically focused on describing the time evolution of client-therapist dyadic influences. Eligible studies with empirical data were usually based on case studies and focused on data with high time intensity of within-session dynamics. CONCLUSION This review provides a descriptive synthesis of the current state of the proliferation of DST/CT methods in the psychotherapy research field.
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Affiliation(s)
- Adam Klocek
- Faculty of Social Studies, Psychology Research Institute, Masaryk University, Brno, Czech Republic
| | | | - Tomáš Řiháček
- Faculty of Social Studies, Department of Psychology, Masaryk University, Brno, Czech Republic
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14
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Marwan M, Han M, Khan R. Generation of multi-scrolls in corona virus disease 2019 (COVID-19) chaotic system and its impact on the zero-covid policy. Sci Rep 2023; 13:13954. [PMID: 37626140 PMCID: PMC10457353 DOI: 10.1038/s41598-023-40651-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
In this paper, we discussed the impossibility of achieving zero-covid cases per day for all time with the help of fuzzy theory, while how a single case can trigger chaotic situation in the nearby city is elaborated using multi-scrolls. To accomplish this goal, we consider the number of new cases per day; [Formula: see text] to be the preferred state variable by restricting its value to the interval (0, 1). One can need to think of [Formula: see text] as a member of a fuzzy set and provide that set with appropriate membership functions. Moreover, how a single incident in one city can spread chaos to other cities is also addressed at length, using multi-scroll attractors and the signal excitation function. In addition, a bifurcation diagram of daily new instances vs the parameter [Formula: see text] is shown, elaborating that daily new cases may show a decrease under strict rules and regulations, but can again lead to chaos. Apart from biologist, this paper can play vital role for engineers as well in a sense that, a signal function can be embedded in non-symmetric systems for the creation of multi-scroll attractors in all directions using a generalized algorithm that has been designed in the current work. Finally, it is our future target to show that the covid is leading towards influenza and will be no more dangerous as was in the past.
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Affiliation(s)
- Muhammad Marwan
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua, 321004, China.
| | - Maoan Han
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Rizwan Khan
- School of Computer Science, Zhejiang Normal University, Jinhua, 321004, China
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15
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Kiselev IN, Akberdin IR, Kolpakov FA. Delay-differential SEIR modeling for improved modelling of infection dynamics. Sci Rep 2023; 13:13439. [PMID: 37596296 PMCID: PMC10439236 DOI: 10.1038/s41598-023-40008-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/03/2023] [Indexed: 08/20/2023] Open
Abstract
SEIR (Susceptible-Exposed-Infected-Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available.
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Affiliation(s)
- I N Kiselev
- FRC for Information and Computational Technologies, Novosibirsk, Russia.
- Sirius University of Science and Technology, Sirius, Russia.
- BIOSOFT.RU, Ltd, Novosibirsk, Russia.
| | - I R Akberdin
- Sirius University of Science and Technology, Sirius, Russia
- BIOSOFT.RU, Ltd, Novosibirsk, Russia
- Novosibirsk State University, Novosibirsk, Russia
| | - F A Kolpakov
- FRC for Information and Computational Technologies, Novosibirsk, Russia
- Sirius University of Science and Technology, Sirius, Russia
- BIOSOFT.RU, Ltd, Novosibirsk, Russia
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16
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Dobreva A, Camacho ET, Miranda M. Mathematical model for glutathione dynamics in the retina. Sci Rep 2023; 13:10996. [PMID: 37419948 PMCID: PMC10328985 DOI: 10.1038/s41598-023-37938-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/29/2023] [Indexed: 07/09/2023] Open
Abstract
The retina is highly susceptible to the generation of toxic reactive oxygen species (ROS) that disrupt the normal operations of retinal cells. The glutathione (GSH) antioxidant system plays an important role in mitigating ROS. To perform its protective functions, GSH depends on nicotinamide adenine dinucleotide phosphate (NADPH) produced through the pentose phosphate pathway. This work develops the first mathematical model for the GSH antioxidant system in the outer retina, capturing the most essential components for formation of ROS, GSH production, its oxidation in detoxifying ROS, and subsequent reduction by NADPH. We calibrate and validate the model using experimental measurements, at different postnatal days up to PN28, from control mice and from the rd1 mouse model for the disease retinitis pigmentosa (RP). Global sensitivity analysis is then applied to examine the model behavior and identify the pathways with the greatest impact in control compared to RP conditions. The findings underscore the importance of GSH and NADPH production in dealing with oxidative stress during retinal development, especially after peak rod degeneration occurs in RP, leading to increased oxygen tension. This suggests that stimulation of GSH and NADPH synthesis could be a potential intervention strategy in degenerative mouse retinas with RP.
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Affiliation(s)
- Atanaska Dobreva
- Department of Mathematics, Augusta University, Augusta, GA, 30912, USA.
| | - Erika Tatiana Camacho
- University of Texas at San Antonio, San Antonio, TX, 78249, USA
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85281, USA
| | - María Miranda
- Department of Biomedical Sciences, Faculty of Health Sciences, Institute of Biomedical Sciences, Cardenal Herrera-CEU University, CEU Universities, 46115, Valencia, Spain
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17
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de Vasconcelos ASV, de Lima JS, Cardoso RTN. Multiobjective optimization to assess dengue control costs using a climate-dependent epidemiological model. Sci Rep 2023; 13:10271. [PMID: 37355697 PMCID: PMC10290689 DOI: 10.1038/s41598-023-36903-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/12/2023] [Indexed: 06/26/2023] Open
Abstract
Arboviruses, diseases transmitted by arthropods, have become a significant challenge for public health managers. The World Health Organization highlights dengue as responsible for millions of infections worldwide annually. As there is no specific treatment for the disease and no free-of-charge vaccine for mass use in Brazil, the best option is the measures to combat the vector, the Aedes aegypti mosquito. Therefore, we proposed an epidemiological model dependent on temperature, precipitation, and humidity, considering symptomatic and asymptomatic dengue infections. Through computer simulations, we aimed to minimize the amount of insecticides and the social cost demanded to treat patients. We proposed a case study in which our model is fitted with real data from symptomatic dengue-infected humans in an epidemic year in a Brazilian city. Our multiobjective optimization model considers an additional control using larvicide, adulticide, and ultra-low volume spraying. The work's main contribution is studying the monetary cost of the actions to combat the vector demand versus the hospital cost per confirmed infected, comparing approaches with and without additional control. Results showed that the additional vector control measures are cheaper than the hospital treatment without the vector control would be.
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Affiliation(s)
- Amália Soares Vieira de Vasconcelos
- Postgraduate Program in Mathematical and Computational Modeling (PPGMMC), Federal Center for Technological Education-CEFET-MG, Av. Amazonas, 7675, Nova Gameleira, Belo Horizonte, Minas Gerais, 30510-000, Brazil.
| | - Josenildo Silva de Lima
- Postgraduate Program in Mathematical and Computational Modeling (PPGMMC), Federal Center for Technological Education-CEFET-MG, Av. Amazonas, 7675, Nova Gameleira, Belo Horizonte, Minas Gerais, 30510-000, Brazil
| | - Rodrigo Tomás Nogueira Cardoso
- Department of Mathematics, Federal Center for Technological Education-CEFET-MG, Av. Amazonas, 7675, Nova Gameleira, Belo Horizonte, Minas Gerais, 30510-000, Brazil
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18
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Benet LZ, Sodhi JK. The Uses and Advantages of Kirchhoff's Laws vs. Differential Equations in Pharmacology, Pharmacokinetics, and (Even) Chemistry. AAPS J 2023; 25:38. [PMID: 37038013 PMCID: PMC10832327 DOI: 10.1208/s12248-023-00801-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/10/2023] [Indexed: 04/12/2023] Open
Abstract
In chemistry, rate processes are defined in terms of rate constants, with units of time-1, and are derived by differential equations from amounts. In contrast, when considering drug concentrations in biological systems, particularly in humans, rate processes must be defined in terms of clearance, with units of volume/time, since biological volumes, which are highly dependent on drug partition into biological tissues, cannot be easily determined. In pharmacology, pharmacokinetics, and in making drug dosing decisions, drug clearance and changes in drug clearance are paramount. Clearance is defined as the amount of drug eliminated or moved divided by the exposure driving that elimination or movement. Historically, all clearance derivations in pharmacology and pharmacokinetics have been based on the use of differential equations in terms of rate constants and amounts, which are then converted into clearance equations when multiplied/divided by a hypothesized volume of distribution. Here, we show that except for iv bolus dosing, multiple volumes may be relevant. We have recently shown that clearance relationships, as well as rate constant relationships, may be derived independent of differential equations using Kirchhoff's Laws from physics. Kirchhoff's Laws may be simply translated to recognize that when two or more rate-defining processes operate in parallel, the total value of the overall reaction parameter is equal to the sum of those rate-defining processes. In contrast, when two or more rate-defining processes operate in series, the inverse of the total reaction parameter is equal to the sum of the inverse of those rate-defining steps.
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Affiliation(s)
- Leslie Z Benet
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, USA.
| | - Jasleen K Sodhi
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, USA
- Department of Drug Metabolism and Pharmacokinetics, Septerna, South San Francisco, California, USA
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19
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Qiao L, Ghosh P, Rangamani P. Design principles of improving the dose-response alignment in coupled GTPase switches. NPJ Syst Biol Appl 2023; 9:3. [PMID: 36720885 PMCID: PMC9889403 DOI: 10.1038/s41540-023-00266-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/17/2023] [Indexed: 02/02/2023] Open
Abstract
"Dose-response alignment" (DoRA), where the downstream response of cellular signaling pathways closely matches the fraction of activated receptor, can improve the fidelity of dose information transmission. The negative feedback has been experimentally identified as a key component for DoRA, but numerical simulations indicate that negative feedback is not sufficient to achieve perfect DoRA, i.e., perfect match of downstream response and receptor activation level. Thus a natural question is whether there exist design principles for signaling motifs within only negative feedback loops to improve DoRA to near-perfect DoRA. Here, we investigated several model formulations of an experimentally validated circuit that couples two molecular switches-mGTPase (monomeric GTPase) and tGTPase (heterotrimeric GTPases) - with negative feedback loops. In the absence of feedback, the low and intermediate mGTPase activation levels benefit DoRA in mass action and Hill-function models, respectively. Adding negative feedback has versatile roles on DoRA: it may impair DoRA in the mass action model with low mGTPase activation level and Hill-function model with intermediate mGTPase activation level; in other cases, i.e., the mass action model with a high mGTPase activation level or the Hill-function model with a non-intermediate mGTPase activation level, it improves DoRA. Furthermore, we found that DoRA in a longer cascade (i.e., tGTPase) can be obtained using Hill-function kinetics under certain conditions. In summary, we show how ranges of activity of mGTPase, reaction kinetics, the negative feedback, and the cascade length affect DoRA. This work provides a framework for improving the DoRA performance in signaling motifs with negative feedback.
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Affiliation(s)
- Lingxia Qiao
- Department of Mechanical and Aerospace Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Pradipta Ghosh
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA. .,Moores Comprehensive Cancer Center, University of California San Diego, La Jolla, CA, USA. .,Department of Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, USA.
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20
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Horn T, Gosliga A, Li C, Enculescu M, Legewie S. Position-dependent effects of RNA-binding proteins in the context of co-transcriptional splicing. NPJ Syst Biol Appl 2023; 9:1. [PMID: 36653378 PMCID: PMC9849329 DOI: 10.1038/s41540-022-00264-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/08/2022] [Indexed: 01/19/2023] Open
Abstract
Alternative splicing is an important step in eukaryotic mRNA pre-processing which increases the complexity of gene expression programs, but is frequently altered in disease. Previous work on the regulation of alternative splicing has demonstrated that splicing is controlled by RNA-binding proteins (RBPs) and by epigenetic DNA/histone modifications which affect splicing by changing the speed of polymerase-mediated pre-mRNA transcription. The interplay of these different layers of splicing regulation is poorly understood. In this paper, we derived mathematical models describing how splicing decisions in a three-exon gene are made by combinatorial spliceosome binding to splice sites during ongoing transcription. We additionally take into account the effect of a regulatory RBP and find that the RBP binding position within the sequence is a key determinant of how RNA polymerase velocity affects splicing. Based on these results, we explain paradoxical observations in the experimental literature and further derive rules explaining why the same RBP can act as inhibitor or activator of cassette exon inclusion depending on its binding position. Finally, we derive a stochastic description of co-transcriptional splicing regulation at the single-cell level and show that splicing outcomes show little noise and follow a binomial distribution despite complex regulation by a multitude of factors. Taken together, our simulations demonstrate the robustness of splicing outcomes and reveal that quantitative insights into kinetic competition of co-transcriptional events are required to fully understand this important mechanism of gene expression diversity.
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Affiliation(s)
- Timur Horn
- grid.424631.60000 0004 1794 1771Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany
| | - Alison Gosliga
- grid.424631.60000 0004 1794 1771Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany ,grid.5719.a0000 0004 1936 9713University of Stuttgart, Department of Systems Biology and Stuttgart Research Center Systems Biology (SRCSB), Allmandring 31, 70569 Stuttgart, Germany
| | - Congxin Li
- grid.5719.a0000 0004 1936 9713University of Stuttgart, Department of Systems Biology and Stuttgart Research Center Systems Biology (SRCSB), Allmandring 31, 70569 Stuttgart, Germany
| | - Mihaela Enculescu
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany.
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany. .,University of Stuttgart, Department of Systems Biology and Stuttgart Research Center Systems Biology (SRCSB), Allmandring 31, 70569, Stuttgart, Germany.
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21
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Nikolaou A, Hogea C, Samyshkin Y, Maiese EM, Sansbury L, Oguz M, Cid-Ruzafa J, Kapoor R, Wang F. An Epidemiology Model for Estimating the Numbers of US Patients With Multiple Myeloma by Line of Therapy and Treatment Exposure. Value Health 2022; 25:1977-1985. [PMID: 35963840 DOI: 10.1016/j.jval.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 03/09/2022] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Estimates on the distribution of patients with multiple myeloma (MM) by line of therapy (LOT) are scarce and get outdated quickly as new treatments become available. The objective of this study was to estimate the number of patients with MM by LOT and the number of patients who have received at least 4 previous LOTs including proteasome inhibitors, immunomodulatory agents, and anti-CD38 monoclonal antibodies (mAbs). METHODS A compartmental model was developed to calculate the number of patients by LOT. Two pathways were considered based on stem cell transplant eligibility, and at each pathway, treatments were stratified in 2 types: anti-CD38 mAbs or other. The model population was stratified into 4 subgroups based on age and cytogenetic risk. Model inputs were informed from real-world evidence. RESULTS The model estimated that, in 2020, 126 869 patients were living with MM in the United States. Of these, 105 701 received treatment in any LOT, with 56 959, 27 252, 11 258, and 5217 in lines 1 to 4, respectively, and 5015 in line 5 or beyond. The model estimated that 3497 patients received at least 4 previous LOTs including proteasome inhibitors, immunomodulatory agents, and anti-CD38 mAbs. The model overall prevalence predictions aligned well with publicly available estimates. CONCLUSIONS This study proposes a novel framework to estimate MM prevalence. It can assist clinicians to understand future trends in MM epidemiology, healthcare systems to plan for future resource use allocation, and payers to quantify the budget impact of new treatments.
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Affiliation(s)
| | - Cosmina Hogea
- Value Evidence and Outcomes, GlaxoSmithKline, Upper Providence, PA, USA
| | - Yevgeniy Samyshkin
- Value Evidence and Outcomes, GlaxoSmithKline, Brentford, Middlesex, England, UK
| | - Eric M Maiese
- Value Evidence and Outcomes, GlaxoSmithKline, Philadelphia, PA, USA
| | - Leah Sansbury
- Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, NC, USA
| | - Mustafa Oguz
- Real-World Evidence, Evidera, London, England, UK
| | | | - Ritika Kapoor
- Modelling and Simulation, Evidera, London, England, UK
| | - Feng Wang
- Value Evidence and Outcomes, GlaxoSmithKline, Upper Providence, PA, USA
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22
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Lefaudeux D, Sen S, Jiang K, Hoffmann A. Kinetics of mRNA nuclear export regulate innate immune response gene expression. Nat Commun 2022; 13:7197. [PMID: 36424375 PMCID: PMC9691726 DOI: 10.1038/s41467-022-34635-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 11/01/2022] [Indexed: 11/25/2022] Open
Abstract
The abundance and stimulus-responsiveness of mature mRNA is thought to be determined by nuclear synthesis, processing, and cytoplasmic decay. However, the rate and efficiency of moving mRNA to the cytoplasm almost certainly contributes, but has rarely been measured. Here, we investigated mRNA export rates for innate immune genes. We generated high spatio-temporal resolution RNA-seq data from endotoxin-stimulated macrophages and parameterized a mathematical model to infer kinetic parameters with confidence intervals. We find that the effective chromatin-to-cytoplasm export rate is gene-specific, varying 100-fold: for some genes, less than 5% of synthesized transcripts arrive in the cytoplasm as mature mRNAs, while others show high export efficiency. Interestingly, effective export rates do not determine temporal gene responsiveness, but complement the wide range of mRNA decay rates; this ensures similar abundances of short- and long-lived mRNAs, which form successive innate immune response expression waves.
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Affiliation(s)
- Diane Lefaudeux
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, 90095, USA
| | - Supriya Sen
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA
| | - Kevin Jiang
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, 90095, USA
| | - Alexander Hoffmann
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, 90095, USA.
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, 90095, USA.
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA.
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23
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Plöntzke J, Berg M, Ehrig R, Leonhard-Marek S, Müller KE, Röblitz S. Model-based exploration of hypokalemia in dairy cows. Sci Rep 2022; 12:19781. [PMID: 36396697 PMCID: PMC9672062 DOI: 10.1038/s41598-022-22596-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 10/17/2022] [Indexed: 11/19/2022] Open
Abstract
Hypokalemia in dairy cows, which is characterized by too low serum potassium levels, is a severe mineral disorder that can be life threatening. In this paper, we explore different originating conditions of hypokalemia-reduced potassium intake, increased excretion, acid-base disturbances, and increased insulin-by using a dynamic mathematical model for potassium balance in non-lactating and lactating cows. The simulations confirm observations described in literature. They illustrate, for example, that changes in dietary intake or excretion highly effect intracellular potassium levels, whereas extracellular levels vary only slightly. Simulations also show that the higher the potassium content in the diet, the more potassium is excreted with urine. Application of the mathematical model assists in experimental planning and therefore contributes to the 3R strategy: reduction, refinement and replacement of animal experiments.
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Affiliation(s)
- Julia Plöntzke
- grid.425649.80000 0001 1010 926XZuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany
| | - Mascha Berg
- grid.425649.80000 0001 1010 926XZuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany
| | - Rainald Ehrig
- grid.425649.80000 0001 1010 926XZuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany
| | - Sabine Leonhard-Marek
- grid.412970.90000 0001 0126 6191Library and Department of Physiology, University of Veterinary Medicine, 30559 Hannover, Germany
| | - Kerstin Elisabeth Müller
- grid.14095.390000 0000 9116 4836Clinic for Ruminants, Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany
| | - Susanna Röblitz
- grid.7914.b0000 0004 1936 7443Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5008 Bergen, Norway
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24
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Zlatinka I. Dimitrova. Flows of Substances in Networks and Network Channels: Selected Results and Applications. Entropy (Basel) 2022; 24:1485. [ DOI: 10.3390/e24101485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/13/2022] [Indexed: 06/18/2023]
Abstract
This review paper is devoted to a brief overview of results and models concerning flows in networks and channels of networks. First of all, we conduct a survey of the literature in several areas of research connected to these flows. Then, we mention certain basic mathematical models of flows in networks that are based on differential equations. We give special attention to several models for flows of substances in channels of networks. For stationary cases of these flows, we present probability distributions connected to the substance in the nodes of the channel for two basic models: the model of a channel with many arms modeled by differential equations and the model of a simple channel with flows of substances modeled by difference equations. The probability distributions obtained contain as specific cases any probability distribution of a discrete random variable that takes values of 0,1,…. We also mention applications of the considered models, such as applications for modeling migration flows. Special attention is given to the connection of the theory of stationary flows in channels of networks and the theory of the growth of random networks.
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25
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Maddu S, Cheeseman BL, Sbalzarini IF, Müller CL. Stability selection enables robust learning of differential equations from limited noisy data. Proc Math Phys Eng Sci 2022; 478:20210916. [PMID: 35756878 PMCID: PMC9199075 DOI: 10.1098/rspa.2021.0916] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 05/12/2022] [Indexed: 11/29/2022] Open
Abstract
We present a statistical learning framework for robust identification of differential equations from noisy spatio-temporal data. We address two issues that have so far limited the application of such methods, namely their robustness against noise and the need for manual parameter tuning, by proposing stability-based model selection to determine the level of regularization required for reproducible inference. This avoids manual parameter tuning and improves robustness against noise in the data. Our stability selection approach, termed PDE-STRIDE, can be combined with any sparsity-promoting regression method and provides an interpretable criterion for model component importance. We show that the particular combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast and robust framework for equation inference that outperforms previous approaches with respect to accuracy, amount of data required, and robustness. We illustrate the performance of PDE-STRIDE on a range of simulated benchmark problems, and we demonstrate the applicability of PDE-STRIDE on real-world data by considering purely data-driven inference of the protein interaction network for embryonic polarization in Caenorhabditis elegans. Using fluorescence microscopy images of C. elegans zygotes as input data, PDE-STRIDE is able to learn the molecular interactions of the proteins.
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Affiliation(s)
- Suryanarayana Maddu
- Faculty of Computer Science, Technische Universität Dresden, Dresden, Germany.,Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.,Center for Systems Biology Dresden, Dresden, Germany.,Cluster of Excellence Physics of Life, TU Dresden, Germany
| | - Bevan L Cheeseman
- Faculty of Computer Science, Technische Universität Dresden, Dresden, Germany.,Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.,Center for Systems Biology Dresden, Dresden, Germany.,Cluster of Excellence Physics of Life, TU Dresden, Germany
| | - Ivo F Sbalzarini
- Faculty of Computer Science, Technische Universität Dresden, Dresden, Germany.,Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.,Center for Systems Biology Dresden, Dresden, Germany.,Cluster of Excellence Physics of Life, TU Dresden, Germany
| | - Christian L Müller
- Center for Computational Mathematics, Flatiron Institute, New York, NY, USA
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26
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Abstract
Face masks do not completely prevent transmission of respiratory infections, but masked individuals are likely to inhale fewer infectious particles. If smaller infectious doses tend to yield milder infections, yet ultimately induce similar levels of immunity, then masking could reduce the prevalence of severe disease even if the total number of infections is unaffected. It has been suggested that this effect of masking is analogous to the pre-vaccination practice of variolation for smallpox, whereby susceptible individuals were intentionally infected with small doses of live virus (and often acquired immunity without severe disease). We present a simple epidemiological model in which mask-induced variolation causes milder infections, potentially with lower transmission rate and/or different duration. We derive relationships between the effectiveness of mask-induced variolation and important epidemiological metrics (the basic reproduction number and initial epidemic growth rate, and the peak prevalence, attack rate and equilibrium prevalence of severe infections). We illustrate our results using parameter estimates for the original SARS-CoV-2 wild-type virus, as well as the Alpha, Delta and Omicron variants. Our results suggest that if variolation is a genuine side-effect of masking, then the importance of face masks as a tool for reducing healthcare burdens from COVID-19 may be under-appreciated.
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Affiliation(s)
- Zachary Levine
- Department of Mathematics and Statistics, McMaster University, Hamilton, Canada L8S 4K1
| | - David J D Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, Canada L8S 4K1
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27
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Abstract
Starting from the general equations of fluid dynamics that describe the atmosphere, and using asymptotic methods, we present the derivation of the leading-order equations for nonlinear wave propagation in the troposphere. The only simplifying assumption is that the flow in the atmosphere exists in a thin shell over a sphere. The systematic approach adopted here enables us to find a consistent balance of terms describing the propagation, and to identify the temperature and pressure gradients that drive the motion, as well as the heat sources required. This produces a new nonlinear propagation equation that is then examined in some detail. With the morning glory in mind, we construct a few exact solutions, which, separately, describe breezes, bores and oscillatory motion.
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Affiliation(s)
- A Constantin
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
| | - R S Johnson
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle NE1 7RU, UK
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28
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Abstract
The leading-order equations governing the unsteady dynamics of large-scale
atmospheric motions are derived, via a systematic asymptotic
approach based on the thin-shell approximation applied to the ellipsoidal model
of the Earth’s geoid. We present some solutions of this single set of
equations that capture properties of specific atmospheric flows, using field
data to choose models for the heat sources that drive the motion. In particular,
we describe standing-waves solutions, waves propagating towards the Equator,
equatorially trapped waves and we discuss the African Easterly Jet/Waves. This
work aims to show the benefits of a systematic analysis based on the governing
equations of fluid dynamics.
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Affiliation(s)
- Adrian Constantin
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
| | - Robin S Johnson
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle NE1 7RU, UK
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29
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Alexa A, Sok P, Gross F, Albert K, Kobori E, Póti ÁL, Gógl G, Bento I, Kuang E, Taylor SS, Zhu F, Ciliberto A, Reményi A. A non-catalytic herpesviral protein reconfigures ERK-RSK signaling by targeting kinase docking systems in the host. Nat Commun 2022; 13:472. [PMID: 35078976 PMCID: PMC8789800 DOI: 10.1038/s41467-022-28109-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 01/07/2022] [Indexed: 12/16/2022] Open
Abstract
The Kaposi's sarcoma associated herpesvirus protein ORF45 binds the extracellular signal-regulated kinase (ERK) and the p90 Ribosomal S6 kinase (RSK). ORF45 was shown to be a kinase activator in cells but a kinase inhibitor in vitro, and its effects on the ERK-RSK complex are unknown. Here, we demonstrate that ORF45 binds ERK and RSK using optimized linear binding motifs. The crystal structure of the ORF45-ERK2 complex shows how kinase docking motifs recognize the activated form of ERK. The crystal structure of the ORF45-RSK2 complex reveals an AGC kinase docking system, for which we provide evidence that it is functional in the host. We find that ORF45 manipulates ERK-RSK signaling by favoring the formation of a complex, in which activated kinases are better protected from phosphatases and docking motif-independent RSK substrate phosphorylation is selectively up-regulated. As such, our data suggest that ORF45 interferes with the natural design of kinase docking systems in the host.
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Affiliation(s)
- Anita Alexa
- Biomolecular Interactions Research Group, Institute of Organic Chemistry, Research Center for Natural Sciences, H-1117, Budapest, Hungary
| | - Péter Sok
- Biomolecular Interactions Research Group, Institute of Organic Chemistry, Research Center for Natural Sciences, H-1117, Budapest, Hungary
| | - Fridolin Gross
- IFOM, Istituto FIRC di Oncologia Molecolare, 20139, Milan, Italy
| | - Krisztián Albert
- Biomolecular Interactions Research Group, Institute of Organic Chemistry, Research Center for Natural Sciences, H-1117, Budapest, Hungary
| | - Evan Kobori
- Department of Chemistry and Biochemistry, University of California San Diego, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0654, USA
| | - Ádám L Póti
- Biomolecular Interactions Research Group, Institute of Organic Chemistry, Research Center for Natural Sciences, H-1117, Budapest, Hungary
| | - Gergő Gógl
- Biomolecular Interactions Research Group, Institute of Organic Chemistry, Research Center for Natural Sciences, H-1117, Budapest, Hungary
| | - Isabel Bento
- European Molecular Biology Laboratory, Hamburg, Germany
| | - Ersheng Kuang
- Department of Biological Science, Florida State University, Tallahassee, FL, 32306-4370, USA
| | - Susan S Taylor
- Department of Pharmacology, University of California San Diego, 9500 Gilman Drive, La Jolla, San Diego, CA, 92093-0654, USA
| | - Fanxiu Zhu
- Department of Biological Science, Florida State University, Tallahassee, FL, 32306-4370, USA
| | - Andrea Ciliberto
- IFOM, Istituto FIRC di Oncologia Molecolare, 20139, Milan, Italy
| | - Attila Reményi
- Biomolecular Interactions Research Group, Institute of Organic Chemistry, Research Center for Natural Sciences, H-1117, Budapest, Hungary.
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30
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Stapor P, Schmiester L, Wierling C, Merkt S, Pathirana D, Lange BMH, Weindl D, Hasenauer J. Mini-batch optimization enables training of ODE models on large-scale datasets. Nat Commun 2022; 13:34. [PMID: 35013141 PMCID: PMC8748893 DOI: 10.1038/s41467-021-27374-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/11/2021] [Indexed: 11/09/2022] Open
Abstract
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.
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Affiliation(s)
- Paul Stapor
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | - Leonard Schmiester
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | | | - Simon Merkt
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | - Dilan Pathirana
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | | | - Daniel Weindl
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany.
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany.
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany.
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31
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Gleeson JP, Brendan Murphy T, O’Brien JD, Friel N, Bargary N, O'Sullivan DJP. Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates. Philos Trans A Math Phys Eng Sci 2022; 380:20210120. [PMID: 34802273 PMCID: PMC8607149 DOI: 10.1098/rsta.2021.0120] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Affiliation(s)
- James P. Gleeson
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
- Insight Centre for Data Analytics, Ireland
- Confirm Centre for Smart Manufacturing, Ireland
- Irish Epidemiological Modelling Advisory Group (IEMAG), Ireland
| | - Thomas Brendan Murphy
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland
- Insight Centre for Data Analytics, Ireland
- Irish Epidemiological Modelling Advisory Group (IEMAG), Ireland
| | - Joseph D. O’Brien
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
| | - Nial Friel
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland
- Insight Centre for Data Analytics, Ireland
| | - Norma Bargary
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
- Insight Centre for Data Analytics, Ireland
- Confirm Centre for Smart Manufacturing, Ireland
| | - David J. P. O'Sullivan
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
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32
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Demirkaya A, Imbiriba T, Lockwood K, Rampersad S, Alhajjar E, Guidoboni G, Danziger Z, Erdogmus D. Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:763-766. [PMID: 34891402 PMCID: PMC9901159 DOI: 10.1109/embc46164.2021.9631038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Modeling biological dynamical systems is challenging due to the interdependence of different system components, some of which are not fully understood. To fill existing gaps in our ability to mechanistically model physiological systems, we propose to combine neural networks with physics-based models. Specifically, we demonstrate how we can approximate missing ordinary differential equations (ODEs) coupled with known ODEs using Bayesian filtering techniques to train the model parameters and simultaneously estimate dynamic state variables. As a study case we leverage a well-understood model for blood circulation in the human retina and replace one of its core ODEs with a neural network approximation, representing the case where we have incomplete knowledge of the physiological state dynamics. Results demonstrate that state dynamics corresponding to the missing ODEs can be approximated well using a neural network trained using a recursive Bayesian filtering approach in a fashion coupled with the known state dynamic differential equations. This demonstrates that dynamics and impact of missing state variables can be captured through joint state estimation and model parameter estimation within a recursive Bayesian state estimation (RBSE) framework. Results also indicate that this RBSE approach to training the NN parameters yields better outcomes (measurement/state estimation accuracy) than training the neural network with backpropagation through time in the same setting.
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33
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Khan MF, Sulaiman M, Tavera Romero CA, Alkhathlan A. Falkner-Skan Flow with Stream-Wise Pressure Gradient and Transfer of Mass over a Dynamic Wall. Entropy (Basel) 2021; 23:e23111448. [PMID: 34828146 PMCID: PMC8620037 DOI: 10.3390/e23111448] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/20/2021] [Accepted: 10/22/2021] [Indexed: 11/16/2022]
Abstract
In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner-Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.
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Affiliation(s)
- Muhammad Fawad Khan
- Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan;
| | - Muhammad Sulaiman
- Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan;
- Correspondence:
| | | | - Ali Alkhathlan
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
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34
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Dawson DA, Sid-Ali A, Zhao YQ. Local Stability of McKean-Vlasov Equations Arising from Heterogeneous Gibbs Systems Using Limit of Relative Entropies. Entropy (Basel) 2021; 23:e23111407. [PMID: 34828105 PMCID: PMC8620427 DOI: 10.3390/e23111407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022]
Abstract
A family of heterogeneous mean-field systems with jumps is analyzed. These systems are constructed as a Gibbs measure on block graphs. When the total number of particles goes to infinity, the law of large numbers is shown to hold in a multi-class context, resulting in the weak convergence of the empirical vector towards the solution of a McKean–Vlasov system of equations. We then investigate the local stability of the limiting McKean–Vlasov system through the construction of a local Lyapunov function. We first compute the limit of adequately scaled relative entropy functions associated with the explicit stationary distribution of the N-particles system. Using a Laplace principle for empirical vectors, we show that the limit takes an explicit form. Then we demonstrate that this limit satisfies a descent property, which, combined with some mild assumptions shows that it is indeed a local Lyapunov function.
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35
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Kosmidis K, Hütt MT. A minimal model for gene expression dynamics of bacterial type II toxin-antitoxin systems. Sci Rep 2021; 11:19516. [PMID: 34593858 DOI: 10.1038/s41598-021-98570-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/07/2021] [Indexed: 02/08/2023] Open
Abstract
Toxin-antitoxin (TA) modules are part of most bacteria's regulatory machinery for stress responses and general aspects of their physiology. Due to the interplay of a long-lived toxin with a short-lived antitoxin, TA modules have also become systems of interest for mathematical modelling. Here we resort to previous modelling efforts and extract from these a minimal model of type II TA system dynamics on a timescale of hours, which can be used to describe time courses derived from gene expression data of TA pairs. We show that this model provides a good quantitative description of TA dynamics for the 11 TA pairs under investigation here, while simpler models do not. Our study brings together aspects of Biophysics with its focus on mathematical modelling and Computational Systems Biology with its focus on the quantitative interpretation of 'omics' data. This mechanistic model serves as a generic transformation of time course information into kinetic parameters. The resulting parameter vector can, in turn, be mechanistically interpreted. We expect that TA pairs with similar mechanisms are characterized by similar vectors of kinetic parameters, allowing us to hypothesize on the mode of action for TA pairs still under discussion.
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36
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Krantz M, Zimmer D, Adler SO, Kitashova A, Klipp E, Mühlhaus T, Nägele T. Data Management and Modeling in Plant Biology. Front Plant Sci 2021; 12:717958. [PMID: 34539712 PMCID: PMC8446634 DOI: 10.3389/fpls.2021.717958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/29/2021] [Indexed: 05/25/2023]
Abstract
The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.
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Affiliation(s)
- Maria Krantz
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Zimmer
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Stephan O. Adler
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anastasia Kitashova
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
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37
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Bergman DR, Karikomi MK, Yu M, Nie Q, MacLean AL. Modeling the effects of EMT-immune dynamics on carcinoma disease progression. Commun Biol 2021; 4:983. [PMID: 34408236 PMCID: PMC8373868 DOI: 10.1038/s42003-021-02499-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 07/27/2021] [Indexed: 02/07/2023] Open
Abstract
During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through simulation of patient cohorts in silico, the model predicts that a controllable region maximizes invasion-free survival. This controllable region depends on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. In light of the model predictions, we analyze EMT-inflammation-associated data from The Cancer Genome Atlas, and find that association with EMT worsens invasion-free survival probabilities. This result supports the predictions of the model, and leads to the identification of genes that influence outcomes in bladder and uterine cancer, including FGF pathway members. These results suggest new means to delay disease progression, and demonstrate the importance of studying cancer-immune interactions in light of EMT.
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Affiliation(s)
- Daniel R. Bergman
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA
| | - Matthew K. Karikomi
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA
| | - Min Yu
- grid.42505.360000 0001 2156 6853USC Norris Comprehensive Cancer Center, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA ,grid.42505.360000 0001 2156 6853Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Qing Nie
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA ,grid.266093.80000 0001 0668 7243Department of Cell and Developmental Biology, University of California, Irvine, CA USA
| | - Adam L. MacLean
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA ,grid.42505.360000 0001 2156 6853USC Norris Comprehensive Cancer Center, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA ,grid.42505.360000 0001 2156 6853Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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38
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Abstract
Mathematical models of cardiac ion channels have been widely used to study and predict the behaviour of ion currents. Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov state transitions. These models provide an abstract description of the underlying conformational changes of the ion channels. However, due to the abstracted conformation states and assumptions for the rates of transition between them, there are differences between the models and reality-termed model discrepancy or misspecification. In this paper, we demonstrate the feasibility of using a mechanistically-inspired neural network differential equation model, a hybrid non-parametric model, to model ion channel kinetics. We apply it to the hERG potassium ion channel as an example, with the aim of providing an alternative modelling approach that could alleviate certain limitations of the traditional approach. We compare and discuss multiple ways of using a neural network to approximate extra hidden states or alternative transition rates. In particular we assess their ability to learn the missing dynamics, and ask whether we can use these models to handle model discrepancy. Finally, we discuss the practicality and limitations of using neural networks and their potential applications.
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Affiliation(s)
- Chon Lok Lei
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau, China
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham, Ningbo, China
| | - Gary R. Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
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Hernansaiz-Ballesteros RD, Földi C, Cardelli L, Nagy LG, Csikász-Nagy A. Evolution of opposing regulatory interactions underlies the emergence of eukaryotic cell cycle checkpoints. Sci Rep 2021; 11:11122. [PMID: 34045495 PMCID: PMC8159995 DOI: 10.1038/s41598-021-90384-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 05/11/2021] [Indexed: 02/04/2023] Open
Abstract
In eukaryotes the entry into mitosis is initiated by activation of cyclin-dependent kinases (CDKs), which in turn activate a large number of protein kinases to induce all mitotic processes. The general view is that kinases are active in mitosis and phosphatases turn them off in interphase. Kinases activate each other by cross- and self-phosphorylation, while phosphatases remove these phosphate groups to inactivate kinases. Crucial exceptions to this general rule are the interphase kinase Wee1 and the mitotic phosphatase Cdc25. Together they directly control CDK in an opposite way of the general rule of mitotic phosphorylation and interphase dephosphorylation. Here we investigate why this opposite system emerged and got fixed in almost all eukaryotes. Our results show that this reversed action of a kinase-phosphatase pair, Wee1 and Cdc25, on CDK is particularly suited to establish a stable G2 phase and to add checkpoints to the cell cycle. We show that all these regulators appeared together in LECA (Last Eukaryote Common Ancestor) and co-evolved in eukaryotes, suggesting that this twist in kinase-phosphatase regulation was a crucial step happening at the emergence of eukaryotes.
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Affiliation(s)
- Rosa D. Hernansaiz-Ballesteros
- grid.13097.3c0000 0001 2322 6764Randall Centre for Cell and Molecular Biophysics, King’s College London, London, SE1 1UL UK ,grid.7700.00000 0001 2190 4373Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, Heidelberg University, 69120 Heidelberg, Germany
| | - Csenge Földi
- grid.418331.c0000 0001 2195 9606Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Szeged, 6726 Hungary
| | - Luca Cardelli
- grid.4991.50000 0004 1936 8948Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD UK
| | - László G. Nagy
- grid.418331.c0000 0001 2195 9606Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Szeged, 6726 Hungary
| | - Attila Csikász-Nagy
- grid.13097.3c0000 0001 2322 6764Randall Centre for Cell and Molecular Biophysics, King’s College London, London, SE1 1UL UK ,grid.425397.e0000 0001 0807 2090Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest, 1083 Hungary
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40
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Pavlinova P, Samsonova MG, Gursky VV. Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering in Pisum sativum. Front Genet 2021; 12:614711. [PMID: 33777095 PMCID: PMC7990781 DOI: 10.3389/fgene.2021.614711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/28/2021] [Indexed: 11/29/2022] Open
Abstract
Transition to flowering is an important stage of plant development. Many regulatory modules that control floral transition are conservative across plants. This process is best studied for the model plant Arabidopsis thaliana. The homologues of Arabidopsis genes responsible for the flowering initiation in legumes have been identified, and available data on their expression provide a good basis for gene network modeling. In this study, we developed several dynamical models of a gene network controlling transition to flowering in pea (Pisum sativum) using two different approaches. We used differential equations for modeling a previously proposed gene regulation scheme of floral initiation in pea and tested possible alternative hypothesis about some regulations. As the second approach, we applied neural networks to infer interactions between genes in the network directly from gene expression data. All models were verified on previously published experimental data on the dynamic expression of the main genes in the wild type and in three mutant genotypes. Based on modeling results, we made conclusions about the functionality of the previously proposed interactions in the gene network and about the influence of different growing conditions on the network architecture. It was shown that regulation of the PIM, FTa1, and FTc genes in pea does not correspond to the previously proposed hypotheses. The modeling suggests that short- and long-day growing conditions are characterized by different gene network architectures. Overall, the results obtained can be used to plan new experiments and create more accurate models to study the flowering initiation in pea and, in a broader context, in legumes.
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Affiliation(s)
- Polina Pavlinova
- Mathematical Biology and Bioinformatics Laboratory, Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia
| | - Maria G Samsonova
- Mathematical Biology and Bioinformatics Laboratory, Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russia
| | - Vitaly V Gursky
- Theoretical Department, Ioffe Institute, Saint Petersburg, Russia
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41
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Nardini JT, Baker RE, Simpson MJ, Flores KB. Learning differential equation models from stochastic agent-based model simulations. J R Soc Interface 2021; 18:20200987. [PMID: 33726540 PMCID: PMC8086865 DOI: 10.1098/rsif.2020.0987] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/22/2021] [Indexed: 12/15/2022] Open
Abstract
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.
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Affiliation(s)
- John T. Nardini
- North Carolina State University, Mathematics, Raleigh, NC, USA
| | - Ruth E. Baker
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane 4001, Australia
| | - Kevin B. Flores
- North Carolina State University, Mathematics, Raleigh, NC, USA
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42
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Silva CJ, Cruz C, Torres DFM, Muñuzuri AP, Carballosa A, Area I, Nieto JJ, Fonseca-Pinto R, Passadouro R, Santos ESD, Abreu W, Mira J. Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal. Sci Rep 2021; 11:3451. [PMID: 33568716 PMCID: PMC7876047 DOI: 10.1038/s41598-021-83075-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/27/2021] [Indexed: 02/08/2023] Open
Abstract
The COVID-19 pandemic has forced policy makers to decree urgent confinements to stop a rapid and massive contagion. However, after that stage, societies are being forced to find an equilibrium between the need to reduce contagion rates and the need to reopen their economies. The experience hitherto lived has provided data on the evolution of the pandemic, in particular the population dynamics as a result of the public health measures enacted. This allows the formulation of forecasting mathematical models to anticipate the consequences of political decisions. Here we propose a model to do so and apply it to the case of Portugal. With a mathematical deterministic model, described by a system of ordinary differential equations, we fit the real evolution of COVID-19 in this country. After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then, we apply optimal control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations. This work allows testing various scenarios of pandemic management (closure of sectors of the economy, partial/total compliance with protection measures by citizens, number of beds in intensive care units, etc.), ensuring the responsiveness of the health system, thus being a public health decision support tool.
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Affiliation(s)
- Cristiana J Silva
- Department of Mathematics, Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3810-193, Aveiro, Portugal.
| | - Carla Cruz
- Department of Mathematics, Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3810-193, Aveiro, Portugal
| | - Delfim F M Torres
- Department of Mathematics, Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3810-193, Aveiro, Portugal
| | - Alberto P Muñuzuri
- Department of Physics, Institute CRETUS, Group of Nonlinear Physics, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - Alejandro Carballosa
- Department of Physics, Institute CRETUS, Group of Nonlinear Physics, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - Iván Area
- Departamento de Matemática Aplicada II, E. E. Aeronáutica e do Espazo, Campus de Ourense, Universidade de Vigo, 32004, Ourense, Spain
| | - Juan J Nieto
- Instituto de Matemáticas, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - Rui Fonseca-Pinto
- Center for Innovative Care and Health Technology (ciTechCare), Polytechnic of Leiria, Leiria, Portugal
| | - Rui Passadouro
- Center for Innovative Care and Health Technology (ciTechCare), Polytechnic of Leiria, Leiria, Portugal
- ACES Pinhal Litoral-ARS Centro, Leiria, Portugal
| | | | - Wilson Abreu
- School of Nursing and Research Centre "Centre for Health Technology and Services Research/ESEP-CINTESIS", Porto, Portugal
| | - Jorge Mira
- Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.
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43
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Städter P, Schälte Y, Schmiester L, Hasenauer J, Stapor PL. Benchmarking of numerical integration methods for ODE models of biological systems. Sci Rep 2021; 11:2696. [PMID: 33514831 PMCID: PMC7846608 DOI: 10.1038/s41598-021-82196-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/08/2021] [Indexed: 11/09/2022] Open
Abstract
Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.
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Affiliation(s)
- Philipp Städter
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
| | - Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany.
- Faculty of Mathematics and Natural Sciences, University of Bonn, 53113, Bonn, Germany.
| | - Paul L Stapor
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
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44
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Manitsaris S, Senteri G, Makrygiannis D, Glushkova A. Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories. Front Robot AI 2021; 7:80. [PMID: 33501247 PMCID: PMC7805970 DOI: 10.3389/frobt.2020.00080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 05/08/2020] [Indexed: 11/13/2022] Open
Abstract
Human-centered artificial intelligence is increasingly deployed in professional workplaces in Industry 4.0 to address various challenges related to the collaboration between the operators and the machines, the augmentation of their capabilities, or the improvement of the quality of their work and life in general. Intelligent systems and autonomous machines need to continuously recognize and follow the professional actions and gestures of the operators in order to collaborate with them and anticipate their trajectories for avoiding potential collisions and accidents. Nevertheless, the recognition of patterns of professional gestures is a very challenging task for both research and the industry. There are various types of human movements that the intelligent systems need to perceive, for example, gestural commands to machines and professional actions with or without the use of tools. Moreover, the interclass and intraclass spatiotemporal variances together with the very limited access to annotated human motion data constitute a major research challenge. In this paper, we introduce the Gesture Operational Model, which describes how gestures are performed based on assumptions that focus on the dynamic association of body entities, their synergies, and their serial and non-serial mediations, as well as their transitioning over time from one state to another. Then, the assumptions of the Gesture Operational Model are translated into a simultaneous equation system for each body entity through State-Space modeling. The coefficients of the equation are computed using the Maximum Likelihood Estimation method. The simulation of the model generates a confidence-bounding box for every entity that describes the tolerance of its spatial variance over time. The contribution of our approach is demonstrated for both recognizing gestures and forecasting human motion trajectories. In recognition, it is combined with continuous Hidden Markov Models to boost the recognition accuracy when the likelihoods are not confident. In forecasting, a motion trajectory can be estimated by taking as minimum input two observations only. The performance of the algorithm has been evaluated using four industrial datasets that contain gestures and actions from a TV assembly line, the glassblowing industry, the gestural commands to Automated Guided Vehicles as well as the Human-Robot Collaboration in the automotive assembly lines. The hybrid approach State-Space and HMMs outperforms standard continuous HMMs and a 3DCNN-based end-to-end deep architecture.
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Affiliation(s)
| | - Gavriela Senteri
- Centre for Robotics, MINES ParisTech, PSL Université, Paris, France
| | | | - Alina Glushkova
- Centre for Robotics, MINES ParisTech, PSL Université, Paris, France
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45
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Akamatsu T, Nagae T, Osawa M, Satsukawa K, Sakai T, Mizutani D. Model-based analysis on social acceptability and feasibility of a focused protection strategy against the COVID-19 pandemic. Sci Rep 2021; 11:2003. [PMID: 33479450 PMCID: PMC7820463 DOI: 10.1038/s41598-021-81630-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 01/07/2021] [Indexed: 02/07/2023] Open
Abstract
This paper studies the social acceptability and feasibility of a focused protection strategy against coronavirus disease 2019 (COVID-19). We propose a control scheme to develop herd immunity while satisfying the following two basic requirements for a viable policy option. The first requirement is social acceptability: the overall deaths should be minimized for social acceptance. The second is feasibility: the healthcare system should not be overwhelmed to avoid various adverse effects. To exploit the fact that the disease severity increases considerably with age and comorbidities, we assume that some focused protection measures for those high-risk individuals are implemented and the disease does not spread within the high-risk population. Because the protected population has higher severity ratios than the unprotected population by definition, the protective measure can substantially reduce mortality in the whole population and also avoid the collapse of the healthcare system. Based on a simple susceptible-infected-recovered model, social acceptability and feasibility of the proposed strategy are summarized into two easily computable conditions. The proposed framework can be applied to various populations for studying the viability of herd immunity strategies against COVID-19. For Japan, herd immunity may be developed by the proposed scheme if [Formula: see text] and the severity rates of the disease are 1/10 times smaller than the previously reported value, although as high mortality as seasonal influenza is expected.
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Affiliation(s)
- Takashi Akamatsu
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, 980-8579, Japan.
| | - Takeshi Nagae
- Graduate School of Engineering, Tohoku University, Sendai, Miyagi, 980-8579, Japan.
| | - Minoru Osawa
- Institute of Economic Research, Kyoto University, Kyoto, 606-8501, Japan.
| | - Koki Satsukawa
- New Industry Creation Hatchery Center, Tohoku University, Sendai, Miyagi, 980-8579, Japan
| | - Takara Sakai
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, 980-8579, Japan
| | - Daijiro Mizutani
- International Research Institute of Disaster Science, Tohoku University, Sendai, Miyagi, 980-8572, Japan
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Lomeli LM, Iniguez A, Tata P, Jena N, Liu ZY, Van Etten R, Lander AD, Shahbaba B, Lowengrub JS, Minin VN. Optimal experimental design for mathematical models of haematopoiesis. J R Soc Interface 2021; 18:20200729. [PMID: 33499768 PMCID: PMC7879761 DOI: 10.1098/rsif.2020.0729] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.
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Affiliation(s)
- Luis Martinez Lomeli
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Abdon Iniguez
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Prasanthi Tata
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Nilamani Jena
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Zhong-Ying Liu
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Richard Van Etten
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
- Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
| | - Arthur D. Lander
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
| | - Babak Shahbaba
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Department of Statistics, University of California Irvine, Irvine, CA, USA
| | - John S. Lowengrub
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, USA
| | - Vladimir N. Minin
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Department of Statistics, University of California Irvine, Irvine, CA, USA
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47
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Carlson-Stevermer J, Das A, Abdeen AA, Fiflis D, Grindel BI, Saxena S, Akcan T, Alam T, Kletzien H, Kohlenberg L, Goedland M, Dombroe MJ, Saha K. Design of efficacious somatic cell genome editing strategies for recessive and polygenic diseases. Nat Commun 2020; 11:6277. [PMID: 33293555 PMCID: PMC7722885 DOI: 10.1038/s41467-020-20065-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 11/13/2020] [Indexed: 02/06/2023] Open
Abstract
Compound heterozygous recessive or polygenic diseases could be addressed through gene correction of multiple alleles. However, targeting of multiple alleles using genome editors could lead to mixed genotypes and adverse events that amplify during tissue morphogenesis. Here we demonstrate that Cas9-ribonucleoprotein-based genome editors can correct two distinct mutant alleles within a single human cell precisely. Gene-corrected cells in an induced pluripotent stem cell model of Pompe disease expressed the corrected transcript from both corrected alleles, leading to enzymatic cross-correction of diseased cells. Using a quantitative in silico model for the in vivo delivery of genome editors into the developing human infant liver, we identify progenitor targeting, delivery efficiencies, and suppression of imprecise editing outcomes at the on-target site as key design parameters that control the efficacy of various therapeutic strategies. This work establishes that precise gene editing to correct multiple distinct gene variants could be highly efficacious if designed appropriately.
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Affiliation(s)
- Jared Carlson-Stevermer
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Amritava Das
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Amr A Abdeen
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - David Fiflis
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Benjamin I Grindel
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Shivani Saxena
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Tugce Akcan
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Tausif Alam
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Heidi Kletzien
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Lucille Kohlenberg
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Madelyn Goedland
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Micah J Dombroe
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Krishanu Saha
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
- Retina Research Foundation Kathryn and Latimer Murfee Chair, Madison, WI, USA.
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48
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Sarmah DT, Bairagi N, Chatterjee S. Tracing the footsteps of autophagy in computational biology. Brief Bioinform 2020; 22:5985288. [PMID: 33201177 PMCID: PMC8293817 DOI: 10.1093/bib/bbaa286] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.
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Affiliation(s)
| | - Nandadulal Bairagi
- Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata, India
| | - Samrat Chatterjee
- Translational Health Science and Technology Institute, Faridabad, India
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49
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N Kolodkin A, Sharma RP, Colangelo AM, Ignatenko A, Martorana F, Jennen D, Briedé JJ, Brady N, Barberis M, Mondeel TDGA, Papa M, Kumar V, Peters B, Skupin A, Alberghina L, Balling R, Westerhoff HV. ROS networks: designs, aging, Parkinson's disease and precision therapies. NPJ Syst Biol Appl 2020; 6:34. [PMID: 33106503 PMCID: PMC7589522 DOI: 10.1038/s41540-020-00150-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.
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Affiliation(s)
- Alexey N Kolodkin
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Raju Prasad Sharma
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
| | - Anna Maria Colangelo
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Andrew Ignatenko
- Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | - Francesca Martorana
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Jacco J Briedé
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Nathan Brady
- Department of Molecular Microbiology & Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Thierry D G A Mondeel
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Michele Papa
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Infrastructure for Systems Biology Europe (ISBE.IT), Naples, Italy
- Department of Mental and Physical Health, University of Campania-L. Vanvitelli, Napoli, Italia
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
- IISPV, Hospital Universitari Sant Joan de Reus, Universitat Rovira I Virgili, Reus, Spain
| | - Bernhard Peters
- Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lilia Alberghina
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Hans V Westerhoff
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
- Manchester Centre for Integrative Systems Biology, School for Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK.
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50
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Bhaduri R, Kundu R, Purkayastha S, Kleinsasser M, Beesley LJ, Mukherjee B. EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy. medRxiv 2020:2020.09.24.20200238. [PMID: 32995829 PMCID: PMC7523173 DOI: 10.1101/2020.09.24.20200238] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number under such data anomalies using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.
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Affiliation(s)
| | | | | | | | | | - Bhramar Mukherjee
- Department of Biostatistics and Epidemiology, University of Michigan, Ann Arbor, USA
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