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Juretić D, Bonačić Lošić Ž. Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation. ENTROPY (BASEL, SWITZERLAND) 2024; 26:151. [PMID: 38392406 PMCID: PMC10888251 DOI: 10.3390/e26020151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/18/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024]
Abstract
Previous studies have revealed the extraordinarily large catalytic efficiency of some enzymes. High catalytic proficiency is an essential accomplishment of biological evolution. Natural selection led to the increased turnover number, kcat, and enzyme efficiency, kcat/KM, of uni-uni enzymes, which convert a single substrate into a single product. We added or multiplied random noise with chosen rate constants to explore the correlation between dissipation and catalytic efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. Our results highlight the role of biological evolution in accelerating thermodynamic evolution. The catalytic performance of these enzymes is proportional to overall entropy production-the main parameter from irreversible thermodynamics. That parameter is also proportional to the evolutionary distance of β-lactamases PC1, RTEM, and Lac-1 when natural or artificial evolution produces the optimal or maximal possible catalytic efficiency. De novo enzyme design and attempts to speed up the rate-limiting catalytic steps may profit from the described connection between kinetics and thermodynamics.
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Affiliation(s)
- Davor Juretić
- Mediterranean Institute for Life Sciences, Šetalište Ivana Meštrovića 45, 21000 Split, Croatia
- Faculty of Science, University of Split, Ruđera Boškovića 33, 21000 Split, Croatia
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2
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Ramezani A, Britton S, Zandi R, Alber M, Nematbakhsh A, Chen W. A multiscale chemical-mechanical model predicts impact of morphogen spreading on tissue growth. NPJ Syst Biol Appl 2023; 9:16. [PMID: 37210381 DOI: 10.1038/s41540-023-00278-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/03/2023] [Indexed: 05/22/2023] Open
Abstract
The exact mechanism controlling cell growth remains a grand challenge in developmental biology and regenerative medicine. The Drosophila wing disc tissue serves as an ideal biological model to study mechanisms involved in growth regulation. Most existing computational models for studying tissue growth focus specifically on either chemical signals or mechanical forces. Here we developed a multiscale chemical-mechanical model to investigate the growth regulation mechanism based on the dynamics of a morphogen gradient. By comparing the spatial distribution of dividing cells and the overall tissue shape obtained in model simulations with experimental data of the wing disc, it is shown that the size of the domain of the Dpp morphogen is critical in determining tissue size and shape. A larger tissue size with a faster growth rate and more symmetric shape can be achieved if the Dpp gradient spreads in a larger domain. Together with Dpp absorbance at the peripheral zone, the feedback regulation that downregulates Dpp receptors on the cell membrane allows for further spreading of the morphogen away from its source region, resulting in prolonged tissue growth at a more spatially homogeneous growth rate.
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Affiliation(s)
- Alireza Ramezani
- Department of Physics and Astronomy, University of California, Riverside, CA, 92521, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, 92521, USA
| | - Samuel Britton
- Department of Mathematics, University of California, Riverside, CA, 92521, USA
| | - Roya Zandi
- Department of Physics and Astronomy, University of California, Riverside, CA, 92521, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, 92521, USA
| | - Mark Alber
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, 92521, USA
- Department of Mathematics, University of California, Riverside, CA, 92521, USA
| | - Ali Nematbakhsh
- Department of Mathematics, University of California, Riverside, CA, 92521, USA.
| | - Weitao Chen
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, 92521, USA.
- Department of Mathematics, University of California, Riverside, CA, 92521, USA.
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3
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Schuurman AR, Sloot PMA, Wiersinga WJ, van der Poll T. Embracing complexity in sepsis. Crit Care 2023; 27:102. [PMID: 36906606 PMCID: PMC10007743 DOI: 10.1186/s13054-023-04374-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/19/2023] [Indexed: 03/13/2023] Open
Abstract
Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.
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Affiliation(s)
- Alex R Schuurman
- Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Peter M A Sloot
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - W Joost Wiersinga
- Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.,Division of Infectious Diseases, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands
| | - Tom van der Poll
- Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. .,Division of Infectious Diseases, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.
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4
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Uleman JF, Mancini E, Al-Shama RF, te Velde AA, Kraneveld AD, Castiglione F. A multiscale hybrid model for exploring the effect of Resolvin D1 on macrophage polarization during acute inflammation. Math Biosci 2023; 359:108997. [PMID: 36996999 DOI: 10.1016/j.mbs.2023.108997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Dysregulated inflammation underlies various diseases. Specialized pro-resolving mediators (SPMs) like Resolvin D1 (RvD1) have been shown to resolve inflammation and halt disease progression. Macrophages, key immune cells that drive inflammation, respond to the presence of RvD1 by polarizing to an anti-inflammatory type (M2). However, RvD1's mechanisms, roles, and utility are not fully understood. This paper introduces a gene-regulatory network (GRN) model that contains pathways for RvD1 and other SPMs and proinflammatory molecules like lipopolysaccharides. We couple this GRN model to a partial differential equation - agent-based hybrid model using a multiscale framework to simulate an acute inflammatory response with and without the presence of RvD1. We calibrate and validate the model using experimental data from two animal models. The model reproduces the dynamics of key immune components and the effects of RvD1 during acute inflammation. Our results suggest RvD1 can drive macrophage polarization through the G protein-coupled receptor 32 (GRP32) pathway. The presence of RvD1 leads to an earlier and increased M2 polarization, reduced neutrophil recruitment, and faster apoptotic neutrophil clearance. These results support a body of literature that suggests that RvD1 is a promising candidate for promoting the resolution of acute inflammation. We conclude that once calibrated and validated on human data, the model can identify critical sources of uncertainty, which could be further elucidated in biological experiments and assessed for clinical use.
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Androulakis IP. Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach? FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:1044281. [PMID: 36866242 PMCID: PMC9977321 DOI: 10.3389/fsysb.2022.1044281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.
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Affiliation(s)
- Ioannis P Androulakis
- Biomedical Engineering Department, New Brunswick, NJ, United States.,Chemical and Biochemical Engineering Department, Rutgers University, New Brunswick, NJ, United States
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6
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Dobreva A, Brady-Nicholls R, Larripa K, Puelz C, Mehlsen J, Olufsen MS. A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during an endotoxin challenge. J Physiol 2021; 599:1459-1485. [PMID: 33450068 DOI: 10.1113/jp280883] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 01/11/2021] [Indexed: 12/12/2022] Open
Abstract
KEY POINTS Inflammation in response to bacterial endotoxin challenge impacts physiological functions, including cardiovascular, thermal and pain dynamics, although the mechanisms are poorly understood. We develop an innovative mathematical model incorporating interaction pathways between inflammation and physiological processes observed in response to an endotoxin challenge. We calibrate the model to individual data from 20 subjects in an experimental study of the human inflammatory and physiological responses to endotoxin, and we validate the model against human data from an independent study. Using the model to simulate patient responses to different treatment modalities reveals that a multimodal treatment combining several therapeutic strategies gives the best recovery outcome. ABSTRACT Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory conditions such as sepsis, one of the ten leading causes of death in the USA, and one of the three leading causes of death in the intensive care unit. Understanding how inflammation affects physiological processes, including cardiovascular, thermal and pain dynamics, can improve a patient's chance of recovery after an inflammatory event caused by surgery or a severe infection. Although the effects of the autonomic response on the inflammatory system are well-known, knowledge about the reverse interaction is lacking. The present study develops a mathematical model analyzing the inflammatory system's interactions with thermal, pain and cardiovascular dynamics in response to a bacterial endotoxin challenge. We calibrate the model with individual data from an experimental study of the inflammatory and physiological responses to a one-time administration of endotoxin in 20 healthy young men and validate it against data from an independent endotoxin study. We use simulation to explore how various treatments help patients exposed to a sustained pathological input. The treatments explored include bacterial endotoxin adsorption, antipyretics and vasopressors, as well as combinations of these. Our findings suggest that the most favourable recovery outcome is achieved by a multimodal strategy, combining all three interventions to simultaneously remove endotoxin from the body and alleviate symptoms caused by the immune system as it fights the infection.
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Affiliation(s)
- Atanaska Dobreva
- Department of Mathematics, North Carolina State University, Raleigh, NC, USA.,School of Mathematical and Natural Sciences, Arizona State University, Glendale, AZ, USA
| | - Renee Brady-Nicholls
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kamila Larripa
- Department of Mathematics, Humboldt State University, Arcata, CA, USA
| | - Charles Puelz
- Department of Pediatrics, Section of Cardiology, Texas Children's Hospital and Baylor College of Medicine, Houston, TX, USA
| | - Jesper Mehlsen
- Section for Surgical Pathophysiology, Rigshospitalet, Copenhagen, Denmark
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, NC, USA
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7
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Sego TJ, Aponte-Serrano JO, Ferrari Gianlupi J, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness. PLoS Comput Biol 2020; 16:e1008451. [PMID: 33347439 PMCID: PMC7785254 DOI: 10.1371/journal.pcbi.1008451] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/05/2021] [Accepted: 10/20/2020] [Indexed: 12/23/2022] Open
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Josua O. Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Samuel R. Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität, Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - James M. Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Ellen M. Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Richard K. Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, United States of America
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
- Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
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8
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Sego TJ, Aponte-Serrano JO, Gianlupi JF, Heaps SR, Breithaupt K, Brusch L, Crawshaw J, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness: A multiscale model of viral infection in epithelial tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.04.27.064139. [PMID: 32511367 PMCID: PMC7263495 DOI: 10.1101/2020.04.27.064139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
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Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Josua O Aponte-Serrano
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Juliano Ferrari Gianlupi
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Samuel R Heaps
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Kira Breithaupt
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Lutz Brusch
- Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Germany
| | - Jessica Crawshaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Richard K Plemper
- Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
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9
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Ayyar VS, Jusko WJ. Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids. Pharmacol Rev 2020; 72:414-438. [PMID: 32123034 DOI: 10.1124/pr.119.018101] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.
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Affiliation(s)
- Vivaswath S Ayyar
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
| | - William J Jusko
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
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10
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Ewald J, Sieber P, Garde R, Lang SN, Schuster S, Ibrahim B. Trends in mathematical modeling of host-pathogen interactions. Cell Mol Life Sci 2020; 77:467-480. [PMID: 31776589 PMCID: PMC7010650 DOI: 10.1007/s00018-019-03382-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/05/2019] [Accepted: 11/12/2019] [Indexed: 12/18/2022]
Abstract
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
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Affiliation(s)
- Jan Ewald
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Patricia Sieber
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Ravindra Garde
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
- Max Planck Institute for Chemical Ecology, Hans-Knöll-Str. 8, 07745, Jena, Germany
| | - Stefan N Lang
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Stefan Schuster
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
| | - Bashar Ibrahim
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
- Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, 32093, Hawally, Kuwait.
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11
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Ramirez-Zuniga I, Rubin JE, Swigon D, Clermont G. Mathematical modeling of energy consumption in the acute inflammatory response. J Theor Biol 2019; 460:101-114. [PMID: 30149010 PMCID: PMC6690200 DOI: 10.1016/j.jtbi.2018.08.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/20/2018] [Accepted: 08/22/2018] [Indexed: 01/20/2023]
Abstract
When a pathogen invades the body, an acute inflammatory response is activated to eliminate the intruder. In some patients, runaway activation of the immune system may lead to collateral tissue damage and, in the extreme, organ failure and death. Experimental studies have found an association between severe infections and depletion in levels of adenosine triphosphate (ATP), increase in nitric oxide production, and accumulation of lactate, suggesting that tissue energetics is compromised. In this work we present a differential equations model that incorporates the dynamics of ATP, nitric oxide, and lactate accompanying an acute inflammatory response and employ this model to explore their roles in shaping this response. The bifurcation diagram of the model system with respect to the pathogen growth rate reveals three equilibrium states characterizing the health, aseptic and septic conditions. We explore the domains of attraction of these states to inform the instantiation of heterogeneous virtual patient populations utilized in a survival analysis. We then apply the model to study alterations in the inflammatory response and survival outcomes in metabolically altered conditions such as hypoglycemia, hyperglycemia, and hypoxia.
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Affiliation(s)
- Ivan Ramirez-Zuniga
- Department of Mathematics, 301 Thackeray Hall, University of Pittsburgh, Pittsburgh, PA 15260, United States.
| | - Jonathan E Rubin
- Department of Mathematics, 301 Thackeray Hall, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - David Swigon
- Department of Mathematics, 301 Thackeray Hall, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Gilles Clermont
- Department of Mathematics, 301 Thackeray Hall, University of Pittsburgh, Pittsburgh, PA 15260, United States; Department of Critical Care Medicine, 3550 Terrace St., University of Pittsburgh Medical Center, Pittsburgh, PA 15261, United States; Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, PA 15260, United States
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12
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Norton KA, Gong C, Jamalian S, Popel AS. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes (Basel) 2019; 7:37. [PMID: 30701168 PMCID: PMC6349239 DOI: 10.3390/pr7010037] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.
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Affiliation(s)
- Kerri-Ann Norton
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Computer Science Program, Department of Science, Mathematics, and Computing, Bard College, Annandale-on-Hudson, NY 12504, USA
| | - Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Samira Jamalian
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
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13
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A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice. PLoS Comput Biol 2018; 14:e1006582. [PMID: 30399158 PMCID: PMC6239343 DOI: 10.1371/journal.pcbi.1006582] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 11/16/2018] [Accepted: 10/15/2018] [Indexed: 12/13/2022] Open
Abstract
Bacterial lipopolysaccharide (LPS) induces an acute inflammatory response across multiple organs, primarily via Toll-like receptor 4 (TLR4). We sought to define novel aspects of the complex spatiotemporal dynamics of LPS-induced inflammation using computational modeling, with a special focus on the timing of pathological systemic spillover. An analysis of principal drivers of LPS-induced inflammation in the heart, gut, lung, liver, spleen, and kidney to assess organ-specific dynamics, as well as in the plasma (as an assessment of systemic spillover), was carried out using data on 20 protein-level inflammatory mediators measured over 0-48h in both C57BL/6 and TLR4-null mice. Using a suite of computational techniques, including a time-interval variant of Principal Component Analysis, we confirm key roles for cytokines such as tumor necrosis factor-α and interleukin-17A, define a temporal hierarchy of organ-localized inflammation, and infer the point at which organ-localized inflammation spills over systemically. Thus, by employing a systems biology approach, we obtain a novel perspective on the time- and organ-specific components in the propagation of acute systemic inflammation. Gram-negative bacterial lipopolysaccharide (LPS) is both a central mediator of sepsis and a canonical inducer of acute inflammation via Toll-like receptor 4 (TLR4). Sepsis involves the systemic spillover of inflammation that normally remains localized in individual organs. The goal of this study was to gain insights into 1) early vs. later drivers of LPS-induced inflammation in various compartments, and 2) the systemic spillover from affected organs vs. local production of inflammatory mediators in the blood. This study involved a large number of data points on the dynamics of inflammatory mediators at the protein level, data-driven computational modeling of principal characteristics and cross-correlations, and validation of key hypotheses. In addition to verifying key mechanisms in LPS/TLR4-driven acute inflammation, this approach yielded key insights into the progression of inflammation across tissues, and also suggested the presence of TLR4-independent pathways (especially in the gut). This is, to our knowledge, the first study examining the dynamic evolution of some key inflammatory mediators and their interactions with each other in both the systemic circulation and within a number of targeted parenchymal organs in mice.
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14
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Aghasafari P, George U, Pidaparti R. A review of inflammatory mechanism in airway diseases. Inflamm Res 2018; 68:59-74. [PMID: 30306206 DOI: 10.1007/s00011-018-1191-2] [Citation(s) in RCA: 142] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 09/12/2018] [Accepted: 09/27/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Inflammation in the lung is the body's natural response to injury. It acts to remove harmful stimuli such as pathogens, irritants, and damaged cells and initiate the healing process. Acute and chronic pulmonary inflammation are seen in different respiratory diseases such as; acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis (CF). FINDINGS In this review, we found that inflammatory response in COPD is determined by the activation of epithelial cells and macrophages in the respiratory tract. Epithelial cells and macrophages discharge transforming growth factor-β (TGF-β), which trigger fibroblast proliferation and tissue remodeling. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production, and airway-wall remodeling. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma. Mutation of the CF transmembrane conductance regulator (CFTR) gene results in CF. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens. We present mechanistic computational models (based on ordinary differential equations, partial differential equations and agent-based models) that have been applied in studying the complex physiological and pathological mechanisms of chronic inflammation in different airway diseases. CONCLUSION The scope of the present review is to explore the inflammatory mechanism in airway diseases and highlight the influence of aging on airways' inflammation mechanism. The main goal of this review is to encourage research collaborations between experimentalist and modelers to promote our understanding of the physiological and pathological mechanisms that control inflammation in different airway diseases.
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Affiliation(s)
| | - Uduak George
- College of Engineering, University of Georgia, Athens, GA, USA.,Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA
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15
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Rao RT, Scherholz ML, Hartmanshenn C, Bae SA, Androulakis IP. On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology. Comput Chem Eng 2017; 107:100-110. [PMID: 29353945 DOI: 10.1016/j.compchemeng.2017.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.
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Affiliation(s)
- Rohit T Rao
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Megerle L Scherholz
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Clara Hartmanshenn
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Seul-A Bae
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854.,Department of Biomedical Engineering, Rutgers The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854
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16
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Shams H, Soheilypour M, Peyro M, Moussavi-Baygi R, Mofrad MRK. Looking "Under the Hood" of Cellular Mechanotransduction with Computational Tools: A Systems Biomechanics Approach across Multiple Scales. ACS Biomater Sci Eng 2017; 3:2712-2726. [PMID: 33418698 DOI: 10.1021/acsbiomaterials.7b00117] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Signal modulation has been developed in living cells throughout evolution to promote utilizing the same machinery for multiple cellular functions. Chemical and mechanical modules of signal transmission and transduction are interconnected and necessary for organ development and growth. However, due to the high complexity of the intercommunication of physical intracellular connections with biochemical pathways, there are many missing details in our overall understanding of mechanotransduction processes, i.e., the process by which mechanical signals are converted to biochemical cascades. Cell-matrix adhesions are mechanically coupled to the nucleus through the cytoskeleton. This modulated and tightly integrated network mediates the transmission of mechanochemical signals from the extracellular matrix to the nucleus. Various experimental and computational techniques have been utilized to understand the basic mechanisms of mechanotransduction, yet many aspects have remained elusive. Recently, in silico experiments have made important contributions to the field of mechanobiology. Herein, computational modeling efforts devoted to understanding integrin-mediated mechanotransduction pathways are reviewed, and an outlook is presented for future directions toward using suitable computational approaches and developing novel techniques for addressing important questions in the field of mechanotransduction.
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Affiliation(s)
- Hengameh Shams
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Mohammad Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Mohaddeseh Peyro
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Ruhollah Moussavi-Baygi
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720-1762, United States
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17
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Kamisoglu K, Acevedo A, Almon RR, Coyle S, Corbett S, Dubois DC, Nguyen TT, Jusko WJ, Androulakis IP. Understanding Physiology in the Continuum: Integration of Information from Multiple - Omics Levels. Front Pharmacol 2017; 8:91. [PMID: 28289389 PMCID: PMC5327699 DOI: 10.3389/fphar.2017.00091] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 02/13/2017] [Indexed: 01/18/2023] Open
Abstract
In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.
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Affiliation(s)
- Kubra Kamisoglu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Alison Acevedo
- Department of Biomedical Engineering, Rutgers University, Piscataway NJ, USA
| | - Richard R Almon
- Department of Biological Sciences, University at Buffalo, Buffalo NY, USA
| | - Susette Coyle
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick NJ, USA
| | - Siobhan Corbett
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick NJ, USA
| | - Debra C Dubois
- Department of Biological Sciences, University at Buffalo, Buffalo NY, USA
| | - Tung T Nguyen
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ, USA
| | - William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Ioannis P Androulakis
- Department of Biomedical Engineering, Rutgers University, PiscatawayNJ, USA; Department of Chemical Engineering, Rutgers University, PiscatawayNJ, USA
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18
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Tomaiuolo M, Kottke M, Matheny RW, Reifman J, Mitrophanov AY. Computational identification and analysis of signaling subnetworks with distinct functional roles in the regulation of TNF production. MOLECULAR BIOSYSTEMS 2016; 12:826-38. [PMID: 26751842 DOI: 10.1039/c5mb00456j] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Inflammation is a complex process driven by the coordinated action of a vast number of pro- and anti-inflammatory molecular mediators. While experimental studies have provided an abundance of information about the properties and mechanisms of action of individual mediators, essential system-level regulatory patterns that determine the time-course of inflammation are not sufficiently understood. In particular, it is not known how the contributions from distinct signaling pathways involved in cytokine regulation combine to shape the overall inflammatory response over different time scales. We investigated the kinetics of the intra- and extracellular signaling network controlling the production of the essential pro-inflammatory cytokine, tumor necrosis factor (TNF), and its anti-inflammatory counterpart, interleukin 10 (IL-10), in a macrophage culture. To tackle the intrinsic complexity of the network, we employed a computational modeling approach using the available literature data about specific molecular interactions. Our computational model successfully captured experimentally observed short- and long-term kinetics of key inflammatory mediators. Subsequent model analysis showed that distinct subnetworks regulate IL-10 production by impacting different temporal phases of the cAMP response element-binding protein (CREB) phosphorylation. Moreover, the model revealed that functionally similar inhibitory control circuits regulate the early and late activation phases of nuclear factor κB and CREB. Finally, we identified and investigated distinct signaling subnetworks that independently control the peak height and tail height of the TNF temporal trajectories. The knowledge of such subnetwork-specific regulatory effects may facilitate therapeutic interventions aimed at precise modulation of the inflammatory response.
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Affiliation(s)
- Maurizio Tomaiuolo
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, ATTN: MCMR-TT, 504 Scott Street, Fort Detrick, MD, USA.
| | - Melissa Kottke
- Military Performance Division, U.S. Army Research Institute of Environmental Medicine, 15 Kansas Street, Building 42, Natick, MA 01760, USA
| | - Ronald W Matheny
- Military Performance Division, U.S. Army Research Institute of Environmental Medicine, 15 Kansas Street, Building 42, Natick, MA 01760, USA
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, ATTN: MCMR-TT, 504 Scott Street, Fort Detrick, MD, USA.
| | - Alexander Y Mitrophanov
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, ATTN: MCMR-TT, 504 Scott Street, Fort Detrick, MD, USA.
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19
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Soheilypour M, Mofrad MRK. Regulation of RNA-binding proteins affinity to export receptors enables the nuclear basket proteins to distinguish and retain aberrant mRNAs. Sci Rep 2016; 6:35380. [PMID: 27805000 PMCID: PMC5090210 DOI: 10.1038/srep35380] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/23/2016] [Indexed: 02/06/2023] Open
Abstract
Export of messenger ribonucleic acids (mRNAs) into the cytoplasm is a fundamental step in gene regulation processes, which is meticulously quality controlled by highly efficient mechanisms in eukaryotic cells. Yet, it remains unclear how the aberrant mRNAs are recognized and retained inside the nucleus. Using a new modelling approach for complex systems, namely the agent-based modelling (ABM) approach, we develop a minimal model of the mRNA quality control (QC) mechanism. Our results demonstrate that regulation of the affinity of RNA-binding proteins (RBPs) to export receptors along with the weak interaction between the nuclear basket protein (Mlp1 or Tpr) and RBPs are the minimum requirements to distinguish and retain aberrant mRNAs. Our results show that the affinity between Tpr and RBPs is optimized to maximize the retention of aberrant mRNAs. In addition, we demonstrate how the length of mRNA affects the QC process. Since longer mRNAs spend more time in the nuclear basket to form a compact conformation and initiate their export, nuclear basket proteins could more easily capture and retain them inside the nucleus.
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Affiliation(s)
- M. Soheilypour
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, 94720, USA
| | - M. R. K. Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics and Integrative Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
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20
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Parker RS, Hogg JS, Roy A, Kellum JA, Rimmelé T, Daun-Gruhn S, Fedorchak MV, Valenti IE, Federspiel WJ, Rubin J, Vodovotz Y, Lagoa C, Clermont G. Modeling and Hemofiltration Treatment of Acute Inflammation. Processes (Basel) 2016; 4:38. [PMID: 33134139 DOI: 10.3390/pr4040038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential filtering of cytokines and WBC does not provide substantial improvement in treatment outcomes vs. existing HA devices.
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Affiliation(s)
- Robert S Parker
- Department of Chemical and Petroleum Engineering; Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, 3550 Terrace St, Pittsburgh, PA 15213, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, 450 Technology Dr, Suite 300, Pittsburgh, PA 15219, USA
| | - Justin S Hogg
- Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology, 3501 Fifth Ave, 3064 BST3, Pittsburgh, PA 15260, USA
| | - Anirban Roy
- Department of Chemical and Petroleum Engineering; Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - John A Kellum
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, 3550 Terrace St, Pittsburgh, PA 15213, USA
| | - Thomas Rimmelé
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, 3550 Terrace St, Pittsburgh, PA 15213, USA
| | - Silvia Daun-Gruhn
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, 3550 Terrace St, Pittsburgh, PA 15213, USA
- Department of Surgery, University of Pittsburgh Medical Center, W944 Biomedical Sciences Tower, Pittsburgh, PA 15213, USA
| | - Morgan V Fedorchak
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, 450 Technology Dr, Suite 300, Pittsburgh, PA 15219, USA
| | - Isabella E Valenti
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - William J Federspiel
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, 450 Technology Dr, Suite 300, Pittsburgh, PA 15219, USA
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh, PA 15261, USA
| | - Yoram Vodovotz
- McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, 450 Technology Dr, Suite 300, Pittsburgh, PA 15219, USA
- Department of Surgery, University of Pittsburgh Medical Center, W944 Biomedical Sciences Tower, Pittsburgh, PA 15213, USA
| | - Claudio Lagoa
- Department of Surgery, University of Pittsburgh Medical Center, W944 Biomedical Sciences Tower, Pittsburgh, PA 15213, USA
| | - Gilles Clermont
- Department of Chemical and Petroleum Engineering; Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, 3550 Terrace St, Pittsburgh, PA 15213, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, 450 Technology Dr, Suite 300, Pittsburgh, PA 15219, USA
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Shi Z, Chapes SK, Ben-Arieh D, Wu CH. An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. PLoS One 2016; 11:e0161131. [PMID: 27556404 PMCID: PMC4996536 DOI: 10.1371/journal.pone.0161131] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 07/29/2016] [Indexed: 01/04/2023] Open
Abstract
We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-α ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies.
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Affiliation(s)
- Zhenzhen Shi
- Health Care Operations Resource Center, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, United States of America
| | - Stephen K. Chapes
- Division of Biology, Kansas State University, Manhattan, Kansas, United States of America
| | - David Ben-Arieh
- Health Care Operations Resource Center, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, United States of America
| | - Chih-Hang Wu
- Health Care Operations Resource Center, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, United States of America
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22
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Ichikawa K, Ohshima D, Sagara H. Regulation of signal transduction by spatial parameters: a case in NF-κB oscillation. IET Syst Biol 2016; 9:41-51. [PMID: 26672147 DOI: 10.1049/iet-syb.2013.0020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
NF-κB is a transcription factor regulating expression of more than 500 genes, and its dysfunction leads to the autoimmune and inflammatory diseases. In malignant cancer cells, NF-κB is constitutively activated. Thus the elucidation of mechanisms for NF-κB regulation is important for the establishment of therapeutic treatment caused by incorrect NF-κB responses. Cytoplasmic NF-κB translocates to the nucleus by the application of extracellular stimuli such as cytokines. Nuclear NF-κB is known to oscillate with the cycle of 1.5-4.5 h, and it is thought that the oscillation pattern regulates the expression profiles of genes. In this review, first we briefly describe regulation mechanisms of NF-κB. Next, published computational simulations on the oscillation of NF-κB are summarised. There are at least 60 reports on the computational simulation and analysis of NF-κB oscillation. Third, the importance of a 'space' for the regulation of oscillation pattern of NF-κB is discussed, showing altered oscillation pattern by the change in spatial parameters such as diffusion coefficient, nuclear to cytoplasmic volume ratio (N/C ratio), and transport through nuclear membrane. Finally, simulations in a true intracellular space (TiCS), which is an intracellular 3D space reconstructed in a computer with organelles such as nucleus and mitochondria are discussed.
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Hussain F, Langmead CJ, Mi Q, Dutta-Moscato J, Vodovotz Y, Jha SK. Automated parameter estimation for biological models using Bayesian statistical model checking. BMC Bioinformatics 2015; 16 Suppl 17:S8. [PMID: 26679759 PMCID: PMC4674867 DOI: 10.1186/1471-2105-16-s17-s8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system. Results Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. Conclusions We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.
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Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection. PLoS One 2015; 10:e0136139. [PMID: 26327290 PMCID: PMC4556515 DOI: 10.1371/journal.pone.0136139] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Accepted: 07/31/2015] [Indexed: 01/08/2023] Open
Abstract
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.
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Boissel JP, Auffray C, Noble D, Hood L, Boissel FH. Bridging Systems Medicine and Patient Needs. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225243 PMCID: PMC4394618 DOI: 10.1002/psp4.26] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While there is widespread consensus on the need both to change the prevailing research and development (R&D) paradigm and provide the community with an efficient way to personalize medicine, ecosystem stakeholders grapple with divergent conceptions about which quantitative approach should be preferred. The primary purpose of this position paper is to contrast these approaches. The second objective is to introduce a framework to bridge simulation outputs and patient outcomes, thus empowering the implementation of systems medicine.
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Affiliation(s)
| | - C Auffray
- European Institute for Systems Biology & Medicine, CNRS-UCBL-ENS, Université de Lyon France
| | - D Noble
- Department of Physiology, Anatomy & Genetics, University of Oxford Oxford, UK
| | - L Hood
- Institute for Systems Biology Seattle, Washington, USA
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Vodovotz Y. Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience. Interface Focus 2014; 4:20140004. [PMID: 25285195 DOI: 10.1098/rsfs.2014.0004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery , University of Pittsburgh , W944 Starzl Biomedical Sciences Tower, 200 Lothrop Street, Pittsburgh, PA 15213 , USA
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Azimi M, Bulat E, Weis K, Mofrad MRK. An agent-based model for mRNA export through the nuclear pore complex. Mol Biol Cell 2014; 25:3643-53. [PMID: 25253717 PMCID: PMC4230623 DOI: 10.1091/mbc.e14-06-1065] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
On the basis of previously published biophysical and biochemical parameters of mRNA export, a three-dimensional, coarse-grained, agent-based model is developed for the study and characterization of mRNA nucleocytoplasmic export. mRNA export from the nucleus is an essential step in the expression of every protein- coding gene in eukaryotes, but many aspects of this process remain poorly understood. The density of export receptors that must bind an mRNA to ensure export, as well as how receptor distribution affects transport dynamics, is not known. It is also unclear whether the rate-limiting step for transport occurs at the nuclear basket, in the central channel, or on the cytoplasmic face of the nuclear pore complex. Using previously published biophysical and biochemical parameters of mRNA export, we implemented a three-dimensional, coarse-grained, agent-based model of mRNA export in the nanosecond regime to gain insight into these issues. On running the model, we observed that mRNA export is sensitive to the number and distribution of transport receptors coating the mRNA and that there is a rate-limiting step in the nuclear basket that is potentially associated with the mRNA reconfiguring itself to thread into the central channel. Of note, our results also suggest that using a single location-monitoring mRNA label may be insufficient to correctly capture the time regime of mRNA threading through the pore and subsequent transport. This has implications for future experimental design to study mRNA transport dynamics.
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Affiliation(s)
- Mohammad Azimi
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, Graduate Program in Chemical Biology, Berkeley, Berkeley, CA 94720
| | - Evgeny Bulat
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, Graduate Program in Chemical Biology, Berkeley, Berkeley, CA 94720 Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Karsten Weis
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720 Institute of Biochemistry, ETH Zurich, 8093 Zurich, Switzerland
| | - Mohammad R K Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, Graduate Program in Chemical Biology, Berkeley, Berkeley, CA 94720
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Quintela BDM, dos Santos RW, Lobosco M. On the coupling of two models of the human immune response to an antigen. BIOMED RESEARCH INTERNATIONAL 2014; 2014:410457. [PMID: 25140313 PMCID: PMC4130187 DOI: 10.1155/2014/410457] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 04/15/2014] [Accepted: 04/15/2014] [Indexed: 12/24/2022]
Abstract
The development of mathematical models of the immune response allows a better understanding of the multifaceted mechanisms of the defense system. The main purpose of this work is to present a scheme for coupling distinct models of different scales and aspects of the immune system. As an example, we propose a new model where the local tissue inflammation processes are simulated with partial differential equations (PDEs) whereas a system of ordinary differential equations (ODEs) is used as a model for the systemic response. The simulation of distinct scenarios allows the analysis of the dynamics of various immune cells in the presence of an antigen. Preliminary results of this approach with a sensitivity analysis of the coupled model are shown but further validation is still required.
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Affiliation(s)
- Bárbara de M. Quintela
- Laboratory of Computational Physiology and High-Performance Computing (FISIOCOMP), Graduate Program in Computational Modeling, UFJF, Rua José Lourenço Kelmer s/n, Campus Universitário, Bairro São Pedro, 36036-900 Juiz de Fora, MG, Brazil
| | - Rodrigo Weber dos Santos
- Laboratory of Computational Physiology and High-Performance Computing (FISIOCOMP), Graduate Program in Computational Modeling, UFJF, Rua José Lourenço Kelmer s/n, Campus Universitário, Bairro São Pedro, 36036-900 Juiz de Fora, MG, Brazil
| | - Marcelo Lobosco
- Laboratory of Computational Physiology and High-Performance Computing (FISIOCOMP), Graduate Program in Computational Modeling, UFJF, Rua José Lourenço Kelmer s/n, Campus Universitário, Bairro São Pedro, 36036-900 Juiz de Fora, MG, Brazil
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Agent-based modeling of the immune system: NetLogo, a promising framework. BIOMED RESEARCH INTERNATIONAL 2014; 2014:907171. [PMID: 24864263 PMCID: PMC4016927 DOI: 10.1155/2014/907171] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 04/02/2014] [Indexed: 12/12/2022]
Abstract
Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.
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Shi ZZ, Wu CH, Ben-Arieh D. Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System. ACTA ACUST UNITED AC 2014. [DOI: 10.4236/ojmsi.2014.21004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Azimi M, Mofrad MRK. Higher nucleoporin-Importinβ affinity at the nuclear basket increases nucleocytoplasmic import. PLoS One 2013; 8:e81741. [PMID: 24282617 PMCID: PMC3840022 DOI: 10.1371/journal.pone.0081741] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 10/25/2013] [Indexed: 01/26/2023] Open
Abstract
Several in vitro studies have shown the presence of an affinity gradient in nuclear pore complex proteins for the import receptor Importinβ, at least partially contributing to nucleocytoplasmic transport, while others have historically argued against the presence of such a gradient. Nonetheless, the existence of an affinity gradient has remained an uncharacterized contributing factor. To shed light on the affinity gradient theory and better characterize how the existence of such an affinity gradient between the nuclear pore and the import receptor may influence the nucleocytoplasmic traffic, we have developed a general-purpose agent based modeling (ABM) framework that features a new method for relating rate constants to molecular binding and unbinding probabilities, and used our ABM approach to quantify the effects of a wide range of forward and reverse nucleoporin-Importinβ affinity gradients. Our results indicate that transport through the nuclear pore complex is maximized with an effective macroscopic affinity gradient of 2000 µM, 200 µM and 10 µM in the cytoplasmic, central channel and nuclear basket respectively. The transport rate at this gradient is approximately 10% higher than the transport rate for a comparable pore lacking any affinity gradient, which has a peak transport rate when all nucleoporins have an affinity of 200 µM for Importinβ. Furthermore, this optimal ratio of affinity gradients is representative of the ratio of affinities reported for the yeast nuclear pore complex – suggesting that the affinity gradient seen in vitro is highly optimized.
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Affiliation(s)
- Mohammad Azimi
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California, United States
| | - Mohammad R. K. Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California, United States
- * E-mail:
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Iskander KN, Osuchowski MF, Stearns-Kurosawa DJ, Kurosawa S, Stepien D, Valentine C, Remick DG. Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding. Physiol Rev 2013; 93:1247-88. [PMID: 23899564 DOI: 10.1152/physrev.00037.2012] [Citation(s) in RCA: 284] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Sepsis represents the host's systemic inflammatory response to a severe infection. It causes substantial human morbidity resulting in hundreds of thousands of deaths each year. Despite decades of intense research, the basic mechanisms still remain elusive. In either experimental animal models of sepsis or human patients, there are substantial physiological changes, many of which may result in subsequent organ injury. Variations in age, gender, and medical comorbidities including diabetes and renal failure create additional complexity that influence the outcomes in septic patients. Specific system-based alterations, such as the coagulopathy observed in sepsis, offer both potential insight and possible therapeutic targets. Intracellular stress induces changes in the endoplasmic reticulum yielding misfolded proteins that contribute to the underlying pathophysiological changes. With these multiple changes it is difficult to precisely classify an individual's response in sepsis as proinflammatory or immunosuppressed. This heterogeneity also may explain why most therapeutic interventions have not improved survival. Given the complexity of sepsis, biomarkers and mathematical models offer potential guidance once they have been carefully validated. This review discusses each of these important factors to provide a framework for understanding the complex and current challenges of managing the septic patient. Clinical trial failures and the therapeutic interventions that have proven successful are also discussed.
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Affiliation(s)
- Kendra N Iskander
- Department of Pathology, Boston University School of Medicine, Boston, Massachusetts, USA
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Abstract
OBJECTIVES To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis. DATA SOURCES PubMed search and review of relevant medical literature. SUMMARY Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed. CONCLUSIONS Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.
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Moore JWJ, Moyo D, Beattie L, Andrews PS, Timmis J, Kaye PM. Functional complexity of the Leishmania granuloma and the potential of in silico modeling. Front Immunol 2013; 4:35. [PMID: 23423646 PMCID: PMC3573688 DOI: 10.3389/fimmu.2013.00035] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Accepted: 01/30/2013] [Indexed: 11/16/2022] Open
Abstract
In human and canine visceral leishmaniasis and in various experimental models of this disease, host resistance is strongly linked to efficient granuloma development. However, it is unknown exactly how the granuloma microenvironment executes an effective antileishmanial response. Recent studies, including using advanced imaging techniques, have improved our understanding of granuloma biology at the cellular level, highlighting heterogeneity in granuloma development and function, and hinting at complex cellular, temporal, and spatial dynamics. In this mini-review, we discuss the factors involved in the formation and function of Leishmania donovani-induced hepatic granulomas, as well as their importance in protecting against inflammation-associated tissue damage and the generation of immunity to rechallenge. Finally, we discuss the role that computational, agent-based models may play in answering outstanding questions within the field.
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Affiliation(s)
- John W J Moore
- Centre for Immunology and Infection, Hull York Medical School and Department of Biology, University of York York, UK
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Scheff JD, Mavroudis PD, Foteinou PT, Calvano SE, Androulakis IP. Modeling physiologic variability in human endotoxemia. Crit Rev Biomed Eng 2013; 40:313-22. [PMID: 23140122 DOI: 10.1615/critrevbiomedeng.v40.i4.60] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.
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Affiliation(s)
- Jeremy D Scheff
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
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Demarest J, Pagsuyoin S, Learmonth G, Mellor J, Dillingham R. Development of a Spatial and Temporal Agent-Based Model for Studying Water and Health Relationships: The Case Study of Two Villages in Limpopo, South Africa. JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION : JASSS 2013; 16:3. [PMID: 25530709 PMCID: PMC4269164 DOI: 10.18564/jasss.2169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Diarrhea, the second leading cause of child morbidity and mortality, can have detrimental effects in the physical and cognitive development of children in developing countries. Health interventions (e.g., increased access to health services and safe water) designed to address this problem are difficult to implement in resource-limited settings. In this paper, we present a tool for understanding the complex relationship between water and public health in rural areas of a developing country. A spatial and temporal agent-based model (ABM) was developed to simulate the current water, sanitation, and health status in two villages in Limpopo Province, South Africa. The model was calibrated using empirical data and published sources. It was used to simulate the effects of poor water quality on the frequency of diarrheal episodes in children, and consequently on child development. Preliminary simulation results show that at the current total coliform levels in the water sources of the studied villages, children are expected to experience stunting by as much as -1.0 standard deviations from the World Health Organization height norms. With minor modifications, the calibrated ABM can be used to design and evaluate intervention strategies for improving child health in these villages. The model can also be applied to other regions worldwide that face the same environmental challenges and conditions as the studied villages.
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Scheff JD, Mavroudis PD, Calvano SE, Androulakis IP. Translational applications of evaluating physiologic variability in human endotoxemia. J Clin Monit Comput 2012. [PMID: 23203205 DOI: 10.1007/s10877-012-9418-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.
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Affiliation(s)
- Jeremy D Scheff
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
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Abstract
Bacterial infections can be of two types: acute or chronic. The chronic bacterial infections are characterized
by being a large bacterial infection and/or an infection where the bacteria grows rapidly. In these cases, the immune
response is not capable of completely eliminating the infection which may lead to the formation of a pattern
known as microabscess (or abscess). The microabscess is characterized by an area comprising fluids, bacteria,
immune cells (mainly neutrophils), and many types of dead cells. This distinct pattern of formation can only be
numerically reproduced and studied by models that capture the spatiotemporal dynamics of the human immune
system (HIS). In this context, our work aims to develop and implement an initial computational model to study
the process of microabscess formation during a bacterial infection.
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McDermott JE, Jarman K, Taylor R, Lancaster M, Shankaran H, Vartanian KB, Stevens SL, Stenzel-Poore MP, Sanfilippo A. Modeling dynamic regulatory processes in stroke. PLoS Comput Biol 2012; 8:e1002722. [PMID: 23071432 PMCID: PMC3469412 DOI: 10.1371/journal.pcbi.1002722] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2011] [Accepted: 08/17/2012] [Indexed: 11/29/2022] Open
Abstract
The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms. Computational modeling aims to use mathematical and algorithmic principles to link components of biological systems to predict system behavior. In the past such models have described a small set of carefully studied molecular interactions (proteins in signal transduction pathways) or larger abstract components (cell types or functional processes in the immune system). In this study we use data from global transcriptional analysis of the processes of neuroprotection in a mouse model of stroke to generate functional modules, groups of genes that coherently act to accomplish functions. We then derive equations relating the expression of these modules to one another, treating these individual equations as a closed system, and demonstrate that the model can be used to simulate the gene expression of the system over time. Our work is novel in describing the use of global transcriptomic data to develop dynamic models of expression in an animal model. We believe that the models developed will aid in understanding the complex dynamics of neuroprotection and provide ways to predict outcomes in terms of neuroprotection or injury. This approach will be broadly applicable to other problems and provides an approach to building dynamic models from the bottom up.
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Affiliation(s)
- Jason E McDermott
- Pacific Northwest National Laboratory, Richland, Washington, United States of America.
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40
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Protection against Th17 cells differentiation by an interleukin-23 receptor cytokine-binding homology region. PLoS One 2012; 7:e45625. [PMID: 23029144 PMCID: PMC3446948 DOI: 10.1371/journal.pone.0045625] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2012] [Accepted: 08/21/2012] [Indexed: 12/18/2022] Open
Abstract
Th17 cells have been reported to produce proinflammatory cytokines like Interleukin-17, IL-22, and regarded as important players in various inflammatory diseases. One of the IL-12 cytokine family cytokines, IL-23, composed of p19 and p40 subunit, is known for its potential to promote Th17 development and IL-17 producing, and the IL-23/IL-17 pathway is considered to be potential therapeutic target for autoimmune inflammation responses. Knockout mice deficient in either IL-23 or IL-17 related genes can suppress the allergic responses. Several IL-23 or IL-17 neutralizing agents are being evaluated in vitro or in vivo to disrupt the IL-23/IL-17 axis. Herein, we report that prokaryotically expressed soluble IL-23 receptor cytokine-binding homology region as an endogenous extracellular receptor analogue could be a natural antagonist against IL-23/IL-17 axis. We provide evidence that IL23R-CHR can bind to IL-23 in a dose-dependent manner in vitro, and block IL-23 signal by IL23R-CHR reducing the RORγt expression, which in turn lowers the expression of IL-17/IL-22, thus protecting naive CD4+ T cells against Th17 development. Together, this study indicates the importance of IL-23 pathway in Th17 development and the negative regulation of Th17 development by IL23R-CHR, and highlights the important roles of the soluble receptor extracellular region in the therapeutic strategy of neutralizing IL-23.
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Narang V, Decraene J, Wong SY, Aiswarya BS, Wasem AR, Leong SR, Gouaillard A. Systems immunology: a survey of modeling formalisms, applications and simulation tools. Immunol Res 2012; 53:251-65. [PMID: 22528121 DOI: 10.1007/s12026-012-8305-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2012; 59:893-903. [PMID: 21712727 DOI: 10.2310/jim.0b013e318224d8cc] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
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Affiliation(s)
- Mary F McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX 77030, USA.
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An G, Nieman G, Vodovotz Y. Computational and systems biology in trauma and sepsis: current state and future perspectives. INTERNATIONAL JOURNAL OF BURNS AND TRAUMA 2012; 2:1-10. [PMID: 22928162 PMCID: PMC3415970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 12/30/2011] [Accepted: 01/15/2012] [Indexed: 06/01/2023]
Abstract
Trauma, often accompanied by hemorrhage, is a leading cause of death worldwide, often leading to inflammation-related late complications that include sepsis and multiple organ failure. These secondary complications are a manifestation of the complexity of biological responses elicited by trauma/hemorrhage, responses that span most, if not all, cell types, tissues, and organ systems. This daunting complexity at the patient level is manifest by the near total dearth of available therapeutics, and we suggest that this dire condition is due in large part to the lack of a rational, systems-oriented framework for drug development, clinical trial design, in-hospital diagnostics, and post-hospital care. We have further suggested that mechanistic computational modeling can form the basis of such a rational framework, given the maturity of systems biology/computational biology. Herein, we briefly summarize the state of the art of these approaches, and highlight the biological insights and novel hypotheses derived from these approaches. We propose a rational framework for transitioning through the currently fragmented process from identification of biological networks that are potential therapeutic targets, through clinical trial design, to personalized diagnosis and care. Insights derived from systems and computational biology in trauma and sepsis include the centrality of Damage-Associated Molecular Pattern molecules as drivers of both beneficial and detrimental inflammation, along with a novel view of multiple organ dysfunction as a cascade of containment failures with distinct implications for therapy. Finally, we suggest how these insights might be best implemented to drive transformational change in the fields of trauma and sepsis.
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Affiliation(s)
- Gary An
- Department of Surgery, University of ChicagoChicago, IL 60637
| | - Gary Nieman
- Department of Surgery, Upstate Medical UniversitySyracuse, NY 13210
| | - Yoram Vodovotz
- Department of Surgery, University of PittsburghPittsburgh, PA 15213
- Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of PittsburghPittsburgh, PA 15219
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Kim M, Christley S, Alverdy JC, Liu D, An G. Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model. Surg Infect (Larchmt) 2012; 13:18-32. [PMID: 22217195 DOI: 10.1089/sur.2011.057] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Necrotizing enterocolitis (NEC) is a complex disease involving prematurity, enteral feeding, and bacterial effects. We propose that the underlying initial condition in its pathogenesis is reduced ability of the neonatal gut epithelial cells (NGECs) to clear oxidative stress (OS), and that when such a NGEC population is exposed to enteral feeding, the increased metabolic OS tips the population toward apoptosis, inflammation, bacterial activation, and eventual necrosis. The multi-factorial complexity of NEC requires characterization with computational modeling, and herein, we used an agent-based model (ABM) to instantiate and examine our unifying hypothesis of the pathogenesis of NEC. METHODS An ABM of the neonatal gut was created with NGEC computational agents incorporating rules for pathways for OS, p53, tight junctions, Toll-like receptor (TLR)-4, nitric oxide, and nuclear factor-kappa beta (NF-κB). The modeled bacteria activated TLR-4 on contact with NGECs. Simulations included parameter sweeps of OS response, response to feeding, addition of bacteria, and alterations in gut mucus production. RESULTS The ABM reproduced baseline cellular respiration and clearance of OS. Reduction in OS clearance consistent with clinical NEC led to senescence, apoptosis, or inflammation, with disruption of tight junctions, but rarely to NGEC necrosis. An additional "hit" of bacteria activating TLR-4 potentiated a shift to NGEC necrosis across the entire population. The mucus layer was modeled to limit bacterial-NGEC interactions and reduce this effect, but concomitant apoptosis in the goblet cell population reduced the efficacy of the mucus layer and limited its protective effect in simulated experiments. This finding suggests a means by which increased apoptosis at the cellular population level can lead to a transition to the necrosis outcome. CONCLUSIONS Our ABM incorporates known components of NEC and demonstrates that impaired OS management can lead to apoptosis and inflammation of NGECs, rendering the system susceptible to an additional insult involving regionalized mucus barrier failure and TLR-4 activation, which potentiates the necrosis outcome. This type of integrative dynamic knowledge representation can be a useful adjunct to help guide and contextualize research.
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Affiliation(s)
- Moses Kim
- Department of Surgery, University of Chicago, Chicago, Illinois 60637, USA
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Abstract
Inflammation is an array of immune responses to infection and injury. It results from a complex immune cascade and is the basis of many chronic diseases such as arthritis, diabetes, and cancer. Numerous mathematical models have been developed to describe the disease progression and effects of anti-inflammatory drugs. This review illustrates the state of the art in modeling the effects of diverse drugs for treating inflammation, describes relevant biomarkers amenable to modeling, and summarizes major advantages and limitations of the published pharmacokinetic/ pharmacodynamic (PK/PD) models. Simple direct inhibitory models are often used to describe in vitro effects of anti-inflammatory drugs. Indirect response models are more mechanism based and have been widely applied to the turnover of symptoms and biomarkers. These, along with target-mediated and transduction models, have been successfully applied to capture the PK/PD of many anti-inflammatory drugs and describe disease progression of inflammation. Biologics have offered opportunities to address specific mechanisms of action, and evolve small systems models to quantitatively capture the underlying physiological processes. More advanced mechanistic models should allow evaluation of the roles of some key mediators in disease progression, assess drug interactions, and better translate drug properties from in vitro and animal data to patients.
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Affiliation(s)
- Hoi-Kei Lon
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY 14214, USA
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Azimi M, Jamali Y, Mofrad MRK. Accounting for diffusion in agent based models of reaction-diffusion systems with application to cytoskeletal diffusion. PLoS One 2011; 6:e25306. [PMID: 21966493 PMCID: PMC3179499 DOI: 10.1371/journal.pone.0025306] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2011] [Accepted: 08/31/2011] [Indexed: 12/18/2022] Open
Abstract
Diffusion plays a key role in many biochemical reaction systems seen in nature. Scenarios where diffusion behavior is critical can be seen in the cell and subcellular compartments where molecular crowding limits the interaction between particles. We investigate the application of a computational method for modeling the diffusion of molecules and macromolecules in three-dimensional solutions using agent based modeling. This method allows for realistic modeling of a system of particles with different properties such as size, diffusion coefficients, and affinity as well as the environment properties such as viscosity and geometry. Simulations using these movement probabilities yield behavior that mimics natural diffusion. Using this modeling framework, we simulate the effects of molecular crowding on effective diffusion and have validated the results of our model using Langevin dynamics simulations and note that they are in good agreement with previous experimental data. Furthermore, we investigate an extension of this framework where single discrete cells can contain multiple particles of varying size in an effort to highlight errors that can arise from discretization that lead to the unnatural behavior of particles undergoing diffusion. Subsequently, we explore various algorithms that differ in how they handle the movement of multiple particles per cell and suggest an algorithm that properly accommodates multiple particles of various sizes per cell that can replicate the natural behavior of these particles diffusing. Finally, we use the present modeling framework to investigate the effect of structural geometry on the directionality of diffusion in the cell cytoskeleton with the observation that parallel orientation in the structural geometry of actin filaments of filopodia and the branched structure of lamellipodia can give directionality to diffusion at the filopodia-lamellipodia interface.
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Affiliation(s)
- Mohammad Azimi
- Molecular Cell Biomechanics Laboratory, Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Yousef Jamali
- Molecular Cell Biomechanics Laboratory, Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Mohammad R. K. Mofrad
- Molecular Cell Biomechanics Laboratory, Department of Bioengineering, University of California, Berkeley, California, United States of America
- * E-mail:
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Seal JB, Alverdy JC, Zaborina O, An G. Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis. Theor Biol Med Model 2011; 8:33. [PMID: 21929759 PMCID: PMC3184268 DOI: 10.1186/1742-4682-8-33] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2011] [Accepted: 09/19/2011] [Indexed: 01/07/2023] Open
Abstract
Background There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis. Methodology/Principal Findings An agent-based model (ABM) of virulence regulation in Pseudomonas aeruginosa was developed by translating bacterial and host cell sense-and-response mechanisms into behavioral rules for computational agents and integrated into a virtual environment representing the host-microbe interface in the gut. The resulting gut milieu ABM (GMABM) was used to: 1) investigate a potential clinically relevant laboratory experimental condition not yet developed - i.e. non-lethal transient segmental intestinal ischemia, 2) examine the sufficiency of existing hypotheses to explain experimental data - i.e. lethality in a model of major surgical insult and stress, and 3) produce behavior to potentially guide future experimental design - i.e. suggested sample points for a potential laboratory model of non-lethal transient intestinal ischemia. Furthermore, hypotheses were generated to explain certain discrepancies between the behaviors of the GMABM and biological experiments, and new investigatory avenues proposed to test those hypotheses. Conclusions/Significance Agent-based modeling can account for the spatio-temporal dynamics of an HPI, and, even when carried out with a relatively high degree of abstraction, can be useful in the investigation of system-level consequences of putative mechanisms operating at the individual agent level. We suggest that an integrated and iterative heuristic relationship between computational modeling and more traditional laboratory and clinical investigations, with a focus on identifying useful and sufficient degrees of abstraction, will enhance the efficiency and translational productivity of biomedical research.
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Affiliation(s)
- John B Seal
- Department of Surgery, University of Chicago, 5841 South Maryland Ave, MC 5031, Chicago, IL 60637, USA
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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2011; 59. [PMID: 21712727 PMCID: PMC3196807 DOI: 10.231/jim.0b013e318224d8cc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
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Affiliation(s)
- Mary F. McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston TX USA,Contact: Mary F. McGuire, School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin, #600, Houston, TX 77030 USA, , 1-832-364-6734
| | - M. Sriram Iyengar
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston TX USA
| | - David W. Mercer
- Department of Surgery, University of Nebraska Medical Center, Omaha NE USA
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Dietert RR. Fractal immunology and immune patterning: potential tools for immune protection and optimization. J Immunotoxicol 2011; 8:101-10. [PMID: 21428733 DOI: 10.3109/1547691x.2011.559951] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fractals are self-similar geometric patterns that are inherently embedded throughout nature. Their discovery and application have produced significant benefits across a wide variety of biomedical applications. Recently, complex physiological systems (e.g., neurological, respiratory, cardiovascular) have been shown to exhibit fractal dimensions that are capable of distinguishing among physiologic function versus dysfunction and, in turn, health versus disease. Additionally, fractal data suggest that the immune system operates under similar patterned relationships, and this is in keeping with the recent findings that immune-based diseases are organized according to specific patterns. This review considers the potential benefits of using fractal analysis along with considerations of nonlinearity, scaling, and chaos as calibration tools to obtain holistic information on immune-environment interactions. The potential uses of both synthetic and artificial immune systems for improved protection of the biological immune system are also discussed. The addition of holistic measures of immune status to currently collected biomarkers of immunotoxicity has the potential to increase the effectiveness of health risk assessment. The objective of extending fractal physiology analyses to the immune system would be to promote immune optimization as a public health benefit, which would include improved: (1) immunotoxicity testing and effective health risk reduction and (2) measures of effective immune management for children, adults, and aged individuals.
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Affiliation(s)
- Rodney R Dietert
- Department of Microbiology and Immunology, Cornell University, Ithaca, NY 14853, USA.
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An G, Bartels J, Vodovotz Y. In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation. Drug Dev Res 2010; 72:187-200. [PMID: 21552346 DOI: 10.1002/ddr.20415] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.
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Affiliation(s)
- Gary An
- Department of Surgery, University of Chicago, Chicago, IL 60637
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