1
|
Krasnyakov I, Bratsun D. Cell-Based Modeling of Tissue Developing in the Scaffold Pores of Varying Cross-Sections. Biomimetics (Basel) 2023; 8:562. [PMID: 38132501 PMCID: PMC10741956 DOI: 10.3390/biomimetics8080562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
In this work, we present a mathematical model of cell growth in the pores of a perfusion bioreactor through which a nutrient solution is pumped. We have developed a 2-D vertex model that allows us to reproduce the microscopic dynamics of the microenvironment of cells and describe the occupation of the pore space with cells. In this model, each cell is represented by a polygon; the number of vertices and shapes may change over time. The model includes mitotic cell division and intercalation. We study the impact of two factors on cell growth. On the one hand, we consider a channel of variable cross-section, which models a scaffold with a porosity gradient. On the other hand, a cluster of cells grows under the influence of a nutrient solution flow, which establishes a non-uniform distribution of shear stresses in the pore space. We present the results of numerical simulation of the tissue growth in a wavy channel. The model allows us to obtain complete microscopic information that includes the dynamics of intracellular pressure, the local elastic energy, and the characteristics of cell populations. As we showed, in a functional-graded scaffold, the distribution of the shear stresses in the pore space has a complicated structure, which implies the possibility of controlling the growth zones by varying the pore geometry.
Collapse
Affiliation(s)
| | - Dmitry Bratsun
- Applied Physics Department, Perm National Research Polytechnic University, 614990 Perm, Russia;
| |
Collapse
|
2
|
Shou Y, Johnson SC, Quek YJ, Li X, Tay A. Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system. Mater Today Bio 2022; 14:100269. [PMID: 35514433 PMCID: PMC9062348 DOI: 10.1016/j.mtbio.2022.100269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.
Collapse
Key Words
- ABM, agent-based model
- APC, antigen-presenting cell
- BV, blood vessel
- Biomaterials
- CPM, Cellular Potts model
- Computational models
- DC, dendritic cell
- ECM, extracellular matrix
- FDC, follicular dendritic cell
- FRC, fibroblastic reticular cell
- Immunotherapy
- LEC, lymphatic endothelial cell
- LN, lymph node
- LV, lymphatic vessel
- Lymph node
- Lymphatic system
- ODE, ordinary differential equation
- PDE, partial differential equation
- PDMS, polydimethylsiloxane
Collapse
Affiliation(s)
- Yufeng Shou
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
| | - Sarah C. Johnson
- Department of Bioengineering, Stanford University, CA, 94305, USA
- Department of Bioengineering, Imperial College London, South Kensington, SW72AZ, UK
| | - Ying Jie Quek
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- Singapore Immunology Network, Agency for Science, Technology and Research, 138648, Singapore
| | - Xianlei Li
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
| | - Andy Tay
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, 117599, Singapore
- NUS Tissue Engineering Program, National University of Singapore, 117510, Singapore
| |
Collapse
|
3
|
Sego TJ, Mochan ED, Ermentrout GB, Glazier JA. A multiscale multicellular spatiotemporal model of local influenza infection and immune response. J Theor Biol 2022; 532:110918. [PMID: 34592264 PMCID: PMC8478073 DOI: 10.1016/j.jtbi.2021.110918] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 12/11/2022]
Abstract
Respiratory viral infections pose a
serious public health concern, from mild seasonal influenza to pandemics
like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection
impact nearly all aspects of the progression of a viral infection, like
the dependence of viral replication rates on the type of cell and
pathogen, the strength of the immune response and localization of
infection. Mathematical modeling is often used to describe respiratory
viral infections and the immune response to them using ordinary
differential equation (ODE) models. However, ODE models neglect
spatially-resolved biophysical mechanisms like lesion shape and the
details of viral transport, and so cannot model spatial effects of a
viral infection and immune response. In this work, we develop a
multiscale, multicellular spatiotemporal model of influenza infection and
immune response by combining non-spatial ODE modeling and spatial,
cell-based modeling. We employ cellularization, a recently developed
method for generating spatial, cell-based, stochastic models from
non-spatial ODE models, to generate much of our model from a calibrated
ODE model that describes infection, death and recovery of susceptible
cells and innate and adaptive responses during influenza infection, and
develop models of cell migration and other mechanisms not explicitly
described by the ODE model. We determine new model parameters to generate
agreement between the spatial and original ODE models under certain
conditions, where simulation replicas using our model serve as
microconfigurations of the ODE model, and compare results between the
models to investigate the nature of viral exposure and impact of
heterogeneous infection on the time-evolution of the viral infection. We
found that using spatially homogeneous initial exposure conditions
consistently with those employed during calibration of the ODE model
generates far less severe infection, and that local exposure to virus
must be multiple orders of magnitude greater than a uniformly applied
exposure to all available susceptible cells. This strongly suggests a
prominent role of localization of exposure in influenza A infection. We
propose that the particularities of the microenvironment to which a virus
is introduced plays a dominant role in disease onset and progression, and
that spatially resolved models like ours may be important to better
understand and more reliably predict future health states based on
susceptibility of potential lesion sites using spatially resolved patient
data of the state of an infection. We can readily integrate the immune
response components of our model into other modeling and simulation
frameworks of viral infection dynamics that do detailed modeling of other
mechanisms like viral internalization and intracellular viral replication
dynamics, which are not explicitly represented in the ODE model. We can
also combine our model with available experimental data and modeling of
exposure scenarios and spatiotemporal aspects of mechanisms like
mucociliary clearance that are only implicitly described by the ODE
model, which would significantly improve the ability of our model to
present spatially resolved predictions about the progression of influenza
infection and immune response.
Collapse
Affiliation(s)
- T J Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Ericka D Mochan
- Department of Analytical, Physical, and Social Sciences, Carlow University, Pittsburgh, PA, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - James A Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| |
Collapse
|
4
|
Abstract
This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.
Collapse
|
5
|
Vyklyuk Y, Manylich M, Škoda M, Radovanović MM, Petrović MD. Modeling and analysis of different scenarios for the spread of COVID-19 by using the modified multi-agent systems - Evidence from the selected countries. RESULTS IN PHYSICS 2021; 20:103662. [PMID: 33318892 PMCID: PMC7724467 DOI: 10.1016/j.rinp.2020.103662] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 05/08/2023]
Abstract
Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computer-time and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the high-accuracy matching of the forecast model with real data.
Collapse
Affiliation(s)
- Yaroslav Vyklyuk
- Institute of Laser and Optoelectronic Intelligent Manufacturing, College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China
- Department of Artificial Intelligence at Lviv Polytechnic National University, Lviv, Bandera str, 12, 79013, Ukraine
| | | | - Miroslav Škoda
- Department of Management and Accounting, DTI University, 018 41 Dubnica nad Váhom, Slovakia
| | - Milan M Radovanović
- Geographical Institute "Jovan Cvijić", Serbian Academy of Sciences and Arts, Djure Jakšića St. 9, Belgrade 11000, Serbia
- South Ural State University, Institute of Sports, Tourism and Service, Sony Krivoy St. 60, Chelyabinsk 454000, Russia
| | - Marko D Petrović
- Geographical Institute "Jovan Cvijić", Serbian Academy of Sciences and Arts, Djure Jakšića St. 9, Belgrade 11000, Serbia
- South Ural State University, Institute of Sports, Tourism and Service, Sony Krivoy St. 60, Chelyabinsk 454000, Russia
| |
Collapse
|
6
|
Rosales GS, Darias NT. Introduction to Multiscale Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11472-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
|
7
|
MATHEMATICAL MODELLING OF IMMUNE PROCESSES AND ITS APPLICATION. BIOTECHNOLOGIA ACTA 2020. [DOI: 10.15407/biotech13.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The aim of the study was to develop a mathematical model to research hypoxic states in case of simulation of an organism infectious lesions. The model is based on the methods of mathematical modeling and the theory of optimal control of moving objects. The processes of organism damage are simulated with the mathematical model of immune response developed by G.I. Marchuk and the members of his scientific school, adapted to current conditions. This model is based on Burnet’s clone selection theory of the determining role of antigen. Simulation results using the model are presented. The dependencies of infectious courses on the volumetric velocity of systemic blood flow is analyzed on the complex mathematical model of immune response, respiratory and blood circulation systems. The immune system is shown to be rather sensitive to the changes in blood flow via capillaries. Thus, the organ blood flows can be used as parameters for the model by which the respiratory, immune response, and blood circulation systems interact and interplay.
Collapse
|
8
|
Bouchnita A, Jebrane A. A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions. CHAOS, SOLITONS, AND FRACTALS 2020; 138:109941. [PMID: 32834575 PMCID: PMC7269965 DOI: 10.1016/j.chaos.2020.109941] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 05/26/2020] [Indexed: 05/03/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we reveal that pre-symptomatic transmission accelerates the onset of the exponential growth of cases. After that, we demonstrate that the persistence of SARS-CoV-2 on hard surfaces determines the number of cases reached during the peak of the epidemic. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China.
Collapse
Affiliation(s)
- Anass Bouchnita
- Complex Systems and Interactions Team, Ecole Centrale Casablanca, Ville Verte, Bouskoura, Casablanca 20000, Morocco
| | - Aissam Jebrane
- Complex Systems and Interactions Team, Ecole Centrale Casablanca, Ville Verte, Bouskoura, Casablanca 20000, Morocco
| |
Collapse
|
9
|
Brown Adipocyte and Splenocyte Co-Culture Maintains Regulatory T Cell Subset in Intermittent Hypobaric Conditions. Tissue Eng Regen Med 2019; 16:539-548. [PMID: 31624708 PMCID: PMC6778593 DOI: 10.1007/s13770-019-00205-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/15/2019] [Accepted: 07/16/2019] [Indexed: 11/02/2022] Open
Abstract
Background Brown adipocytes have thermogenic characteristics in neonates and elicit anti-inflammatory responses. We postulated that thermogenic brown adipocytes produce distinctive intercellular effects in a hypobaric state. The purpose of this study is to analyze the correlation between brown adipocyte and regulatory T cell (Treg) expression under intermittent hypobaric conditions. Methods Brown and white adipocytes were harvested from the interscapular and flank areas of C57BL6 mice, respectively. Adipocytes were cultured with syngeneic splenocytes after isolation and differentiation. Intermittent hypobaric conditions were generated using cyclic negative pressure application for 48 h in both groups of adipocytes. Expression levels of Tregs (CD4 + CD25 + Foxp3 + T cells), cytokines [tumor necrosis factor-α (TNF-α) and interleukin-10 (IL-10), and the programmed death-ligand 1 (PD-L1)] co-inhibitory ligand were examined. Results Splenocytes, cultured with brown and white adipocytes, exhibited comparable Treg expression in a normobaric state. Under hypobaric conditions, brown adipocytes maintained a subset of Tregs. However, a decrease in Tregs was found in the white adipocyte group. TNF-α levels increased in both groups under hypobaric conditions. In the brown adipocyte group, anti-inflammatory IL-10 expression increased significantly; meanwhile, IL-10 expression decreased in the white adipocyte group. PD-L1 levels increased more significantly in brown adipocytes than in white adipocytes under hypobaric conditions. Conclusion Both brown and white adipocytes support Treg expression when they are cultured with splenocytes. Of note, brown adipocytes maintained Treg expression in intermittent hypobaric conditions. Anti-inflammatory cytokines and co-inhibitory ligands mediate the immunomodulatory effects of brown adipocytes under altered atmospheric conditions. Brown adipocytes showed the feasibility as a source of adjustment in physical stresses.
Collapse
|
10
|
Grebennikov DS, Donets DO, Orlova OG, Argilaguet J, Meyerhans A, Bocharov GA. Mathematical Modeling of the Intracellular Regulation of Immune Processes. Mol Biol 2019. [DOI: 10.1134/s002689331905008x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
11
|
Grebennikov D, Bouchnita A, Volpert V, Bessonov N, Meyerhans A, Bocharov G. Spatial Lymphocyte Dynamics in Lymph Nodes Predicts the Cytotoxic T Cell Frequency Needed for HIV Infection Control. Front Immunol 2019; 10:1213. [PMID: 31244829 PMCID: PMC6579925 DOI: 10.3389/fimmu.2019.01213] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/13/2019] [Indexed: 11/29/2022] Open
Abstract
The surveillance of host body tissues by immune cells is central for mediating their defense function. In vivo imaging technologies have been used to quantitatively characterize target cell scanning and migration of lymphocytes within lymph nodes (LNs). The translation of these quantitative insights into a predictive understanding of immune system functioning in response to various perturbations critically depends on computational tools linking the individual immune cell properties with the emergent behavior of the immune system. By choosing the Newtonian second law for the governing equations, we developed a broadly applicable mathematical model linking individual and coordinated T-cell behaviors. The spatial cell dynamics is described by a superposition of autonomous locomotion, intercellular interaction, and viscous damping processes. The model is calibrated using in vivo data on T-cell motility metrics in LNs such as the translational speeds, turning angle speeds, and meandering indices. The model is applied to predict the impact of T-cell motility on protection against HIV infection, i.e., to estimate the threshold frequency of HIV-specific cytotoxic T cells (CTLs) that is required to detect productively infected cells before the release of viral particles starts. With this, it provides guidance for HIV vaccine studies allowing for the migration of cells in fibrotic LNs.
Collapse
Affiliation(s)
- Dmitry Grebennikov
- Moscow Institute of Physics and Technology, National Research University, Dolgoprudny, Russia.,Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia.,Peoples' Friendship University of Russia (RUDN University), Moscow, Russia
| | - Anass Bouchnita
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vitaly Volpert
- Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.,Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, France.,INRIA Team Dracula, INRIA Lyon La Doua, Villeurbanne, France
| | - Nikolay Bessonov
- Institute of Problems of Mechanical Engineering, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Andreas Meyerhans
- Infection Biology Laboratory, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Gennady Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia.,Sechenov First Moscow State Medical University, Moscow, Russia
| |
Collapse
|
12
|
Benchaib MA, Bouchnita A, Volpert V, Makhoute A. Mathematical Modeling Reveals That the Administration of EGF Can Promote the Elimination of Lymph Node Metastases by PD-1/PD-L1 Blockade. Front Bioeng Biotechnol 2019; 7:104. [PMID: 31157216 PMCID: PMC6528618 DOI: 10.3389/fbioe.2019.00104] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 04/24/2019] [Indexed: 01/29/2023] Open
Abstract
In the advanced stages of cancers like melanoma, some of the malignant cells leave the primary tumor and infiltrate the neighboring lymph nodes (LNs). The interaction between secondary cancer and the immune response in the lymph node represents a complex process that needs to be fully understood in order to develop more effective immunotherapeutic strategies. In this process, antigen-presenting cells (APCs) approach the tumor and initiate the adaptive immune response for the corresponding antigen. They stimulate the naive CD4+ and CD8+ T lymphocytes which subsequently generate a population of helper and effector cells. On one hand, immune cells can eliminate tumor cells using cell-cell contact and by secreting apoptosis inducing cytokines. They are also able to induce their dormancy. On the other hand, the tumor cells are able to escape the immune surveillance using their immunosuppressive abilities. To study the interplay between tumor progression and the immune response, we develop two new models describing the interaction between cancer and immune cells in the lymph node. The first model consists of partial differential equations (PDEs) describing the populations of the different types of cells. The second one is a hybrid discrete-continuous model integrating the mechanical and biochemical mechanisms that define the tumor-immune interplay in the lymph node. We use the continuous model to determine the conditions of the regimes of tumor-immune interaction in the lymph node. While we use the hybrid model to elucidate the mechanisms that contribute to the development of each regime at the cellular and tissue levels. We study the dynamics of tumor growth in the absence of immune cells. Then, we consider the immune response and we quantify the effects of immunosuppression and local EGF concentration on the fate of the tumor. Numerical simulations of the two models show the existence of three possible outcomes of the tumor-immune interactions in the lymph node that coincide with the main phases of the immunoediting process: tumor elimination, equilibrium, and tumor evasion. Both models predict that the administration of EGF can promote the elimination of the secondary tumor by PD-1/PD-L1 blockade.
Collapse
Affiliation(s)
| | - Anass Bouchnita
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vitaly Volpert
- Institut Camille Jordan, Université Lyon 1, Villeurbanne, France
- INRIA Team Dracula, INRIA Lyon La Doua, Villeurbanne, France
- Peoples Friendship University of Russia (RUDN University), Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the RAS, Moscow, Russia
| | - Abdelkader Makhoute
- Faculté des Sciences, Université Moulay Ismail, Meknes, Morocco
- Faculty of Sciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
13
|
Bouchnita A, Hellander S, Hellander A. A 3D Multiscale Model to Explore the Role of EGFR Overexpression in Tumourigenesis. Bull Math Biol 2019; 81:2323-2344. [PMID: 31016574 PMCID: PMC6612322 DOI: 10.1007/s11538-019-00607-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 04/15/2019] [Indexed: 11/30/2022]
Abstract
The epidermal growth factor receptor (EGFR) signalling cascade is one of the main pathways that regulate the survival and division of mammalian cells. It is also one of the most altered transduction pathways in cancer. Acquired mutations in the EGFR/ERK pathway can cause the overexpression of EGFR on the surface of the cell, while others downregulate the inactivation of switched on intracellular proteins such as Ras and Raf. This upregulates the activity of ERK and promotes cell division. We develop a 3D multiscale model to explore the role of EGFR overexpression on tumour initiation. In this model, cells are described as individual objects that move, interact, divide, proliferate, and die by apoptosis. We use Brownian Dynamics to describe the extracellular and intracellular regulations of cells as well as the spatial and stochastic effects influencing them. The fate of each cell depends on the number of active transcription factors in the nucleus. We use numerical simulations to investigate the individual and combined effects of mutations on the intracellular regulation of individual cells. Next, we show that the distance between active receptors increase the level of EGFR/ERK signalling. We demonstrate the usefulness of the model by quantifying the impact of mutational alterations in the EGFR/ERK pathway on the growth rate of in silico tumours.
Collapse
Affiliation(s)
- Anass Bouchnita
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 75105, Uppsala, Sweden.
| | - Stefan Hellander
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 75105, Uppsala, Sweden
| | - Andreas Hellander
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 75105, Uppsala, Sweden
| |
Collapse
|
14
|
Baral S, Raja R, Sen P, Dixit NM. Towards multiscale modeling of the CD8 + T cell response to viral infections. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 11:e1446. [PMID: 30811096 PMCID: PMC6614031 DOI: 10.1002/wsbm.1446] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 01/23/2019] [Accepted: 01/28/2019] [Indexed: 12/22/2022]
Abstract
The CD8+ T cell response is critical to the control of viral infections. Yet, defining the CD8+ T cell response to viral infections quantitatively has been a challenge. Following antigen recognition, which triggers an intracellular signaling cascade, CD8+ T cells can differentiate into effector cells, which proliferate rapidly and destroy infected cells. When the infection is cleared, they leave behind memory cells for quick recall following a second challenge. If the infection persists, the cells may become exhausted, retaining minimal control of the infection while preventing severe immunopathology. These activation, proliferation and differentiation processes as well as the mounting of the effector response are intrinsically multiscale and collective phenomena. Remarkable experimental advances in the recent years, especially at the single cell level, have enabled a quantitative characterization of several underlying processes. Simultaneously, sophisticated mathematical models have begun to be constructed that describe these multiscale phenomena, bringing us closer to a comprehensive description of the CD8+ T cell response to viral infections. Here, we review the advances made and summarize the challenges and opportunities ahead. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Cell Fates Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models.
Collapse
Affiliation(s)
- Subhasish Baral
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Rubesh Raja
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Pramita Sen
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India
| | - Narendra M Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.,Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| |
Collapse
|
15
|
Novkovic M, Onder L, Cheng HW, Bocharov G, Ludewig B. Integrative Computational Modeling of the Lymph Node Stromal Cell Landscape. Front Immunol 2018; 9:2428. [PMID: 30405623 PMCID: PMC6206207 DOI: 10.3389/fimmu.2018.02428] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 10/02/2018] [Indexed: 11/13/2022] Open
Abstract
Adaptive immune responses develop in secondary lymphoid organs such as lymph nodes (LNs) in a well-coordinated series of interactions between migrating immune cells and resident stromal cells. Although many processes that occur in LNs are well understood from an immunological point of view, our understanding of the fundamental organization and mechanisms that drive these processes is still incomplete. The aim of systems biology approaches is to unravel the complexity of biological systems and describe emergent properties that arise from interactions between individual constituents of the system. The immune system is greater than the sum of its parts, as is the case with any sufficiently complex system. Here, we review recent work and developments of computational LN models with focus on the structure and organization of the stromal cells. We explore various mathematical studies of intranodal T cell motility and migration, their interactions with the LN-resident stromal cells, and computational models of functional chemokine gradient fields and lymph flow dynamics. Lastly, we discuss briefly the importance of hybrid and multi-scale modeling approaches in immunology and the technical challenges involved.
Collapse
Affiliation(s)
- Mario Novkovic
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Lucas Onder
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Hung-Wei Cheng
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Gennady Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| |
Collapse
|
16
|
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.
Collapse
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
| | | |
Collapse
|
17
|
Bocharov G, Meyerhans A, Bessonov N, Trofimchuk S, Volpert V. Interplay between reaction and diffusion processes in governing the dynamics of virus infections. J Theor Biol 2018; 457:221-236. [PMID: 30170043 DOI: 10.1016/j.jtbi.2018.08.036] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 08/22/2018] [Accepted: 08/28/2018] [Indexed: 02/02/2023]
Abstract
Spreading of viral infection in the tissues such as lymph nodes or spleen depends on virus multiplication in the host cells, their transport and on the immune response. Reaction-diffusion systems of equations with delays in cell proliferation and death by apoptosis represent an appropriate model to study this process. The properties of the cells of the immune system and the initial viral load determine the spatiotemporal regimes of infection spreading. Infection can be completely eliminated or it can persist at some level together with a certain chronic immune response in a spatially uniform or oscillatory mode. Finally, the immune cells can be completely exhausted leading to a high viral load persistence in the tissue. It has been found experimentally, that virus proteins can affect the immune cell migration. Our study shows that both the motility of immune cells and the virus infection spreading represented by the diffusion rate coefficients are relevant control parameters determining the fate of virus-host interaction.
Collapse
Affiliation(s)
- G Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences Gubkina Street 8, 119333 Moscow, Russian Federation; Peoples Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation; Gamaleya Center of Epidemiology and Microbiology, Moscow, Russian Federation.
| | - A Meyerhans
- Infection Biology Laboratory, Department of Experimental and Health Sciences Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Pg. Llus Companys 23, 08010 Barcelona, Spain
| | - N Bessonov
- Institute of Problems of Mechanical Engineering, Russian Academy of Sciences 199178 Saint Petersburg, Russia
| | - S Trofimchuk
- Instituto de Matematica y Fisica, Universidad de Talca, Casilla 747, Talca, Chile
| | - V Volpert
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, 69622 Villeurbanne, France; INRIA, Université de Lyon, Université Lyon 1, Institut Camille Jordan 43 Bd. du 11 Novembre 1918, 69200 Villeurbanne Cedex, France; Poncelet Center, UMI 2615 CNRS, 11 Bolshoy Vlasyevskiy, 119002 Moscow, Russian Federation; Peoples Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation
| |
Collapse
|