期刊论文详细信息
BMC Systems Biology
The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models
Paolo Vicini4  Bernhard O Palsson3  Mary E Spilker4  Peter J O’Brien4  Karin Jooss1  Michael Zager4  Erica L Bradshaw-Pierce2  Timothy P Hickling5  Sirus Palsson3 
[1] Vaccines Research Unit, Pfizer Worldwide Research and Development, San Diego, CA, USA;Current address: Department of Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Colorado Anschutz Medical Campus, Aurora, CO, USA;GT Life Sciences, San Diego, CA, USA;Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA, USA;Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Sandwich, UK
关键词: Systems biology;    Ordinary differential equation systems;    Mathematical modeling;    Immune response;    Biological networks;   
Others  :  1142194
DOI  :  10.1186/1752-0509-7-95
 received in 2012-11-15, accepted in 2013-08-21,  发布年份 2013
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【 摘 要 】

Background

The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.

Results

A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells.

Conclusions

The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

【 授权许可】

   
2013 Palsson et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Anderson ARA, Quaranta V: Integrative mathematical oncology. Nat Rev Cancer 2008, 8(3):227-234.
  • [2]Bauer AL, Beauchemin CAA, Perelson AS: Agent-based modeling of host-pathogen systems: The successes and challenges. Inform Sci 2009, 179(10):1379-1389.
  • [3]Mestas J, Hughes CCW: Of Mice and Not Men: Differences between Mouse and Human Immunology. J Immunol 2004, 172(5):2731-2738.
  • [4]Perelson AS, Weisbuch G: Immunology for physicists. Rev Mod Phys 1997, 69(4):1219-1268.
  • [5]De Boer RJ, et al.: Macrophage T lymphocyte interactions in the anti-tumor immune response: a mathematical model. J Immunol 1985, 134(4):2748-2758.
  • [6]De Pillis LG, Radunskaya AE, Wiseman CL: A validated mathematical model of cell-mediated immune response to tumor growth. Cancer Res 2005, 65(17):7950-7958.
  • [7]Kirschner D, Panetta JC: Modeling immunotherapy of the tumor–immune interaction. J Math Biol 1998, 37(3):235-252.
  • [8]Bell GI: Mathematical model of clonal selection and antibody production. J Theor Biol 1970, 29(2):191-232.
  • [9]Kolev M: Mathematical modeling of the competition between acquired immunity and cancer. Int J Appl Math Comput Sci 2003, 13(3):289-296.
  • [10]Marino S, Kirschner DE: The human immune response to Mycobacterium tuberculosis in lung and lymph node. J Theor Biol 2004, 227(4):463-486.
  • [11]Burke MA, et al.: Modeling the Proliferative Response of T Cells to IL-2 and IL-4. Cell Immunol 1997, 178(1):42-52.
  • [12]Cheng Y, et al.: A discrete computer model of the immune system reveals competitive interactions between the humoral and cellular branch and between cross-reacting memory and naïve responses. Vaccine 2009, 27(6):833-845.
  • [13]Celada F, Seiden PE: A computer model of cellular interactions in the immune system. Immunol Today 1992, 13(2):56-62.
  • [14]Castiglione F, et al.: A Network of Cellular Automata for the Simulation of the Immune System. INTERNATIONAL JOURNAL OF MODERN PHYSICS C 1999, 10:677-686.
  • [15]Puzone R, et al.: IMMSIM, a flexible model for in machina experiments on immune system responses. Futur Gener Comput Syst 2002, 18(7):961-972.
  • [16]Kohler B, et al.: A systematic approach to vaccine complexity using an automaton model of the cellular and humoral immune system: I. Viral characteristics and polarized responses. Vaccine 2000, 19(7–8):862-876.
  • [17]Cappuccio A, Castiglione F, Piccoli B: Determination of the optimal therapeutic protocols in cancer immunotherapy. Math Biosci 2007, 209(1):1-13.
  • [18]Castiglione F, Piccoli B: Cancer immunotherapy, mathematical modeling and optimal control. J Theor Biol 2007, 247(4):723-732.
  • [19]Kirschner D, Marino S: Mycobacterium tuberculosis as viewed through a computer. Trends Microbiol 2005, 13(5):206-211.
  • [20]Kirschner DE, et al.: Toward a multiscale model of antigen presentation in immunity. Immunol Rev 2007, 216(1):93.
  • [21]Kim PS, Lee PP, Levy D: Modeling regulation mechanisms in the immune system. J Theor Biol 2007, 246(1):33-69.
  • [22]Agoram BM, Martin SW, van der Graaf PH: The role of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational research of biologics. Drug Discov Today 2007, 12(23–24):1018-1024.
  • [23]Mager DE, Jusko WJ: Development of Translational Pharmacokinetic-Pharmacodynamic Models. Clin Pharmacol Ther 2008, 83(6):909-912.
  • [24]Melder RJ, et al.: Systemic distribution and tumor localization of adoptively transferred lymphocytes in mice: comparison with physiologically based pharmacokinetic model. Neoplasia 2002, 4(1):3-8.
  • [25]Garg A, Balthasar J: Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn 2007, 34(5):687-709.
  • [26]Mager DE, Wyska E, Jusko WJ: Diversity of Mechanism-Based Pharmacodynamic Models. Drug Metab Dispos 2003, 31(5):510-518.
  • [27]Vicini P: Multiscale Modeling in Drug Discovery and Development: Future Opportunities and Present Challenges. Clin Pharmacol Ther 2010, 88(1):126-129.
  • [28]Sorger PK, et al.: Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms. 2011. [An NIH White Paper by the QSP Workshop Group] http://www.nigms.nih.gov/nr/rdonlyres/8ecb1f7c-be3b-431f-89e6-a43411811ab1/0/systemspharmawpsorger2011.pdf webcite
  • [29]Webb K, White T: UML as a cell and biochemistry modeling language. Biosystems 2005, 80(3):283-302.
  • [30]Ayyadurai V, Dewey C: CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models. Cell Mol Bioeng 2011, 4(1):28-45.
  • [31]Palsson B: Systems biology: properties of reconstructed networks. Cambridge: Cambridge Univ Pr; 2006.
  • [32]Jamshidi N, Palsson BØ: Mass Action Stoichiometric Simulation Models: Incorporating Kinetics and Regulation into Stoichiometric Models. Biophys J 2010, 98(2):175-185.
  • [33]Weinberg RA: The biology of cancer. New York: Garland Science; 2007.
  • [34]Colombo MP, Piconese S: Regulatory-T-cell inhibition versus depletion: the right choice in cancer immunotherapy. Nat Rev Cancer 2007, 7(11):880-887.
  • [35]Park J, et al.: Natural immunosurveillance against spontaneous, autochthonous breast cancers revealed and enhanced by blockade of IL-13-mediated negative regulation. Cancer Immunol Immunother 2008, 57(6):907-912.
  • [36]Edwards JS, Palsson BO: Systems Properties of the Haemophilus influenzaeRd Metabolic Genotype. J Biol Chem 1999, 274(25):17410-17416.
  • [37]Feist AM, Palsson BO: The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotech 2008, 26(6):659-667.
  • [38]Duarte NC, et al.: Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci 2007, 104(6):1777-1782.
  • [39]Gianchandani EP, et al.: Functional States of the Genome-Scale Escherichia Coli Transcriptional Regulatory System. PLoS Comput Biol 2009, 5(6):e1000403.
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