Abstract
Agent-based modeling is an object-oriented, discrete event, population-focused method for the computational representation of dynamic systems. Agent-based models (ABMs) treat systems as aggregates of populations of interacting components governed by rules. This means of system representation allows ABMs to map well to how biological knowledge is represented and communicated. As a result, agent-based modeling is an intuitive means by which biomedical researchers can represent their knowledge in a dynamic computational form and in so doing can lower the threshold for the general biological researcher to engage in computational modeling. ABMs are particularly suited for representing the behavior of populations of cells (i.e., “cell-as-agents”) but have also been used to model lower level processes, such as molecular interactions when spatial and structural properties are involved, as well as higher level systems, such as in human populations in epidemiological studies. For purposes of its use in translational systems biology, we focus on the use of cell/tissue-as-agent ABMs and demonstrate how agent-based modeling can serve as an integrating framework for dynamic knowledge representation of biological systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
An G (2010) Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2(41):41ps34
An G et al (2009) Agent-based models in translational systems biology. Wiley Interdiscip Rev Syst Biol Med 1(2):159–171
Bankes SC (2002) Agent-based modeling: a revolution? Proc Natl Acad Sci U S A 99(Suppl 3):7199–7200
Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci USA 99(Suppl 3):7280–7287
Hunt CA et al (2009) At the biological modeling and simulation frontier. Pharm Res 26(11):2369–2400
Walker DC, Southgate J (2009) The virtual cell – a candidate co-ordinator for ‘middle-out’ modeling of biological systems. Brief Bioinform 10(4):450–461
Zhang L, Athale CA, Deisboeck TS (2007) Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J Theor Biol 244(1):96–107
Santoni D, Pedicini M, Castiglione F (2008) Implementation of a regulatory gene network to simulate the TH1/2 differentiation in an agent-based model of hypersensitivity reactions. Bioinformatics 24(11):1374–1380
Fallahi-Sichani M et al (2011) Multiscale computational modeling reveals a critical role for TNF-alpha receptor 1 dynamics in tuberculosis granuloma formation. J Immunol 186(6):3472–3483
An G (2009) Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models. Methods Mol Biol 500:445–468
An G (2006) Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. J Crit Care 21(1):105–110, discussion 110–111
An G (2008) Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 5(1):11
Kirschner DE et al (2007) Toward a multiscale model of antigen presentation in immunity. Immunol Rev 216:93–118
Christley S, Alber MS, Newman SA (2007) Patterns of mesenchymal condensation in a multiscale, discrete stochastic model. PLoS Comput Biol 3(4):e76
Gardner M (1970) Mathematical games: the fantastic combinations of John Conway’s new solitare game of “life”. Sci Am 223:120–123
Kauffman S, Weinberger E (1989) The N-k Model of the application to the maturation of the immune response. J Theor Biol 141(2):211–245
Graner F, Glazier J (1992) Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys Rev Lett 69(13):2013–2016
Engelberg JA, Ropella GE, Hunt CA (2008) Essential operating principles for tumor spheroid growth. BMC Syst Biol 2(1):110
Hunt CA et al (2006) Physiologically based synthetic models of hepatic disposition. J Pharmacokinet Pharmacodyn 33(6):737–772
Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model computer graphics. In: SIGGRAPH ‘87
Lipniacki T et al (2006) Stochastic regulation in early immune response. Biophys J 90(3):725–742
Lipniacki T et al (2006) Transcriptional stochasticity in gene expression. J Theor Biol 238(2):348–367
Vodovotz Y et al (2007) Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J Crit Care 22(1):77–84
Grimm V et al (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987–991
An G (2009) A model of TLR4 signaling and tolerance using a qualitative, particle-event-based method: introduction of spatially configured stochastic reaction chambers (SCSRC). Math Biosci 217(1):43–52
An G (2001) Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. Shock 16(4):266–273
An G (2004) In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Crit Care Med 32(10):2050–2060
Mansury Y, Diggory M, Deisboeck TS (2006) Evolutionary game theory in an agent-based brain tumor model: exploring the ‘Genotype-Phenotype’ link. J Theor Biol 238(1):146–156
Deisboeck TS et al (2001) Pattern of self-organization in tumour systems: complex growth dynamics in a novel brain tumour spheroid model. Cell Prolif 34(2):115–134
Chen S, Ganguli S, Hunt CA (2004) An agent-based computational approach for representing aspects of in vitro multi-cellular tumor spheroid growth. Conf Proc IEEE Eng Med Biol Soc 1:691–694
Thorne BC et al (2006) Modeling blood vessel growth and leukocyte extravasation in ischemic injury: an integrated agent-based and finite element analysis approach. J Crit Care 21(4):346
Tang J, Ley KF, Hunt CA (2007) Dynamics of in silico leukocyte rolling, activation, and adhesion. BMC Syst Biol 1:14
Tang J et al (2004) Simulating leukocyte-venule interactions – a novel agent-oriented approach. Conf Proc IEEE Eng Med Biol Soc 7:4978–4981
Bailey AM, Thorne BC, Peirce SM (2007) Multi-cell agent-based simulation of the microvasculature to study the dynamics of circulating inflammatory cell trafficking. Ann Biomed Eng 35(6):916–936
Bailey AM et al (2009) Agent-based model of therapeutic adipose-derived stromal cell trafficking during ischemia predicts ability to roll on P-selectin. PLoS Comput Biol 5(2):e1000294
Jeong E et al (2007) Cell system ontology: representation for modeling, visualizing and simulating biological pathways. In Silico Biol 7(6):623–638
Walker DC et al (2004) Agent-based computational modeling of wounded epithelial cell monolayers. IEEE Trans Nanobiosci 3(3):153–163
Adra S et al (2010) Development of a three dimensional multiscale computational model of the human epidermis. PLoS One 5(1):e8511
Broderick G et al (2005) A life-like virtual cell membrane using discrete automata. In Silico Biol 5(2):163–178
Pogson M et al (2008) Introducing spatial information into predictive NF-kappaB modelling – an agent-based approach. PLoS One 3(6):e2367
Pogson M et al (2006) Formal agent-based modelling of intracellular chemical interactions. Biosystems 85(1):37–45
Ridgway D et al (2008) Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys J 94(10):3748–3759
Troisi A, Wong V, Ratner MA (2005) An agent-based approach for modeling molecular self-organization. Proc Natl Acad Sci USA 102(2):255–260
Dong X et al (2010) Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS One 5(2):e9249
Auchincloss AH, Diez Roux AV (2008) A new tool of epidemiology. The usefulness of dynamic-agent models in understanding place effects on health. Am J Epidemiol 168(1):1–8
Hoehme S, Drasdo D (2010) A cell-based simulation software for multi-cellular systems. Bioinformatics 26(20):2641–2642
An G, Christley S (2011) Agent-based modeling and biomedical ontologies: a roadmap. Wiley Interdiscip Rev Comput Stat 3(4):343–356
Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623
Vodovotz Y et al (2009) Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 217(1):1–10
Deitch EA (2010) Gut lymph and lymphatics: a source of factors leading to organ injury and dysfunction. Ann N Y Acad Sci 1207(Suppl 1):E103–E111
Christley S, An G (2011) A proposed method for dynamic knowledge representation via agent-directed composition from biomedical and simulation ontologies: an example using gut mucus layer dynamics. In: 2011 Spring simulation multiconference/agent-directed simulation symposium, Boston, MA
Uschold M, Gruninger M (2009) Ontologies: principles, methods and applications. Knowl Eng Rev 11:93–136
Noy NF et al (2009) BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 1(37):170–173
Rubin DL et al (2006) National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge. OMICS 10(2):185–198
Jeong E, Nagasaki M, Miyano S (2008) Rule-based reasoning for system dynamics in cell systems. Genome Inform 20:25–36
Takai-Igarashi T (2005) Ontology based standardization of Petri net modeling for signaling pathways. In Silico Biol 5(5–6):529–536
Shegogue D, Zheng WJ (2005) Integration of the gene ontology into an object-oriented architecture. BMC Bioinformatics 6:113
Ruebenacker O et al (2007) Kinetic modeling using BioPAX ontology. In: Proceedings of IEEE international conference on bioinformatics and biomedicine 2007, pp 339–348
Lister AL et al (2010) Annotation of SBML models through rule-based semantic integration. J Biomed Semantics 1(Suppl 1):S3
Colasanti R, An G (2009) The abstracted biological computational unit (ABCU): introduction of a recursive descriptor for multi-scale computational modeling of biologica systems. J Crit Care 24:e35–e36
Benjamin P, Patki M, Mayer R (2006) Using ontologies for simulation modeling. In: Proceedings of the 2006 Winter simulation conference, pp 1151–1159
Petty MD, Weisel EW (2003) A composability lexicon. In: Proceedings of the 2003 Spring simulation conference, pp 181–187
Yilmaz L (2007) A strategy for improving dynamic composability: ontology-driven introspective agent architectures. J Syst Cybern Inf 5(5):1–9
Alonso-Calvo R et al (2007) An agent- and ontology-based system for integrating public gene, protein and disease databases. J Biomed Inform 40(1):17–29
Bartocci E et al (2007) An agent-based multilayer architecture for bioinformatics grids. IEEE Trans Nanobiosci 6(2):142–148
Merelli E et al (2006) Agents in bioinformatics, computational and systems biology. Brief Bioinform 8(1):45–59
Keele JW, Wray JE (2005) Software agents in molecular computational biology. Brief Bioinform 6(4):370–379
Karasavvas KA, Baldock R, Burger A (2004) Bioinformatics integration and agent technology. J Biomed Inform 37(3):205–219
Grimm V et al (2010) The ODD protocol. A review and first update. Ecol Model 221(23):2760–2768
Hinkelmann F et al (2011) A mathematical framework for agent based models of complex biological networks. Bull Math Biol 73(7):1583–1602
Segovia-Juarez JL, Ganguli S, Kirschner D (2004) Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. J Theor Biol 231(3):357–376
Richards RS et al (2008) Data-parallel techniques for agent-based tissue modeling on graphical processing units. In: Design engineering technical conference and computers and information in engineering conference, New York City, NY
Richmond P et al (2010) High performance cellular level agent-based simulation with FLAME for the GPU. Brief Bioinform 11(3):334–347
Christley S et al (2010) Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms. BMC Syst Biol 4:107
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Christley, S., An, G. (2013). Agent-Based Modeling in Translational Systems Biology. In: Vodovotz, Y., An, G. (eds) Complex Systems and Computational Biology Approaches to Acute Inflammation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8008-2_3
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8008-2_3
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8007-5
Online ISBN: 978-1-4614-8008-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)