Agent-Based Modeling Approaches to Multi-Scale Systems Biology: An Example Agent-Based Model of Acute Pulmonary Inflammation

Abstract

Implicit in systems biology is the concept that the whole is greater than the sum of its parts. Agent-based modeling, an object-oriented, discrete event, population-based computational modeling method, is well suited to meeting this goal. By viewing systems as aggregates of populations of interacting components, agent-based models (ABMs) map well to biological conceptual models and present an intuitive means by which biomedical researchers can represent their knowledge in a dynamic computational form. ABMs are particularly suited for representing the behaviour of populations of cells (i.e. “cell-as-agents”), but ABMs have also been used to model molecular interactions, particularly when spatial and structural properties are involved. Presented herein are a series of ABMs of biomedical systems that cross multiple scales of biological organization, as well as a detailed description of an example ABM of acute pulmonary inflammation. Because of these characteristics agent-based modeling is a useful addition to the suite of equation-based mathematical modeling methods found in systems biology, and can serve as an integrating framework for dynamic knowledge representation of biological systems.

Keywords

Inflammation Agent-based modeling Translational systems biology Complex systems analysis 

Abbreviations

ABM

Agent-Based Modeling

ABMF

Agent-Based Modeling Format

AI

Artificial Intelligence

ALI

Acute Lung Injury

APIABM

Acute Pulmonary Injury Agent-Based Model

ARDS

Acute Respiratory Distress Syndrome

CMA

Computational Modeling Assistant

DAMP

Damage-Associated Molecular Products

EINISI

Enteric Immunity Simulator

I-κB

I-kappa-B

NCBO

National Center for Biomedical Ontology

NEC

Necrotizing enterocolitis

NF-κB

Nuclear Factor kappa-B

ODD

Overview, Design and Detail Protocol

ODE

Ordinary differential equation

PMN

Polymorphonuclear neutrophils

TGF-β1

Transforming growth factor-β1

TNF-α

Tumor necrosis factor-α

VILI

Ventilator Induced Lung Injury

References

  1. 1.
    Innovation or stagnation: challenge and opportunity on the critical path to new medical products (2004) [cited 1 May 2008]. Available from: http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html
  2. 2.
    An G (2010) Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2(41): 41ps34Google Scholar
  3. 3.
    An G et al (2009) Agent-based models in translational systems biology. Wiley Interdisc Rev Syst Biol Med. doi:10:1002/wsbm.45Google Scholar
  4. 4.
    Bankes SC (2002) Agent-based modeling: a revolution? Proc Natl Acad Sci U S A 99(3):7199–7200PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci U S A 99(3):7280–7287PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Hunt CA et al (2009) At the biological modeling and simulation frontier. Pharm ResGoogle Scholar
  7. 7.
    Walker DC, Southgate J (2009) The virtual cell: a candidate co-ordinator for ‘middle-out’ modeling of biological systems. Brief Bioinform 10(4):450–461PubMedCrossRefGoogle Scholar
  8. 8.
    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):96–107PubMedCrossRefGoogle Scholar
  9. 9.
    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):1374–1380PubMedCrossRefGoogle Scholar
  10. 10.
    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):3472–3483PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    An G (2009) Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models. Methods Mol Biol 500:445–468PubMedCrossRefGoogle Scholar
  12. 12.
    Hunt CA et al (2006) Physiologically based synthetic models of hepatic disposition. J Pharmacokinet Pharmacodyn 33(6):737–772PubMedCrossRefGoogle Scholar
  13. 13.
    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):11PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Kirschner DE et al (2007) Toward a multiscale model of antigen presentation in immunity. Immunol Rev 216:93–118PubMedGoogle Scholar
  15. 15.
    Christley S, Alber MS, Newman SA (2007) Patterns of mesenchymal condensation in a multiscale, discrete stochastic model. PLoS Comput Biol 3(4):e76PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Engelberg JA, Ropella GE, Hunt CA (2008) Essential operating principles for tumor spheroid growth. BMC Syst Biol 2(1):110PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model. SIGGRAPH 87 Comput GraphGoogle Scholar
  18. 18.
    Lipniacki T et al (2006) Stochastic regulation in early immune response. Biophys J 90(3):725–742PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Lipniacki T et al (2006) Transcriptional stochasticity in gene expression. J Theor Biol 238(2):348–367PubMedCrossRefGoogle Scholar
  20. 20.
    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):77–84PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Grimm V et al (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987–991PubMedCrossRefGoogle Scholar
  22. 22.
    An G, Wilensky U (2009) From artificial life to in silico medicine: netlogo as a means of translational knowledge representation in biomedical research. In: Adamatsky A, Komosinski M (eds) Artificial life in software, vol 2. Springer, London, pp 183–209CrossRefGoogle Scholar
  23. 23.
    An G (2001) Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. Shock 16(4):266–273PubMedCrossRefGoogle Scholar
  24. 24.
    An G (2004) In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Crit Care Med 32(10):2050–2060PubMedCrossRefGoogle Scholar
  25. 25.
    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):146–156PubMedCrossRefGoogle Scholar
  26. 26.
    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):115–134PubMedCrossRefGoogle Scholar
  27. 27.
    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 1:691–694PubMedGoogle Scholar
  28. 28.
    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):346CrossRefGoogle Scholar
  29. 29.
    Tang J, Ley KF, Hunt CA (2007) Dynamics of in silico leukocyte rolling, activation, and adhesion. BMC Syst Biol 1:14PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Tang J et al (2004) Simulating leukocyte-venule interactions: a novel agent-oriented approach. Conf Proc IEEE Eng Med Biol Soc 7:4978–4981PubMedGoogle Scholar
  31. 31.
    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):916–936PubMedCrossRefGoogle Scholar
  32. 32.
    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):e1000294PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Mi Q et al (2007) Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-beta1. Wound Repair Regen 15(5):671–682PubMedCrossRefGoogle Scholar
  34. 34.
    Walker DC et al (2004) Agent-based computational modeling of wounded epithelial cell monolayers. IEEE Trans Nanobioscience 3(3):153–163PubMedCrossRefGoogle Scholar
  35. 35.
    Adra S et al (2010) Development of a three dimensional multi scale computational model of the human epidermis. PLoS ONE 5(1):e8511PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    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):43–52PubMedCrossRefGoogle Scholar
  37. 37.
    Broderick G et al (2005) A life-like virtual cell membrane using discrete automata. In Silico Biol 5(2):163–178PubMedGoogle Scholar
  38. 38.
    Pogson M et al (2008) Introducing spatial information into predictive NF-kappaB modelling: an agent-based approach. PLoS ONE 3(6):e2367PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Pogson M et al (2006) Formal agent-based modelling of intracellular chemical interactions. Biosystems 85(1):37–45PubMedCrossRefGoogle Scholar
  40. 40.
    Ridgway D et al (2008) Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys J 94(10):3748–3759PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Troisi A, Wong V, Ratner MA (2005) An agent-based approach for modeling molecular self-organization. Proc Natl Acad Sci U S A 102(2):255–260PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Dong X et al (2010) Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS ONE 5(2):e9249PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Hoehme S, Drasdo D (2010) A cell-based simulation software for multi-cellular systems. Bioinformatics 26(20):2641–2642PubMedCentralPubMedCrossRefGoogle Scholar
  44. 44.
    An G, Christley S (2011) Agent-based modeling and biomedical ontologies: a roadmap. Wiley Interdisc Rev Comput Stat 3(4):343–356CrossRefGoogle Scholar
  45. 45.
    Solovyev A et al (2011) SPARK: a framework for multi-scale agent-based biomedical modeling. Int J Agent Technol Syst 2(3):18–31CrossRefGoogle Scholar
  46. 46.
    Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623CrossRefGoogle Scholar
  47. 47.
    Vodovotz Y et al (2009) Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 217(1):1–10PubMedCentralPubMedCrossRefGoogle Scholar
  48. 48.
    Nathan C (2002) Points of control in inflammation. Nature 420(6917):846–852PubMedCrossRefGoogle Scholar
  49. 49.
    Schlag G, Redl H (1996) Mediators of injury and inflammation. World J Surg 20(4):406–410PubMedCrossRefGoogle Scholar
  50. 50.
    Matzinger P (2002) The danger model: a renewed sense of self. Science 296(5566):301–305PubMedCrossRefGoogle Scholar
  51. 51.
    Santos CC et al (2005) Bench-to-bedside review: biotrauma and modulation of the innate immune response. Crit Care 9(3):280–286PubMedCentralPubMedCrossRefGoogle Scholar
  52. 52.
    Medzhitov R (2008) Origin and physiological roles of inflammation. Nature 454(7203):428–435PubMedCrossRefGoogle Scholar
  53. 53.
    Oviedo JA, Wolfe MM (2001) Clinical potential of cyclo-oxygenase-2 inhibitors. BioDrugs 15(9):563–572PubMedCrossRefGoogle Scholar
  54. 54.
    Borer JS, Simon LS (2005) Cardiovascular and gastrointestinal effects of COX-2 inhibitors and NSAIDs: achieving a balance. Arthritis Res Ther 7(4):S14–S22PubMedCentralPubMedCrossRefGoogle Scholar
  55. 55.
    Rychly DJ, DiPiro JT (2005) Infections associated with tumor necrosis factor-alpha antagonists. Pharmacotherapy 25(9):1181–1192PubMedCrossRefGoogle Scholar
  56. 56.
    Calabrese L (2006) The yin and yang of tumor necrosis factor inhibitors. Cleve Clin J Med 73(3):251–256PubMedCrossRefGoogle Scholar
  57. 57.
    An GC (2010) Translational systems biology using an agent-based approach for dynamic knowledge representation: an evolutionary paradigm for biomedical research. Wound Repair Regen 18(1):8–12PubMedCrossRefGoogle Scholar
  58. 58.
    Li NY et al (2008) A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS ONE 3(7):e2789PubMedCentralPubMedCrossRefGoogle Scholar
  59. 59.
    Li NY et al (2010) Bio simulation of inflammation and healing in surgically injured vocal folds. Ann Otol Rhinol Laryngol 119(6):412–423PubMedCentralPubMedGoogle Scholar
  60. 60.
    Seal JB et al (2010) The molecular Koch’s postulates and surgical infection: a view forward. Surgery 147(6):757–765PubMedCrossRefGoogle Scholar
  61. 61.
    Wendelsdorf K et al (2011) Enteric immunity simulator: a tool for in silico study of gut immunopathologies. Virginia Bioinformatics Institute, Blacksburg, p 1–27Google Scholar
  62. 62.
    Seal JB et al (2011) 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 8:33PubMedCentralPubMedCrossRefGoogle Scholar
  63. 63.
    Kim M et al (2012) Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model. Surg Infect (Larchmt) 13(1):18–32CrossRefGoogle Scholar
  64. 64.
    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):357–376PubMedCrossRefGoogle Scholar
  65. 65.
    Brown BN et al (2011) An agent-based model of inflammation and fibrosis following particulate exposure in the lung. Math Biosci 231(2):186–196PubMedCentralPubMedCrossRefGoogle Scholar
  66. 66.
    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–E111PubMedCrossRefGoogle Scholar
  67. 67.
    Grimm V et al (2010) The ODD protocol: a review and first update. Ecol Model 221:2760–2768CrossRefGoogle Scholar
  68. 68.
    Wilensky U, Rand W (2009) An introduction to agent-based modeling: modeling natural, social and engineered complex systems with NetLogo. MIT Press, CambridgeGoogle Scholar
  69. 69.
    van oud Alblas AB, van Furth R (1979) Origin, kinetics, and characteristics of pulmonary macrophages in the normal steady state. J Exp Med 149(6):1504–1518CrossRefGoogle Scholar
  70. 70.
    Zeigler B, Praehofer H, Kim TG (2000) Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems, vol 2. Elsevier, Sas Diego, p 510Google Scholar
  71. 71.
    Balci O (2001) A methodology for certification of modeling and simulation applications. ACM Trans Model Comput Simul 11(4):352–377CrossRefGoogle Scholar
  72. 72.
    Hinkelmann F et al (2011) A mathematical framework for agent based models of complex biological networks. Bull Math Biol 73(7):1583–1602PubMedCrossRefGoogle Scholar
  73. 73.
    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 YorkGoogle Scholar
  74. 74.
    Richmond P et al (2010) High performance cellular level agent-based simulation with FLAME for the GPU. Briefings Bioinform 11(3):334–347CrossRefGoogle Scholar
  75. 75.
    Christley S et al (2010) Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms. BMC Syst Biol 4:107PubMedCentralPubMedCrossRefGoogle Scholar
  76. 76.
    Christley S, An G (2011) A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant. Comput Math Organ Theor 17(4):1–24Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of SurgeryUniversity of ChicagoChicagoUSA
  2. 2.Department of SurgeryNorthwestern UniversityChicagoUSA

Personalised recommendations