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Agent-Based Models in Biology and Medicine

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This page addresses applications of agent-based modeling in biology and medicine, divided into several subcategories. Please be aware of the separate page for ecology and of the extensive literature in Artificial Life at http://www.alife.org.

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Contents

Theoretical biology

Evo

Evo is a software development framework that allows developers to build complex alife simulations. Using Evo, researchers can easily build systems of independent agents interacting with one another and with their environment. Evo implements biological operators such as genetic recombination and mutation to evolve the behavior of agents so that they are more adapted to their environment.

SCL v0.05.11 [Updated to Swarm 2.1]

Barry McMullin is studying artificial chemistries and the origins of life. He has two papers available at SFI and many more accessible via his home page. The first one, Computational Autopoiesis: The Original Algorithm, is about the particular artificial chemistry that inspired him to develop the SCL model. The second one, SCL: An Artificial Chemistry in Swarm, is about the SCL model. (The file scl-data00.tar.gz contains the data for bmcm-ecal97.)

Organism, organ, and cell biology

FURM

Functional Unit Representation Method - FURM is a hybrid modeling method that provides a framework for studying models (representations) of biological functional units. It provides infrastructure for co-simulation, model comparison, and V&V. In the future, it will also provide infrastructure for automated evolution of models and the operation of search algorithms over loosely-defined model spaces. It is being developed by the Hunt Laboratory at the University of California, San Francisco.
-- gepr 15:27 24 Jul 2003 (EDT)

Medicine

Agent-based modeling applications in Medicine (General Articles)

  • 2006-4-23: "In Pixels and In Health: Computer modeling pushes the threshold of medical research" by Niala Moreira. Science News, January 21, 2006 (Vol. 169, No 3, Pages 40-41, 44).
This article provides a lay overview of several ABM approaches to medical problems, featuring comments by John Holland, and examples including Denise Kirschner's work on tuberculosis, Gary An's work on acute inflammation and Thomas Diesboeck's work on tumor growth. The Science News site is, unfortunately, password/subscription protected.--Gary An 11:08, 23 Apr 2006 (EDT)
  • 2007-6-1: "From molecules to insect communities - how formal agent based computational modelling is uncovering new biological facts." Coakley S, Smallwood R and Holcombe M (2006) Scientiae Mathematicae Japonicae 64:185-198
  • 2007-7-16: "Combining experiments with multi-cell agent-absed modeling to study biological tissue patterning." Thorne BC, Bailey AM and Peirce SM. Briefings in Bioinformatics. 2007, June 21, Epub ahead of print.
This is a nice survey of multi-cellular ABM approaches to biomedical issues up to this point. It also includes summary statements regarding the design, use and validation/verification issues in the biomedical arena.
  • 2009-1-31: "Mechanistic simulations of inflammation: Current state and future prospects." Vodovotz Y, Constantine G, Rubin J, Csete M, Voit EO, An G. Math Biosci. 2009 Jan;217(1):1-10. Epub 2008 Aug 26.
This is a relatively recent review looking at a whole suite of computational methods for modeling inflammation, including agent-based approaches.
  • 2009-7-26: "Computational disease modeling - fact or fiction?" Tegnér JN, Compte A, Auffray C, An G, Cedersund G, Clermont G, Gutkin B, Oltvai ZN, Stephan KE, Thomas R, Villoslada P.

BMC Syst Biol. 2009 Jun 4;3:56.

  • 2009-7-26: "Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models." An G. Methods Mol Biol. 2009;500:445-68.
This is a "how-to" introductory chapter on the use of agent based modeling in the biomedical arena.
This review paper discusses the differences between synthetic models (such as ABMs) and inductive mathematical models. The supplementary also includes a relatively comprehensive bibliography of synthetic and inductive biomedical models.


Agent-based modeling of Acute Inflammation

  • 2006-6-26: "Agent based Computer Simulation and SIRS: Building a Bridge between Basic Research and Clinical Trials", An G. Shock 2001: 16(4): 266-273.
This is the first paper in the critical care literature describing the use of agent based modeling.
  • 2004-11-07: "In-Silico Experiments of Existing and Hypothetical Cytokine-Directed Clinical Trials using Agent Based Modeling", An G. Critical Care Medicine 2004; 32(10):2050-2060.
This paper uses a StarlogoT model (available on the StarlogoT Community Model website) of the Innate Inflammatory response to simulate a series of mediator directed pharmacological clinical trials. The paper is intended primarily as an introduction to the critical care medical community to agent based models and their potential uses.
  • 2006-4-22: "Mathematical models of the acute inflammatory response." Vodovotz Y, Clermont G, Chow C, An G. Cur Opin Crit Care 2004; 10:383-390.
This paper is a review of ODE and ABM approaches to modeling the acute inflammatory response.
  • 2006-4-22: "Concepts for developing a collaborative in-silico model of the acute inflammatory response using agent based modeling." An G. J Crit Care 2006; 21(1): 105-110.
This paper is an introduction to the idea of creating a syntactical grammar that can be potentially used to bind together different and disparate wet lab experiments into a collaborative, community-wide series of models that could be a functional representation of the state of knowledge on inflammation. It is, admittedly, an extremely preliminary communication. --Gary An 20:21, 22 Apr 2006 (EDT)
  • 2009-1-31: "Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation." An G. Theor Biol Med Model. 2008 May 27;5:11.
This paper presents a progressive multi-scale approach to modeling biological systems tied to the differenct levels of biological research: in-vitro, in-vivo, organ physiology and clinical behavior. As an example, this paper presents the effect of inflammation on epithelial barrier function in the gut and lung. --Gary An
This paper introduces the In Silico White Blood Cell model (ISWBC), a multi-scale model of leukocyte rolling, activation, and adhesion during inflammation that was constructed using RePast J.
This paper demonstrates the use of the ISWBC model to test specific hypothesized mechanisms and events thought to mediate leukocyte adhesion in both an ex vivo and in vivo mouse model of inflammation.


Agent-based modeling of Immunology

  • 2009-1-31: "The Basic Immune Simulator: an agent-based model to study the interactions between innate and adaptive immunity." Folcik VA, An, GC and Orosz CG. Theor Biol Med Model. 2007 Sept 27:4:39.
This paper presents a Repast implementation of an ABM of the multiple levels of the immune response to viral infection. The BIS is designed to be an entry-point for investigators interested in applying agent-based modeling to immunology research.
  • 2009-1-31: "The Multiscale Systems Immunology project: software for cell-based immunological simulation." Mitha F, Lucas TA, Feng F, Kepler TB, Chan C. Source Code Biol Med. 2008 Apr 28;3:6.
  • 2009-1-31: "Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling." Chavali AK, Gianchandani EP, Tung KS, Lawrence MB, Peirce SM, and Papin JA. Trends Immunol. 2008 Dec;29(12):589-99. Epub 2008 Oct 27


Agent Based Modeling of Cancer and Tumor Biology

  • 2006-6-26: ""Emerging Patterns in Tumor Systems: Simulating the Dynamics of Multicellular clusters with an agent-based spatial agglomeration model", Mansury Y, Kimura M, Lobo J and Deisboeck T. Journal of Theoretical Biology 2002; 219:343-70.
This paper outlines the construction and initial utilization of an agent based model of tumor growth. Dr. Deisboeck and colleagues are in the process of producing a series of follow up papers that expand upon this model. Those interested should do a Pubmed search for Dr. Deisboeck to see his progress. Three more recent publications are below:
  • 2009-1-31: "Simulating non-small cell lung cancer with a multiscale agent-based model." Wang Z, Zhang L, Sagotsky J, Deisboeck TS. Theor Biol Med Model. 2007 Dec 21;4:50.
  • 2009-1-31: "Multiscale agent-based cancer modeling." Zhang L, Wang Z, Sagotsky JA, Deisboeck TS. J Math Biol. 2009 Apr;58(4-5):545-59. Epub 2008 Sep 12.
  • 2009-1-31: "Cancer cell motility: Optimizing spatial search strategies." Chen LL, Zhang L, Yoon J, Deisboeck TS. Biosystems. 2008 Nov 14. [Epub ahead of print]
  • 2009-7-26: "Cross-Scale, Cross-Pathway Evaluation using an Agent-Based Non-Small Cell Lung Cancer Model." Wang Z, Birch CM, Sagotsky J, Deisboeck TS. Bioinformatics. 2009 Jul 6. [Epub ahead of print]
  • 2010-3-18: "A computational approach to resolve cell level contributions to early glandular epithelial cancer progression." Kim SH, Debnath J, Mostov K, Park S, Hunt CA. BMC Syst Biol 2009 Dec 31;3:122. (http://www.biomedcentral.com/1752-0509/3/122)
  • 2011-4-1: "Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling" Tang J, Enderling H, Becker-Weimann S, Pham C, Polyzos A, Chen CY, Costes SV. Integr Biol 2011 Mar 4; 3(4):408-421. (http://www.ncbi.nlm.nih.gov/pubmed/21373705)


Agent Based Modeling of Wound Healing

  • 2007-6-1: "The Epitheliome: modelling the social behaviour of cells." Walker D C, Southgate J S, Hill G, Holcombe M, Hose D R, Wood S M, MacNeil S and Smallwood R H (2004) BioSystems 76 89-100.
This paper is an introduction to the Epitheliome Project (see below) from the University of Sheffield.
  • 2007-6-1: "Modeling the Effect of Exogenous Calcium on Keratinocyte and HaCat Cell Proliferation and Differentiation Using an Agent-Based Computational Paradigm." Walker D, Sun T, Macneil S, Smallwood R (2006) Tissue Eng. 12(8):2301-2309
  • 2007-6-1: "Agent-based computational modelling of wounded epithelial cell monolayers." Walker D C, Hill G, Wood S M, Smallwood R H and Southgate J (2004) IEEE Trans Nanobioscience 3 153-163
  • 2009-1-31: "Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-beta1." Mi Q, Rivière B, Clermont G, Steed DL, Vodovotz Y. Wound Repair Regen. 2007 Sep-Oct;15(5):671-82.
  • 2009-1-31: "A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury." Li NY, Verdolini K, Clermont G, Mi Q, Rubinstein EN, Hebda PA, Vodovotz Y. PLoS ONE. 2008 Jul 30;3(7):e2789.
  • 2009-7-26: "Modeling Multi-Cellular Behavior in Epidermal Tissue Homeostasis via Finite State Machines in Multi-Agent Systems." Sütterlin T, Huber S, Dickhaus H, Grabe N. Bioinformatics. 2009 Jun 17. [Epub ahead of print]


Agent Based Modeling of Vascular Biology

  • 2006-6-26: "Multicellular simulation predicts microvascular patterning and in silico tissue assembly", Peirce SM, Van Gieson EJ, Skalak TC. FASEB J. 2004 Apr;18(6):731-3.
This paper uses a Starlogo model to simulate the effect of growth factors on angiogenesis. It is a good example of the use of the spatial characteristics of ABM in the validation process.
  • 2007-7-16: "Multi-cell agent-based simulation of the microvasculature to study the dynamics of circulating inflammatory cell trafficking. Bailey AM, Thorne BC and Peirce SM. Annals of Biomedical Engineering 2007; 35:916-36.
  • 2009-7-26: "Agent-based model of therapeutic adipose-derived stromal cell trafficking during ischemia predicts ability to roll on P-selectin." Bailey AM, Lawrence MB, Shang H, Katz AJ, Peirce SM. PLoS Comput Biol. 2009 Feb;5(2):e1000294. Epub 2009 Feb 27.


Agent Based Modeling of Intracellular Signalling and Metabolic Processes

  • 2007-6-1: "Formal agent-based modelling of intracellular chemical interactions." Pogson M, Smallwood R, Qwarnstrom E, Holcombe M (2006) Biosystems. 85(1):37-45
  • 2007-6-1: "An integrated agent-mathematical model of the effect of intercellular signalling via the epidermal growth factor receptor on cell proliferation." Walker D, Wood S, Southgate J, Holcombe M, Smallwood R (2006) J Theor Biol. 242:774-789
  • 2007-7-16: "A life-like virtual cell membrane using discrete automata." Broderick G, Ru'aini M, Chan E and Ellison MJ. In Silico Biology 2005;5(2):163-178.
This paper presents the formation of cell membrane structures based on relatively simple interaction rules drawn from classical flocking models. This project is related to the ongoing CyberCell project (http://129.128.166.250/Main_New.html)
  • 2007-7-16: "AgentCell (Digital E. Coli)" Emonet T, Wickersham C, Cluzel P (Institute of Biophysical Dynamics, University of Chicago), Macal C, North M (Center for Complex Adaptive Agent Systems Simulation, Argonne National Laboratory), Gallagher B (ASC Flaxh Center Visualization Group, University of Chicago). Description available at http://129.128.166.250/Main_New.html
This paper presents a virtual E. Coli, and has a site for download of the simulation package at http://sourceforge.net/projects/agentcell. The simulation work on this project was carried out by the folks who bring you RePast.
  • 2009-1-31: "A model of TLR4 signaling and tolerance using a qualitative, particle-event-based method: Introduction of spatially configured stochastic reaction chambers (SCSRC)." An G. Math Biosci. 2009 Jan;217(1):43-52. Epub 2008 Oct 11.
This paper presents an agent-based method of abstractly representing intracellular signaling pathway behavior from a particle-event standpoint.
  • 2009-7-26: "Biophysically realistic filament bending dynamics in agent-based biological simulation." Alberts JB. PLoS One. 2009;4(3):e4748. Epub 2009 Mar 13.


Agent Based Modeling of Infectious Diseases

  • 2009-1-31: "A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health." Auchincloss AH, Diez Roux AV. Am J Epidemiol. 2008 Jul 1;168(1):1-8. Epub 2008 May 13. Review.
  • 2009-1-31: "The virtue of virtuality: the promise of agent-based epidemic modeling." Hupert N, Xiong W, Mushlin A. Transl Res. 2008 Jun;151(6):273-4. Epub 2008 May 12.
  • 2009-1-31: "Virtual epidemic in a virtual city: simulating the spread of influenza in a US metropolitan area." Lee BY, Bedford VL, Roberts MS, Carley KM. Transl Res. 2008 Jun;151(6):275-87. Epub 2008 Apr 3.
  • 2009-1-31: "A hybrid agent-based approach for modeling microbiological systems." Guo Z, Sloot PM, Tay JC. J Theor Biol. 2008 Nov 21;255(2):163-75. Epub 2008 Aug 15.
  • 2009-1-31: "An agent-based model for predicting the prevalence of Trypanosoma cruzi I and II in their host and vector populations." Devillers H, Lobry JR, Menu F. J Theor Biol. 2008 Dec 7;255(3):307-15. Epub 2008 Aug 29.
  • 2009-1-31: "Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission." Roche B, Guégan JF, Bousquet F. BMC Bioinformatics. 2008 Oct 15;9:435.
  • 2009-1-31: "The design and use of an agent-based model to simulate the 1918 influenza epidemic at Norway House, Manitoba." Carpenter C, Sattenspiel L. Am J Hum Biol. 2008 Dec 23. [Epub ahead of print]


Links to Other Resources

  • Link to Society of Complexity in Acute Illness (SCAI). This is an organization of physicians and biomedical researchers committed to promoting the use of mathematical modeling and computational methods to analyze the pathophysiology of acute illnesses with a focus on inflammation. http://www.scai-med.org
  • Link to Modeling Glossary developed and maintained by the Biosystems Research Group at the University of California, San Francisco under the leadership of C. Anthony Hunt (Also see FURM above under "Organism, Organs and Cell Biology"). This Glossary can be used as a reference point by those interested in biomedical modeling as a means of fostering communication between modelers and domain specialists. http://biosystems.ucsf.edu/Researc/dictionary.html . Feedback on the Glossary and any new additions can be emailed to scaimed@gmail.com.
  • Link to Epitheliome Project. http://www.dcs.shef.ac.uk/~rod/Integrative_Biology.html This is a project headed by Rod Smallwood from the Computational Systems Biology group at the University of Sheffield that promotes the use of ABM for biomedical research. The Epitheliome Project focuses on multi-scale modeling of epithelial cells, intracellular signalling and use with X-machines. This is one of the most productive groups utilizing ABM for biomedical research.--Gary An 23:25, 31 May 2007 (EDT)
  • Link to FLexible Agent Modelling Environment (FLAME). http://www.flame.ac.uk/ From the FLAME Introduction Page:"FLAME has been developed to allow a wide range of agent and non-agent models to be brought together within one simulation environment. FLAME provides specifications in the form of a formal framework which can be used by developers to create models, and software tools, that are compatible with one another. New models, adhering to the specifications, may be easily incorporated into existing, or new, simulations with minimum effort. Parallelisation methods and testing techniques, allow the development of large multiprocessor simulations with feedback provided on the functionality of written code." FLAME is being developed primarily at the University of Sheffield.--Gary An 23:25, 31 May 2007 (EDT)

Epidemiology

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