Topic: Optimal Control and Optimization for Individual-based and Agent-based Models
Meeting dates: December 1-3, 2009.
Filippo Castiglione (Institute for Computing Applications, Rome);
Volker Grimm (UFZ Center for Environmental Research, Leipzig);
Reinhard Laubenbacher (Virginia Bioinformatics Institute);
Suzanne Lenhart (Univ. of Tennessee, Knoxville)
Objectives: Agent-based models are used increasingly to understand a broad range of biological phenomena, including, e.g., tumor growth, the immune system, and the spread of infectious diseases across social networks. In all these cases it would be very useful to have analytic methods available to study in general how possible interventions would affect system dynamics. The advantage of agent-based models is that they integrate local relationships to capture global emergent dynamics, without needing global parameters as input. The disadvantage of this type of model is that very few mathematical analysis methods are available to produce general descriptions of model response particularly in terms of spatio-temporal patterns arising from even fairly simple ABMs. In particular, the absence of a state space description of ABMs makes it very difficult to apply available control theory methods to study effective interventions. Applications of ABMs in situations with possible interventions by human actions (e.g. vaccination and quarantine schemes) have usually been limited to scenario analyses. In this case the models are simulated numerous times to compare alternative scenarios for intervention.
One possible approach to this problem is to construct state space models that approximate the agent-based model, similar to approaches proposed for discrete event simulations. This uses system identification methods developed for the state space model framework for agent-based simulations. Control-theoretic approaches for this modeling framework have been explored in a few cases. A first exploratory project in this direction resulted in a control method for in vitro competition of viruses. Such methods from approximate models may not work when there is spatial heterogeneity in the agent-based model (Federico, Gross and Lenhart, in preparation). Various techniques from optimal control and discrete optimization should be considered to investigate alternative formulations of control in relation to a state-space approximation and then compared to a similar formulation applied to the ABM.
This workshop brought together researchers working in agent-based models, optimal control and optimization to discuss the possible development of control theoretic approaches for agent-based models, beginning with the ones mentioned above. Alternative formulations of the approximation models and optimal control/optimization methods appropriate to each formulation were considered.
Evaluation report (PDF)
Summary Report. The workshop began with an overview of some optimal control techniques. Among the other presentation topics were strategies involved in constructing IBMs, protocol for standardizing the reporting of results, epidemic IBMs with adaptive behavior, emergence behavior from ABMs, and the use of ABMs in films and games. Discussion sessions highlighted the fact that there are a variety of different types of ABMs and IBMs. Other topics of interest included algebraic-based control methods; aggregate model approaches to aid in 'optimal' control of IBMs; and high performance computing and large simulations in relation to IBMs.
Here's what participants are saying about their experience at the NIMBioS Optimal Control Investigative Workshop.
Ben G. Fitzpatrick, Loyola Marymount University, Clarence J. Wallen, S. J. Professor of Mathematics
Katarzyna Rejniak, Faculty Member, Integrative Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute
Volker Grimm, Senior Scientist, Helmholtz Center for Environmental Research
Leander R, Lenhart S, Protopopescu V. 2015. Optimal control of continuous systems with impulse controls. Optimal Control Applications & Methods, 36(4): 535-549. [Online]
Leander R, Lenhart S, Protopopescu V. 2015. Controlling synchrony in a network of Kuramoto oscillators with time-varying coupling. Physica D-Nonlinear Phenomena, 301: 36-47. [Online]
Oremland M, Laubenbacher R. 2013. Optimization of agent-based models: Scaling methods and heuristic algorithms. Journal of Artificial Societies and Social Simulation, 17(2): 6. [Online]
Leander R, Lenhart S, Protopopescu V. 2012. Using optimal control theory to identify network structures that foster synchrony. Physica D-Nonlinear Phenomena, 241(5): 574-582. [Online]
Perminov VD. 2012. On the reproduction number and a presentation of results for infectious diseases models. Journal of Life Scienes, 6: 754-757. [PDF]
An G, Bartels J, Vodovotz Y. 2011. In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Development Research, 72(2): 187-200. [Online]
An G, Christley S. 2011. Agent-based modeling and biomedical ontologies: A roadmap. Wiley Interdisciplinary Reviews: Computational Statistics, 3(4): 343-356. [Online]
Hinkelmann F, Murrugarra D, Laubenbacher R, Jarrah AS. 2011. A mathematical framework for agent based models of complex biological networks. Bulletin of Mathematical Biology, 73(7): 1583-1602. [Online]
Neilan RM, Lenhart S. 2011. Optimal vaccine distribution in a spatiotemporal epidemic model with an application to rabies and raccoons. Journal of Mathematical Analysis and Applications, 378(2): 603-619. [Online]
An G. 2010. Closing the scientific loop: Bridging correlation and causality in the petaflop age. Science Translational Medicine, 2(41): 41ps34. [Online]
An G. 2010. Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research. Wound Repair and Regeneration, 18(1): 8-12. [Online]
D'Onofrio D, An G. 2010. A comparative approach for the investigation of biological information processing: An examination of the structure and function of computer hard drives and DNA. Theoretical Biology and Medical Modelling, 7: 3. [Online]
Mi Q, Li NYK, Ziraldo C, Ghuma A, Mikheev M, Squires R, Okonkwo DO, Verdolini K, Constantine G, An G, Vodovotz Y. 2010. Translational systems biology of inflammation: Potential applications to personalized medicine. Personalized Medicine, 7(5): 549-559. [Online]
Miller NR, Schaefer E, Gaff H, Fister KR, Lenhart S. 2010. Modeling optimal intervention strategies for cholera. Bulletin of Mathematical Biology, 72(8): 2004-2018. [Online]
Vodovotz Y, Constantine G, Faeder J, Mi Q, Rubin J, Bartels J, Sarkar J, Squires R, Okonkwo D, Gerlach J, Zamora R, Luckhart S, Ermentrout B, An G. 2010. Translational systems approaches to the biology of inflammation and healing. Immunopharmacol Immunotoxicology, 32(2): 181-195. [Online]
Castiglione F. 2009. Agent Based Modeling and Simulation, Introduction to. In: Meyers R (ed.), Encyclopedia of Complexity and Systems Science, 1, pp. 197-200. Springer: New York. [Online]
Fitzpatrick BM, Fordyce JA, Gavrilets S. 2009. Pattern, process and geographic modes of speciation. Journal of Evolutionary Biology, 22(11): 2332-2341. [Online]
Perminov V. 1 December 2014. Agent-based models and ill-posed problems. Keynote, 8th International Conference on Bio-inspired Information and Communications Technologies, Boston, MA. [Online]
An G. April 2011. Insights into core epistemological issues in biomedical research through the use of computational modeling and simulation: Addressing the fallacy of ontological truth and learning to deal with incompleteness. 1st Conference on Epistemology in Modeling and Simulation, Pittsburgh, PA.
An G. March 2011. Meta-engineering the generation and utilization of biomedical knowledge. Global Health Colloquium, Eck Institute for Global Health, Notre Dame University, South Bend, IN.
An G. March 2011. Translational systems biology of inflammation and healing. 10th Annual Scientific Retreat and Strategic Planning Mission of the McGowan Institute for Regenerative Medicine, Nemacolin Woodlands Resort, PA.
An G. October 2010. Facilitating knowledge instantiation with agent-based modeling: Towards an ecological paradigm for biomedical research. Committee on Immunology Seminar Series, University of Chicago, Chicago, IL.
An G. September 2010. Making science scale: Operational strategies for the future of biomedical research. Forum on the Future of Complex Systems Research and Application at the Complex Institute, University of North Carolina, Charlotte, NC.
An G. September 2010. Translational computational research: A future pathway for the academic surgeon. Grand Rounds, Dept of Surgery, University of Chicago, Chicago, IL.
An G. August 2010. Facilitating knowledge instantiation with agent-based modeling: Towards an ecological paradigm for biomedical research. Computational Institute Seminar Series, Chicago, IL.
An G, Colasanti R. August 2010. Studies of cellular movement using computational cellular ethology. 2010 q-Bio Conference, Santa Fe, NM.
An G. June 2010. Agent-based dynamic knowledge representation as an evolutionary paradigm for biomedical research. Institute on Systems Science and Health, Columbia University, New York City, NY.
An G. May 2010. Dynamic knowledge representation of pulmonary inflammation. American Thoracic Society 2010 International Conference, New Orleans, LA.
An G. May 2010. Meta-engineering knowledge and utilization: A surgeon's perspective. Solving the Most Challenging Surgical Problems, University of Chicago, Chicago, IL.
An G. May 2010. The translational dilemma. Annual Project Meeting of the National Center for Biomedical Ontology, Palo Alto, CA.
An G, Colasanti R, Barua J. March 2010. Poster: Automated ontology integration within a agent-based modeling framework for executable knowledge representation. 2010 AMIA Summit for Translational Bioinformatics, San Francisco, CA.
Christley S, An G, Bhattacharya S, Mariana T. 17 May 2011. A novel parsed network analysis of whole lung genomic data; towards spatially-explicit dynamic modeling of early human lung development. American Thoracic Society, Denver, CO.
Christley S, An G. April 2011. A proposed method for dynamic knowledge representation via agent-directed composition from biomedical and simulation ontologies: An example using Gut Mucus Layer Dynamics. SpringSim 2011/Agent-Directed Simulation Track, Boston, MA.
Christley S, An G. March 2011. Proposed agent-based composition of biomedical and simulation ontologies: Facilitating dynamic hypothesis instantiation. AMIA Summit on Translational Bioinformatics 2001, San Francisco, CA.
Colasanti R, An G. 9 April 2011. The Scorpion King and the frog: Why it is the nature of gut bacteria to be virulent. Huggins Research Symposium, Dept. of Surgery, University of Chicago, Chicago, IL.
Davilia AA, An G. February 2010. An agent-based model of liver damage, inflammation, and repair: In silico translation of cellular and molecular mechanisms to the clinical phenomena of cirrhosis using netlogo. 5th Annual Academic Surgical Congress, San Antonio, TX.
Katz D, An G. February 2011. A systems dynamics approach to oxidative stress, p53 activity and their effects on cellular fate. 6th Academic Surgical Congress, Huntington Beach, CA.
Katz D, An G. September 2010. Dynamic knowledge representation of cellular redox homeostasis and oxidative stress response in a systems dynamics model. 9th International Conference on Complexity in Acute Illness, Atlanta, GA.
Kim M, Christley S, Liu D, Alverdy JC, An G. 14 May 2011. Role of feeding-induced oxidative stress and TLR-response on cellular population dynamics in the pathogenesis of necrotizing enterocolitis: Insights from an agent-based model. 31st Meeting of the Surgical Infection Society, Palm Beach, FL.
Kim M, Christley S, Liu D, Alverdy JC, An G. 9 April 2011. Role of feeding-induced oxidative stress and TLR-response on cellular population dynamics in the pathogenesis of necrotizing enterocolitis: Insights from an agent-based model. Huggins Research Symposium, Dept. of Surgery, University of Chicago, Chicago, IL.
Kim M, Christley S, Liu D, Alverdy J, and An G. February 2011. Feeding induced oxidative stress and the pathogenesis of necrotizing enterocolitis: Insights form an agent-based model. 6th Academic Surgical Congress, Huntington Beach, CA.
Kurahasi C, An G. February 2010. Examining the spatial dynamics of the inflammatory response with topographical metrics in an agent-based computational model of inflammation and healing. 5th Annual Academic Surgical Congress, San Antonio, TX.
Seal J, An G. September 2010. Agent-based model of instant blood-mediated inflammatory reaction (IBMIR) effects on immediate graft loss following intra-portal islet cell transplantation. 9th International Conference on Complexity in Acute Illness, Atlanta, GA.
Seal J, Alverdy J, Babrowski T, Fink D, Romanowski K, Zaborina O, An G. April 2010. Mechanistic computational representation of pathogen response to microenvironment changes during host stress. 30th Annual Meeting of the Surgical Infection, Las Vegas, NV.
Seal JB, Alverdy JC, An G. February 2010. Mechanistic computational representation of iron metabolism in the gut milieu. 5th Annual Academic Surgical Congress, San Antonio, TX.
Seal JB, Alverdy JC, Zaborina O, Zaborin A, Babrowski T, Romanowski K, An G. January 2010. Computational mechanistic representation of phosphate sensing and virulence activation in pseudomonas aeruginosa in the gut milieu. 39th Annual Critical Care Congress of the Society of Critical Care Medicine, Miami Beach, FL.
Sheth KR, An G. February 2010. In silico translation of cellular and molecular mechanisms to clinical phenomena in atheroma development with an agent based model. 5th Annual Academic Surgical Congress, San Antonio, TX.
Stern JR, Seal JB, Alverdy JC, An G. 13 May 2011. Use of an agent-based model of epithelial wound healing to study differential phenotypes of virulence activation in Psuedomonas aeruginosa. 31st Meeting of the Surgical Infection Society, Palm Beach, FL.
Stern JR, Christley S, Alverdy JC, An G. 15 April 2011. Integration of epidermal growth factor receptor and transforming growth factor-beta based mechanisms of wound healing with agent-based modeling. Symposium on Advanced Wound Care and the Wound Healing Society, Dallas, TX.
Stern JR, Seal JB, Alverdy JC, An G. 9 April 2011. Use of an agent-based model of epithelial wound healing to study differential phenotypes of virulence activation in Pseudomonas aeruginosa. Huggins Research Symposium, Dept. of Surgery, University of Chicago, Chicago, IL.
Stern JR, Zaborina O, Valuckaite V, Connolly J, Olivas AD, Alverdy JC, An G. February 2011. Bacterial virulence activation and impaired gut epithelial healing: Integration of an in vitro mechanisms with an agent-based model. 6th Academic Surgical Congress, Huntington Beach, CA.
Swaroop M, An G. June 2010. Cell-level agent-based model of renal function and acute tubular necrosis. 33rd Annual Conference on Shock, Portland, OR.
Tridane A, Pasour V. 20-21 March 2010. Mathematical modeling in life sciences: Control and optimization. 34th Society for the Industrial and Applied Mathematics (SIAM) Southeastern-Atlantic Section Conference, North Carolina State University, Raleigh, NC.
Wandling M, An G. February 2010. Multi-scale dynamic knowledge representation of pulmonary inflammation with an agent-based model: From gene regulation to clinical phenomenon. 5th Annual Academic Surgical Congress, San Antonio, TX.
NIMBioS Investigative Workshops focus on broad topics or a set of related topics, summarizing/synthesizing the state of the art and identifying future directions. Workshops have up to 35 participants. Organizers and key invited researchers make up half the participants; the remaining participants are filled through open application from the scientific community. Open applicants selected to attend are notified by NIMBioS within two weeks of the application deadline. Investigative Workshops have the potential for leading to one or more future Working Groups. Individuals with a strong interest in the topic, including post-docs and graduate students, are encouraged to apply. If needed, NIMBioS can provide support (travel, meals, lodging) for Workshop attendees, whether from a non-profit or for-profit organization.
A goal of NIMBioS is to enhance the cadre of researchers capable of interdisciplinary efforts across mathematics and biology. As part of this goal, NIMBioS is committed to promoting diversity in all its activities. Diversity is considered in all its aspects, social and scientific, including gender, ethnicity, scientific field, career stage, geography and type of home institution. Questions regarding diversity issues should be directed to email@example.com. You can read more about our Diversity Plan on our NIMBioS Policies web page. The NIMBioS building is fully handicapped accessible.