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Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling
1Department of Surgery, University of Chicago, Chicago, IL 60637, USA. docgca@gmail.com
Critical Reviews in Biomedical Engineering
|November 13, 2012
Summary
Agent-based modeling (ABM) offers a computational approach to understand complex diseases like sepsis and cancer. This method enhances the translation of mechanistic knowledge into effective therapeutics by improving hypothesis evaluation for biomedical research.
Area of Science:
- Biomedical Research
- Computational Biology
- Systems Biology
Background:
- Disordered inflammation underlies numerous system-level diseases, including sepsis, atherosclerosis, cancer, and autoimmune disorders.
- Translating mechanistic understanding of inflammation into effective therapeutics faces a bottleneck in hypothesis evaluation.
- Computational modeling is crucial for increasing the efficiency of identifying and evaluating mechanistic causality hypotheses.
Purpose of the Study:
- To explore agent-based modeling (ABM) as a tool for dynamic knowledge representation in biomedical research.
- To demonstrate the utility of ABM in studying inflammation across multiple biological scales.
- To propose expanding the use of modeling and simulation to enhance knowledge generation and evaluation in the broader biomedical community.
Main Methods:
- Agent-based modeling (ABM) is described as an object-oriented, discrete-event, rule-based simulation method.
- Examples of ABM applications in the study of inflammation at multiple scales are provided.
- The review discusses the suitability of ABM for dynamic knowledge representation, incorporating spatial and multi-scale biological processes.
Main Results:
- ABM is well-suited for biomedical dynamic knowledge representation due to its intuitive paradigm and ability to handle multi-scale and spatial considerations.
- Existing applications demonstrate ABM's effectiveness in studying inflammation.
- The framework facilitates the translation of mechanistic insights into therapeutic strategies.
Conclusions:
- Agent-based modeling provides a powerful framework for addressing the translational dilemma in inflammation research.
- Expanding the use of ABM can significantly augment the biomedical research community's ability to generate and evaluate mechanistic hypotheses.
- This approach promises to accelerate the development of novel therapeutics for inflammatory diseases.
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