Agent-based models in translational systems biology
By G. An, Qi Mi, Joyeeta Dutta-Moscato, Y. Vodovotz. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2009
333 citations recorded by the source corpus.
Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent‐based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent‐based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. Copyright © 2009 John Wiley & Sons, Inc.
Subfields: Computational Theory and Mathematics, Modeling and Simulation, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b96f8102bd145e09704889cf7f4c37ca297fd36f
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In Molecular Biology
Citations in the corpus
- Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis (2018, positive): “Agent-based models and their use as a sepsis simulation Agent-based models (ABMs) are a generic class of computational models well-suited to represent the complex, stochastic, and non-linear dynamics of biological systems such as the immune response [12].”