Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions
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Morris_2011-Querying quantitative logic.pdf
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Author(s)
Morris, Melody Kay
Sasisekharan, Ram
Lauffenburger, Douglas A.
Shriver, Zachary H.
Date Issued
November 2011
Journal
Biotechnology Journal
Publisher
Wiley Blackwell
Citation
Morris, Melody K., Zachary Shriver, Ram Sasisekharan, and Douglas A. Lauffenburger. “Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions.” Biotechnology Journal 7, no. 3 (March 10, 2012): 374-386. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Version
Final published version
Abstract
Mathematical models have substantially improved our ability to predict the response of a complex biological system to perturbation, but their use is typically limited by difficulties in specifying model topology and parameter values. Additionally, incorporating entities across different biological scales ranging from molecular to organismal in the same model is not trivial. Here, we present a framework called “querying quantitative logic models” (Q2LM) for building and asking questions of constrained fuzzy logic (cFL) models. cFL is a recently developed modeling formalism that uses logic gates to describe influences among entities, with transfer functions to describe quantitative dependencies. Q2LM does not rely on dedicated data to train the parameters of the transfer functions, and it permits straight-forward incorporation of entities at multiple biological scales. The Q2LM framework can be employed to ask questions such as: Which therapeutic perturbations accomplish a designated goal, and under what environmental conditions will these perturbations be effective? We demonstrate the utility of this framework for generating testable hypotheses in two examples: (i) a intracellular signaling network model; and (ii) a model for pharmacokinetics and pharmacodynamics of cell-cytokine interactions; in the latter, we validate hypotheses concerning molecular design of granulocyte colony stimulating factor.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Cell Decision Process Center
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. School of Engineering
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DOI of Published Version
https://doi.org/10.1002/biot.201100222
