Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions

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 s...

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Veröffentlicht in:Biotechnology journal Jg. 7; H. 3; S. 374 - 386
Hauptverfasser: Morris, Melody K., Shriver, Zachary, Sasisekharan, Ram, Lauffenburger, Douglas A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Weinheim WILEY-VCH Verlag 01.03.2012
WILEY‐VCH Verlag
Schlagworte:
Algorithms
Cell Communication
Computer Simulation
Constrained fuzzy logic model
Cytokines - metabolism
Cytokines - pharmacokinetics
Fuzzy Logic
G-CSF
Granulocyte Colony-Stimulating Factor - metabolism
Granulocyte Colony-Stimulating Factor - pharmacokinetics
Humans
Models, Theoretical
Multi-scale model
Signal Transduction
Signaling network
ISSN:1860-6768, 1860-7314, 1860-7314
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Zusammenfassung: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. Querying Quantitative Logic Models (Q2LM) to study intracellular signaling networks and cell/cytokine interactions: The authors present a framework for building and asking questions of constrained fuzzy logic (cFL) models constructed based on prior knowledge. We demonstrate the utility of this framework for generating testable hypotheses in an intracellular signaling network model and a model for pharmacokinetics and pharmacodynamics of G‐CSF.
Bibliographie:istex:7BE082D4D11E6A3F28D93C3C02E3B12FAA41C2DF
ark:/67375/WNG-SC2QRS5B-X
Institute for Collaborative Biotechnologies - No. W911NF-09-0001
Funded Access
ArticleID:BIOT201100222
NIH - No. P50-GM068762 and R24-DK090963
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Supporting information available online
ISSN:1860-6768
1860-7314
1860-7314
DOI:10.1002/biot.201100222