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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| Published in: | Biotechnology journal Vol. 7; no. 3; pp. 374 - 386 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Weinheim
WILEY-VCH Verlag
01.03.2012
WILEY‐VCH Verlag |
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| ISSN: | 1860-6768, 1860-7314, 1860-7314 |
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| 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.
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. |
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| AbstractList | 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. 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. 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.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. |
| Author | Sasisekharan, Ram Morris, Melody K. Shriver, Zachary Lauffenburger, Douglas A. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22125256$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Glass, L., Kauffman, S. A., The logical analysis of continuous, non-linear biochemical control networks. J. Theo. Bio. 1973, 39, 103-129. – reference: Hess, J., Angel, P., Schorpp-Kistner, M., AP-1 subunits: quarrel and harmony among siblings. J. Cell Sci. 2004, 117, 5965-5973. – reference: Yoon, D. J., Liu, C. T., Quinlan, D. S., Nafisi, P. M., Kamei, D. T., Intracellular trafficking considerations in the development of natural ligand-drug molecular conjugates for cancer. Ann. Biomed. Eng. 2011, 39, 1235-1251. – reference: Janes, K. A., Albeck, J. G., Gaudet, S., Sorger, P. et al., A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 2005, 310, 1646-1653. – reference: Heinrich, R., Neel, B. G., Rapoport, T. A., Mathematical models of protein kinase signal transduction. Mol. Cell 2002, 9, 957-970. – reference: Morris, M. K., Saez-Rodriguez, J., Sorger, P. K., Lauffenburger, D. A., Logic-based models for the analysis of cell signaling networks. Biochemistry 2010, 49, 3216-3224. – reference: Zhang, R., Shah, M., Yang, J., Nyland, S. et al., Network model of survival signaling in large granular lymphocyte leukemia. Proc. Natl. Acad. Sci. USA 2008, 105, 16308-16313. – reference: Mendoza, L., Xenarios, I., A method for the generation of standardized qualitative dynamical systems of regulatory networks. Theo. Biol. Med. Model. 2006, 3, 13. – reference: Sarkar, C. A., Lowenhaupt, K., Horan, T., Boone, T. C. et al., Rational cytokine design for increased lifetime and enhanced potency using pH-activated "histidine switching". Nat. Biotechnol. 2002, 20, 908-913. – reference: Hendriks, B. S., Opresko, L. K., Wiley, H. S., Lauffenburger, D., Quantitative analysis of HER2-mediated effects on HER2 and epidermal growth factor receptor endocytosis: distribution of homo- and heterodimers depends on relative HER2 levels. J. Biol. 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