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Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.

Aldridge BB, Saez-Rodriguez J, Muhlich JL, Sorger PK, Lauffenburger DA - PLoS Comput. Biol. (2009)

Bottom Line: Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation.We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling.More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.

View Article: PubMed Central - PubMed

Affiliation: Center for Cell Decision Processes, Cambridge, Massachusetts, United States of America.

ABSTRACT
When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.

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Spectrum of modeling methods.Modeling techniques balance specificity and complexity. Principal component                        analysis elucidates correlations among network components (A–E) by                        a linear transformation of the data, resulting in orthogonal principal                        components. Bayesian networks use conditional probabilities to associate                        correlations and influences between network components. Fuzzy logic uses                        rule-based gates and probabilistic representation of input variables to                        quantify influences and mechanism that regulate network species.                        Differential-equations models using mass-action kinetics are highly                        specified defining regulatory mechanism by defining rates of change in                        network species concentrations.
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pcbi-1000340-g001: Spectrum of modeling methods.Modeling techniques balance specificity and complexity. Principal component analysis elucidates correlations among network components (A–E) by a linear transformation of the data, resulting in orthogonal principal components. Bayesian networks use conditional probabilities to associate correlations and influences between network components. Fuzzy logic uses rule-based gates and probabilistic representation of input variables to quantify influences and mechanism that regulate network species. Differential-equations models using mass-action kinetics are highly specified defining regulatory mechanism by defining rates of change in network species concentrations.

Mentions: A variety of modeling methods can be applied to understanding protein signaling networks and the links between signals and phenotypes [1]. The choice of modeling method depends on the question being posed (e.g., mechanistic or phenotypic), the quality and type of experimental data (quantitative or qualitative), and the state of prior knowledge about the network (interaction map or detailed biochemical pathway; Figure 1). Abstract techniques are largely data-driven and aim to discover correlations among signals or between signals and cellular phenotypes [2]–[4]; these methods include principal component analysis (PCA) and partial least-squares regression (PLSR). Mechanistic differential equation-based models, in contrast, are highly specified and dependent on extensive prior knowledge about components and their interactions, but have the advantage that they capture temporal and spatial dynamics at the level of individual reactions [5]–[9]. Between these extremes, modeling methods such as Bayesian statistics, hidden Markov models, and logic-based models have been used to construct graph-based representations of influences and dependencies among signals and phenotypes based on experimental data [10]–[18]. An advantage of these methods is their applicability to situations in which mechanistic information is incomplete or fragmentary but the notion of a network of interacting biochemical species is nonetheless informative. Moreover, logic-based models use natural language to encode common logical statements such as “if the kinase is not active or the phosphatase is overexpressed, the substrate is not phosphorylated”. Logic-based models are commonly depicted as edge-node graphs in which interactions among species occur at nodes, with gates specifying the logic of the interactions based on a set of specified rules. The identities of the gates are typically determined based on prior knowledge or experimental observables and the input-output relationships of each gate inferred from experimental data [11], [12], [19]–[24].


Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.

Aldridge BB, Saez-Rodriguez J, Muhlich JL, Sorger PK, Lauffenburger DA - PLoS Comput. Biol. (2009)

Spectrum of modeling methods.Modeling techniques balance specificity and complexity. Principal component                        analysis elucidates correlations among network components (A–E) by                        a linear transformation of the data, resulting in orthogonal principal                        components. Bayesian networks use conditional probabilities to associate                        correlations and influences between network components. Fuzzy logic uses                        rule-based gates and probabilistic representation of input variables to                        quantify influences and mechanism that regulate network species.                        Differential-equations models using mass-action kinetics are highly                        specified defining regulatory mechanism by defining rates of change in                        network species concentrations.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2663056&req=5

pcbi-1000340-g001: Spectrum of modeling methods.Modeling techniques balance specificity and complexity. Principal component analysis elucidates correlations among network components (A–E) by a linear transformation of the data, resulting in orthogonal principal components. Bayesian networks use conditional probabilities to associate correlations and influences between network components. Fuzzy logic uses rule-based gates and probabilistic representation of input variables to quantify influences and mechanism that regulate network species. Differential-equations models using mass-action kinetics are highly specified defining regulatory mechanism by defining rates of change in network species concentrations.
Mentions: A variety of modeling methods can be applied to understanding protein signaling networks and the links between signals and phenotypes [1]. The choice of modeling method depends on the question being posed (e.g., mechanistic or phenotypic), the quality and type of experimental data (quantitative or qualitative), and the state of prior knowledge about the network (interaction map or detailed biochemical pathway; Figure 1). Abstract techniques are largely data-driven and aim to discover correlations among signals or between signals and cellular phenotypes [2]–[4]; these methods include principal component analysis (PCA) and partial least-squares regression (PLSR). Mechanistic differential equation-based models, in contrast, are highly specified and dependent on extensive prior knowledge about components and their interactions, but have the advantage that they capture temporal and spatial dynamics at the level of individual reactions [5]–[9]. Between these extremes, modeling methods such as Bayesian statistics, hidden Markov models, and logic-based models have been used to construct graph-based representations of influences and dependencies among signals and phenotypes based on experimental data [10]–[18]. An advantage of these methods is their applicability to situations in which mechanistic information is incomplete or fragmentary but the notion of a network of interacting biochemical species is nonetheless informative. Moreover, logic-based models use natural language to encode common logical statements such as “if the kinase is not active or the phosphatase is overexpressed, the substrate is not phosphorylated”. Logic-based models are commonly depicted as edge-node graphs in which interactions among species occur at nodes, with gates specifying the logic of the interactions based on a set of specified rules. The identities of the gates are typically determined based on prior knowledge or experimental observables and the input-output relationships of each gate inferred from experimental data [11], [12], [19]–[24].

Bottom Line: Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation.We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling.More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.

View Article: PubMed Central - PubMed

Affiliation: Center for Cell Decision Processes, Cambridge, Massachusetts, United States of America.

ABSTRACT
When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.

Show MeSH
Related in: MedlinePlus