Limits...
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.

Show MeSH

Related in: MedlinePlus

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

License
getmorefigures.php?uid=PMC2663056&req=5

pcbi-1000340-g001: Spectrum of modeling methods.Modeling techniques balance specificity and complexity. Principal componentanalysis elucidates correlations among network components (A–E) bya linear transformation of the data, resulting in orthogonal principalcomponents. Bayesian networks use conditional probabilities to associatecorrelations and influences between network components. Fuzzy logic usesrule-based gates and probabilistic representation of input variables toquantify influences and mechanism that regulate network species.Differential-equations models using mass-action kinetics are highlyspecified defining regulatory mechanism by defining rates of change innetwork species concentrations.

Mentions: A variety of modeling methods can be applied to understanding protein signalingnetworks and the links between signals and phenotypes [1]. The choice of modelingmethod depends on the question being posed (e.g., mechanistic or phenotypic), thequality and type of experimental data (quantitative or qualitative), and the stateof prior knowledge about the network (interaction map or detailed biochemicalpathway; Figure 1). Abstracttechniques are largely data-driven and aim to discover correlations among signals orbetween signals and cellular phenotypes [2]–[4]; thesemethods include principal component analysis (PCA) and partial least-squaresregression (PLSR). Mechanistic differential equation-based models, in contrast, arehighly specified and dependent on extensive prior knowledge about components andtheir interactions, but have the advantage that they capture temporal and spatialdynamics at the level of individual reactions [5]–[9]. Betweenthese extremes, modeling methods such as Bayesian statistics, hidden Markov models,and logic-based models have been used to construct graph-based representations ofinfluences and dependencies among signals and phenotypes based on experimental data[10]–[18]. An advantage ofthese methods is their applicability to situations in which mechanistic informationis incomplete or fragmentary but the notion of a network of interacting biochemicalspecies is nonetheless informative. Moreover, logic-based models use naturallanguage to encode common logical statements such as “if the kinase is notactive or the phosphatase is overexpressed, the substrate is notphosphorylated”. Logic-based models are commonly depicted as edge-nodegraphs in which interactions among species occur at nodes, with gates specifying thelogic of the interactions based on a set of specified rules. The identities of thegates are typically determined based on prior knowledge or experimental observablesand 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 componentanalysis elucidates correlations among network components (A–E) bya linear transformation of the data, resulting in orthogonal principalcomponents. Bayesian networks use conditional probabilities to associatecorrelations and influences between network components. Fuzzy logic usesrule-based gates and probabilistic representation of input variables toquantify influences and mechanism that regulate network species.Differential-equations models using mass-action kinetics are highlyspecified defining regulatory mechanism by defining rates of change innetwork species concentrations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2663056&req=5

pcbi-1000340-g001: Spectrum of modeling methods.Modeling techniques balance specificity and complexity. Principal componentanalysis elucidates correlations among network components (A–E) bya linear transformation of the data, resulting in orthogonal principalcomponents. Bayesian networks use conditional probabilities to associatecorrelations and influences between network components. Fuzzy logic usesrule-based gates and probabilistic representation of input variables toquantify influences and mechanism that regulate network species.Differential-equations models using mass-action kinetics are highlyspecified defining regulatory mechanism by defining rates of change innetwork species concentrations.
Mentions: A variety of modeling methods can be applied to understanding protein signalingnetworks and the links between signals and phenotypes [1]. The choice of modelingmethod depends on the question being posed (e.g., mechanistic or phenotypic), thequality and type of experimental data (quantitative or qualitative), and the stateof prior knowledge about the network (interaction map or detailed biochemicalpathway; Figure 1). Abstracttechniques are largely data-driven and aim to discover correlations among signals orbetween signals and cellular phenotypes [2]–[4]; thesemethods include principal component analysis (PCA) and partial least-squaresregression (PLSR). Mechanistic differential equation-based models, in contrast, arehighly specified and dependent on extensive prior knowledge about components andtheir interactions, but have the advantage that they capture temporal and spatialdynamics at the level of individual reactions [5]–[9]. Betweenthese extremes, modeling methods such as Bayesian statistics, hidden Markov models,and logic-based models have been used to construct graph-based representations ofinfluences and dependencies among signals and phenotypes based on experimental data[10]–[18]. An advantage ofthese methods is their applicability to situations in which mechanistic informationis incomplete or fragmentary but the notion of a network of interacting biochemicalspecies is nonetheless informative. Moreover, logic-based models use naturallanguage to encode common logical statements such as “if the kinase is notactive or the phosphatase is overexpressed, the substrate is notphosphorylated”. Logic-based models are commonly depicted as edge-nodegraphs in which interactions among species occur at nodes, with gates specifying thelogic of the interactions based on a set of specified rules. The identities of thegates are typically determined based on prior knowledge or experimental observablesand 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