The statistical mechanics of complex signaling networks: nerve growth factor signaling

Brown, Kevin; Hill, Charles; Calero, Guillermo; Myers, Christopher R.; Lee, K H; Sethna, James P.; Cerione, Richard A.

doi:10.1088/1478-3967/1/3/006

Cited by 237 publications

(320 citation statements)

References 41 publications

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“…Note that the eigenvalues are more or less uniformly distributed over approximately 6 orders of magnitude (with occasionally a few exceptionally sloppy eigenvectors); this behavior is characteristic of other sloppy systems (30)(31)(32)(33) …”

Section: Derivation Of Kinase Inactivation Inhibitory Mechanismmentioning

confidence: 88%

“…The pathway model here presented belongs to a class of so-called "sloppy" models (30) (see the Appendix), characterized by having a significant number of poorly known parameters with widely varying sensitivities. As an alternative to our approach, one can utilize methods to reduce the dimensionality of parameter space, transforming into a new set of dimensions (reflecting parameter combinations) that are organized by their overall sensitivities (30)(31)(32). In principle, such an approach can help optimize computations in parameter estimation and identify dependencies in parameter variation.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway

2008

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Mechanistic models of signal transduction have emerged as valuable tools for untangling complex signaling networks and gaining detailed insight into pathway dynamics. The natural extension of these tools is for the design of therapeutic strategies. We have generated a novel computational model of lipopolysaccharide-induced p38 signaling in the context of TNF-R production in inflammatory disease. Using experimental measurement of protein levels and phospho-protein time courses, populations of model parameters were estimated. With a collection of parameter sets, reflecting virtual diversity, we step through analysis of the p38 signaling pathway model to answer specific drug discovery questions regarding target prioritization, inhibitor simulation, model robustness and co-drugging. We demonstrate that target selection cannot be assessed independently from inhibitor mechanism of action and is also linked with robustness to cellular variability. Finally, we assert that in the face of parameter uncertainty one can still uncover consistent findings that can guide drug discovery efforts.

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Section: Derivation Of Kinase Inactivation Inhibitory Mechanismmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway

2008

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“…S1 and S2). Phosphorylation and docking reactions are modeled according to Kholodenko et al;37 the MAP kinase pathway reactions are modeled after Schoeberl et al; 57 Akt and PI3K activation are incorporated into the model as described in Brown et al 7 The similar parameterization and topology in these models allowed us to construct a consistent, stable, and comprehensive system with results in good agreement with published experimental data. 52 Seventeen of these reactions are novel to this work and represent enhanced molecular resolution and detail in EGFR activation, phosphorylation, and docking reactions ( Fig.…”

Section: Overall Summary Of Methodsmentioning

confidence: 94%

A Multiscale Computational Approach to Dissect Early Events in the Erb Family Receptor Mediated Activation, Differential Signaling, and Relevance to Oncogenic Transformations

et al. 2007

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Abstract-We describe a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy-based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/ continuum stochastic dynamics protocols to study the dimermediated receptor activation characteristics of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR). Through these modeling approaches, we are able to extend the prior modeling of EGF-mediated signal transduction by considering specific EGFR tyrosine kinase (EGFRTK) docking interactions mediated by differential binding and phosphorylation of different C-terminal peptide tyrosines on the RTK tail. By modeling signal flows through branching pathways of the EGFRTK resolved on a molecular basis, we are able to transcribe the effects of molecular alterations in the receptor (e.g., mutant forms of the receptor) to differing kinetic behavior and downstream signaling response. Our molecular dynamics simulations show that the drug sensitizing mutation (L834R) of EGFR stabilizes the active conformation to make the system constitutively active. Docking simulations show preferential characteristics (for wildtype vs. mutant receptors) in inhibitor binding as well as preferential enhancement of phosphorylation of particular substrate tyrosines over others. We find that in comparison to the wildtype system, the L834R mutant RTK preferentially binds the inhibitor erlotinib, as well as preferentially phosphorylates the substrate tyrosine Y1068 but not Y1173. We predict that these molecular level changes result in preferential activation of the Akt signaling pathway in comparison to the Erk signaling pathway for cells with normal EGFR expression. For cells with EGFR over expression, the mutant over activates both Erk and Akt pathways, in comparison to wildtype. These results are consistent with qualitative experimental measurements reported in the literature. We discuss these consequences in light of how the network topology and signaling characteristics of altered (mutant) cell lines are shaped differently in relationship to native cell lines.

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“…The Fisher information provides a local measure of the expected second-order sensitivities of a system evaluated at a particular set of parameter values. An eigenvalue decomposition of the Fisher information can elucidate the parameter dependencies within the model; most biochemical networks have a relatively small number of parameter combinations that strongly affect the model output, with the remaining combinations having little effect [20,24,25,41].…”

Section: Differential Geometric Markov Chainmentioning

confidence: 99%

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

Calderhead

Girolami

2011

Interface Focus.

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Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.

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The statistical mechanics of complex signaling networks: nerve growth factor signaling

Cited by 237 publications

References 41 publications

Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway

Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway

A Multiscale Computational Approach to Dissect Early Events in the Erb Family Receptor Mediated Activation, Differential Signaling, and Relevance to Oncogenic Transformations

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

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