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

Abstract

It is becoming increasingly appreciated that the signal transduction systems used by eukaryotic cells to achieve a variety of essential responses represent highly complex networks rather than simple linear pathways. While significant effort is being made to experimentally measure the rate constants for individual steps in these signaling networks, many of the parameters required to describe the behavior of these systems remain unknown, or at best, estimates. With these goals and caveats in mind, we use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. To establish the usefulness of our approach, we have applied our methods towards modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. Using our approach, we are able to extract predictions that are highly specific and accurate, thereby enabling us to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. We show that extracting biologically relevant predictions from complex signaling models appears to be possible even in the absence of measurements of all the individual rate constants. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems wherein particular ''soft'' combinations of parameters can be varied over wide ranges without impacting the final output and demonstrating that a few ''stiff'' parameter combinations center around the paramount regulatory steps of the network. We refer to this property -- which is distinct from robustness -- as ''sloppiness.''

24 pages, 10 EPS figures, 1 GIF (makes 5 multi-panel figs + caption for GIF), IOP style; supp. info/figs. included as brown_supp.pdf

Cited by in corpus (35)

• Universally Sloppy Parameter Sensitivities in Systems Biology
• Parameter Space Compression Underlies Emergent Theories and Predictive Models
• The geometry of nonlinear least squares with applications to sloppy models and optimization
• Sloppiness, robustness, and evolvability in systems biology
• The sloppy model universality class and the Vandermonde matrix
• The limitations of model-based experimental design and parameter estimation in sloppy systems
• Stochastic win-stay-lose-shift strategy with dynamic aspirations in evolutionary social dilemmas
• PCA meets RG
• Bridging Mechanistic and Phenomenological Models of Complex Biological Systems
• Extracting falsifiable predictions from sloppy models
• Systems theory of Smad signaling
• Deciphering the imprint of topology on nonlinear dynamical network stability
• Optimizing sensors placement in complex networks for localization of hidden signal source: A review
• Cause and Cure of Sloppiness in Ordinary Differential Equation Models
• Seven Lessons from Manyfield Inflation in Random Potentials
• Untangling the hairball: fitness based asymptotic reduction of biological networks
• Chebyshev approximation and the global geometry of sloppy models
• Strategic model reduction by analysing model sloppiness: a case study in coral calcification
• Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
• Inflation as an Information Bottleneck - A strategy for identifying universality classes and making robust predictions
• Novel statistical ensemble analysis for simulating extrinsic noise-driven response in NF-κB signaling network
• Variational method for estimating the rate of convergence of Markov Chain Monte Carlo algorithms
• Unwinding the model manifold: choosing similarity measures to remove local minima in sloppy dynamical systems
• Ambitions for theory in the physics of life
• Does the Data Induce Capacity Control in Deep Learning?
• A distance constrained synaptic plasticity model of C. elegans neuronal network
• Sloppy models can be identifiable
• Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data
• Planar 2-D Cracks And Inclusions In Elastic Media
• Statistical Model Checking based Analysis of Biological Networks
• Proteins with greater influence on network dynamics evolve more slowly but are not more essential
• Global optimization using Gaussian Processes to estimate biological parameters from image data
• GNisi: A graph network for reconstructing Ising models from multivariate binarized data
• Dynamic Parameter Estimation of Brain Mechanisms
• Imposition of Different Optimizing Object with Non-Linear Constraints on Flux Sampling and Elimination of Free Futile Pathways