Self-organization of signal transduction.

Gabriele Scheler

doi:10.12688/f1000research.2-116.v1

Abstract

We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. We apply this to learn kinetic rates as a form of evolutionary learning, and look for parameters which satisfy the objective. This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data. The resulting model is self-organizing, i.e. perturbations in protein concentrations or changes in extracellular signaling will automatically lead to adaptation. We systematically perturb protein concentrations and observe the response of the system. We find compensatory or co-regulation of protein expression levels. In a novel experiment, we alter the distribution of extracellular signaling, and observe adaptation based on optimizing signal transmission. We also discuss the relationship between signaling with and without transients. Signaling by transients may involve maximization of signal transmission efficiency for the peak response, but a minimization in steady-state responses. With an appropriate objective function, this can also be achieved by concentration adjustment. Self-organizing systems may be predictive of unwanted drug interference effects, since they aim to mimic complex cellular adaptation in a unified way.

Highlights

Signal transduction systems are often modeled as networks of biochemical kinetic equations implemented as continuous-time dynamical models using differential equations[1,2]
If we regard a subset of species as inputs, and make sure that the system always converges to equilibrium values by using weakly reversible equations[3,4,5], we may transform these models into a set of matrices fulfilling the role of input-output transfer functions, i.e. a mapping from sustained input signal levels to steady-state concentrations for all target species[6]
The input is a membrane receptor, a G-protein coupled receptor (GPCR), β(2)AR, which is activated by an extracellular pharmacological agonist ISO; the output is a membrane protein, VASP, which promotes actin filament elongation

Summary

Introduction

Signal transduction systems are often modeled as networks of biochemical kinetic equations implemented as continuous-time dynamical models using differential equations[1,2]. If we regard a subset of species as inputs, and make sure that the system always converges to equilibrium values by using weakly reversible equations[3,4,5], we may transform these models into a set of matrices fulfilling the role of input-output transfer functions, i.e. a mapping from sustained input signal levels to steady-state concentrations for all target species[6]. Protein signaling functions (psfs) are a systemic gener alization of individual dose-response functions, which are usually described by Hill equations[7]. We assume that any biological signal transduction system is constructed with optimized efficiency of signal transmission. We assume that cells have the ability to adapt to perturbations of protein concentrations and changes in extracellular signaling by reinstating signal transmission efficiency. We investigate this question using a biologically realistic system – beta-adrenergic signaling in a submembrane compartment of a mouse embryonic fibroblast for a single input scenario, focusing on a selected target species as relevant output or actuator of the system (Figure 1A)

Methods

Results

Conclusion