Self-organization of signal transduction

TL;DR

This paper introduces a self-organizing model of signal transduction that learns kinetic rates through an evolutionary approach, enabling automatic adaptation to perturbations and optimizing signal transmission efficiency.

Contribution

It presents a novel parameter learning method based on signal transmission efficiency, leading to a self-organizing model that adapts to perturbations and external changes.

Findings

Proteins exhibit compensatory and co-regulation responses.

System adapts to extracellular signaling changes by optimizing transmission.

Signaling with transients involves maximizing peak response while minimizing steady-state responses.

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.