Homologous control of protein signaling networks

Abstract

In a previous paper we introduced a method called augmented sparse reconstruction (ASR) that identifies links among nodes of ordinary differential equation networks, given a small set of observed trajectories with various initial conditions. The main purpose of that technique was to reconstruct intracellular protein signaling networks. In this paper we show that a recursive augmented sparse reconstruction generates artificial networks that are homologous to a large, reference network, in the sense that kinase inhibition of several reactions in the network alters the trajectories of a sizable number of proteins in comparable ways for reference and reconstructed networks. We show this result using a large in-silico model of the epidermal growth factor receptor (EGF-R) driven signaling cascade to generate the data used in the reconstruction algorithm. The most significant consequence of this observed homology is that a nearly optimal combinatorial dosage of kinase inhibitors can be inferred, for many nodes, from the reconstructed network, a result potentially useful for a variety of applications in personalized medicine.

The Agnostic Structure of Data Science Methods

“…On this question, note that there are ways to apply data science also to small data sets, if we accept strong limitations on the type of questions and we impose strong regularization restrictions on the type of solutions[18].…”

The Agnostic Structure of Data Science Methods

“…-On this question, note that there are also ways to apply data science to small data sets, if we accept strong limitations on the type of questions and we impose strong regularization restrictions on the type of solutions(Napoletani, Signore et al 2011). …”