Homologous Control of Protein Signaling Networks

TL;DR

This paper demonstrates that recursive augmented sparse reconstruction can generate networks homologous to a reference signaling network, enabling inference of effective kinase inhibitor combinations for personalized medicine.

Contribution

It introduces a recursive ASR method that produces homologous networks, aiding in drug dosage inference from reconstructed models.

Findings

Reconstructed networks are homologous to the reference network.

Kinase inhibition effects are comparable between reconstructed and original networks.

Potential for personalized medicine through inferred drug combinations.

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.