Controlling Directed Protein Interaction Networks in Cancer

Krishna Kanhaiya,Cristian Gratie,Eugen Czeizler,Ion Petre

doi:10.1038/s41598-017-10491-y

Abstract

Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

Highlights

The main cause of cancer is genetic and epigenetic alterations, which allow normal cells to over-proliferate as tumour cells[1]
It has been shown that transforming growth factor-β (TGF β) regulates various kinase cascades such as the mitogen-activated protein kinase (MAPKs) ERK, the p38 MAPK pathways, the Jun N-terminal kinase (JNK), the PI3K kinase, the PP2A phosphatases and the Rho family members[5, 6]
Generalizing a similar result for target controllability, we prove that the algorithmic problem of minimizing the size of the controlling set while restricting the search to a subset of potential, drug targetable, driver nodes is algorithmically hard (i.e., NP-hard)

Summary

Introduction

The main cause of cancer is genetic and epigenetic alterations, which allow normal cells to over-proliferate as tumour cells[1]. A network approach over the cancer’s signal transduction dynamics gives us the tools to provide a better understanding of the various information-processing abilities employed during the molecular alteration of the cancerous cells[10] In human diseases, both associated and non-associated diseased proteins interact with one-another to create disease modules[11], and pave the way towards a layered configuration and understanding of these complex diseases[12]. Interactions is essential in demonstrating the complex molecular mechanism inside these networks, and in providing an inside-view of the dysfunctional signaling transduction processes within these networks All these examples illustrate that a network approach toward disease analysis could provide significant new insights into disease-gene identifications, as well as it could open new approaches towards network-based therapeutic tools, targeting entire disease modules together instead of individual elements[15]. We analyze the outcome differences when a small number of random protein signalling interactions are removed (or added) to the networks

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