Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes

TL;DR

This paper introduces an algorithm that uses Boolean network attractors to identify therapeutic targets by removing pathological phenotypes, demonstrated on cancer and Fanconi anemia models, aiding drug discovery.

Contribution

It presents a novel in silico method for target identification that links Boolean network attractors to phenotypes, enabling the removal of disease-associated attractors.

Findings

Successfully removed pathological attractors in models

Identified target combinations for disease phenotypes

Supports computational approach to drug target discovery

Abstract

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.