Abduction Based Drug Target Discovery Using Boolean Control Network

Abstract

Abstract. A major challenge in cancer research is to determine the genetic mutations causing the cancerous phenotype of cells and conversely, the actions of drugs initiating programmed cell death in cancer cells. However, such a challenge is compounded by the complexity of the genotype-phenotype relationship and therefore, requires to relate the molecular effects of mutations and drugs to their consequences on cellular phenotypes. Discovering these complex relationships is at the root of new molecular drug targets discovery and cancer etiology investigation. In their elucidation, computational methods play a major role for the inference of the molecular causal actions from molecular and biological networks data analysis. In this article, we propose a theoretical framework where mutations and drug actions are seen as topological perturbations/actions on molecular networks inducing cell phenotype reprogramming. The framework is based on Boolean control networks where the topological network actions are modelled by control parameters. We present a new algorithm using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. The framework is validated on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and finding drug targets.

Sequential Reprogramming of Boolean Networks Made Practical

“…Existing works mainly focus on one-step reprogramming [2,3,5,6,7,21,24], considering various kinds of perturbations and targeted dynamical properties. Predictions are obtained following different techniques, with probabilistic modelling in [3,5,6], or qualitative modelling in [2,7,21,24].…”

“…Existing works mainly focus on one-step reprogramming [2,3,5,6,7,21,24], considering various kinds of perturbations and targeted dynamical properties. Predictions are obtained following different techniques, with probabilistic modelling in [3,5,6], or qualitative modelling in [2,7,21,24]. Sequential reprogramming is also studied in the literature [1,19,12] using quite different approaches: Abou-Jaoudé et al [1] applied model checking to verify that a set of perturbations can reprogram the cell correctly, using other attractors as intermediate steps if needed, Ronquist et al [19] used a quantitative model that returns a specific time for the perturbations to be made; lastly in the work of Mandon et al [12], the perturbations can be done at any time, but require precise knowledge of the state of the system (i.e., complete observability).…”

Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery

“…These pathways are gathered into a unique Boolean network through the lens of their role in the regulation of the G1/S transition and the triggering of apoptosis in case of DNA damage. The corresponding Boolean network 6 , constructed from published litterature and signalling pathways databases (KEGG [27] and Signor [28]), is shown in Figure 5 and the molecular mechanism for each interaction is detailed and referenced in the conference version of this article [29]). The Boolean dynamics is bistable characterizing two cellular functions in normal cells: either (1) the cell enters division by activation of the G1/S transition and inhibition of apoptosis, or (2) it enters in apoptosis and arrest the cell cycle.…”

Online Observability of Boolean Control Networks

“…A natural extension of BN is Boolean control network (BCN) with external regulations and perturbations (Ideker et al [2001]). BCNs have been applied to various real-life problems and typical examples including structural and functional analysis of signaling and regulatory networks (Kaufman et al [1999] and Klamt et al [2006]), abduction based drug target discovery (Biane and Delaplace [2017]), and pursuing evasion problems in polygonal environments (Thunberg et al [2011]).…”