Bilevel integer programming on a Boolean network for discovering critical genetic alterations in cancer development and therapy

Kyungduk Moon, Kangbok Lee, Sunil Chopra and Steve Kwon

European Journal of Operational Research, 2022, vol. 300, issue 2, 743-754

Abstract: Boolean network is a modeling tool that describes a dynamic system with binary variables and their logical transition formulas. Recent studies in precision medicine use a Boolean network to discover critical genetic alterations that may lead to cancer or target genes for effective therapies to individuals. In this paper, we study a logical inference problem in a Boolean network to find all such critical genetic alterations in a minimal (parsimonious) way. We propose a bilevel integer programming model to find a single minimal genetic alteration. Using the bilevel integer programming model, we develop a branch and bound algorithm that effectively finds all of the minimal alterations. Through a computational study with eleven Boolean networks from the literature, we show that the proposed algorithm finds solutions much faster than the state-of-the-art algorithms in large data sets.

Keywords: Bioinformatics; Boolean network; Bilevel programming; Branch and bound algorithm (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:300:y:2022:i:2:p:743-754

DOI: 10.1016/j.ejor.2021.10.019

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