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

Kyungduk Moon, Kangbok Lee*, Sunil Chopra, Steve Kwon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)743-754
Number of pages12
JournalEuropean Journal of Operational Research
Volume300
Issue number2
DOIs
StateAccepted/In press - 2021

Keywords

  • Bilevel programming
  • Bioinformatics
  • Boolean network
  • Branch and bound algorithm

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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