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

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

doi:10.1016/j.ejor.2021.10.019

Cited by 5 publications

(8 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As stressed in the introduction, there exists only tools addressing P1 to compare with. The experiments of Moon, Lee, Chopra, and Kwon (2022) show that their bilevel integer programmingbased method systematically outperforms the ASP implementation of pyActoNet (Biane and Delaplace, 2019). On the same benchmark, BoNesis performed either similarly or in shorter time, albeit limited to locally-monotone BNs only.…”

Section: Scalabilitymentioning

confidence: 95%

“…In order to evaluate the scalability on realistic BNs, we use the benchmark constituted by Moon, Lee, Chopra, and Kwon (2022) to evaluate the reprogramming of fixed points (P1). Their benchmark gathers 10 locally-monotone BNs and 1 non-monotone one, that BoNesis cannot address.…”

Section: Scalabilitymentioning

confidence: 99%

“…MP attractors match with so-called minimal trap spaces of the BN, which are the smallest sub-hypercubes closed by the Boolean map. Problem P1 has been already addressed in the literature, notably by Biane and Delaplace (2019) with the ActoNet method and by Moon, Lee, Chopra, and Kwon (2022), based on bilevel integer programming. To our knowledge, none of the other problems have been addressed completely in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis

Paulevé¹

2023

Peer Community Journal

View full text Add to dashboard Cite

Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker properties, which specify that some components are fixed to a specific value. We study 4 variants of the marker reprogramming problem: the reprogramming of fixed points, of minimal trap spaces, and of fixed points and minimal trap spaces reachable from a given initial configuration with the most permissive update mode. The perturbations consist of fixing a set of components to a fixed value. They can destroy and create new attractors. In each case, we give an upper bound on their theoretical computational complexity, and give an implementation of the resolution using the BoNesis Python framework. Finally, we lift the reprogramming problems to ensembles of BNs, as supported by BoNesis, bringing insight on possible and universal reprogramming strategies. This paper can be executed and modified interactively.

show abstract

Section: Scalabilitymentioning

confidence: 95%

Section: Scalabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis

Paulevé¹

2023

Peer Community Journal

View full text Add to dashboard Cite

show abstract

“…www.ijacsa.thesai.org Scholars such as Moon K proposed a two-level programming model to find a single minimum genetic variation by studying the logical reasoning of Boolean networks, and developed a branching and constraint algorithm, which can effectively find all the minimum mutations. The effectiveness of this model is validated through computational studies on a variety of Boolean networks [8]. Song et al constructed an energy optimal scheduling model based on uncertain two-level programming.…”

Section: Related Workmentioning

confidence: 99%

“…In Eq. ( 7), L is the number of all path nodes, in Equation (8), θ k is the angle between the direction of the transportation vehicle at the current moment and the planned path, and 1 θ k  is the angle between the direction of the transportation vehicle and the planned path at the previous moment. The research abstracts the transportation reserve as a mass point, which can θ also be regarded as the expected turning angle of the vehicle, as shown in Eq.…”

mentioning

confidence: 99%

Research on the Optimization Problem of Agricultural Product Logistics based on Genetic Algorithm

Wang¹

2022

IJACSA

View full text Add to dashboard Cite

China's national development and reform commission issued the "logistics industry adjustment and revitalization plan" in 2009 to support the development of agricultural product logistics and distribution centers. China's agricultural product logistics and distribution have entered a stage of rapid development. With the rise of the sharing economy, logistics has become a bottleneck restricting the further development of agricultural product distribution. In order to realize the effective cooperation among the main body of agricultural product logistics distribution, improve the distribution efficiency and reduce the distribution cost, a logistics distribution optimization model based on the two-layer planning idea and genetic algorithm is proposed. A two-level programming model is constructed by combining qualitative and quantitative methods, theory and examples, and insertion and deletion operators are introduced to optimize the genetic algorithm. The research results show that the optimized genetic algorithm has a 54.55% increase in convergence speed, 1.08% in performance, and a 54.231% reduction in path length compared to the benchmark algorithm. It effectively improves the efficiency of path planning and saves the planning cost, and the final target value is reduced by 48.19%.

show abstract

Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks

Riva,

Lagniez,

López

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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

Cited by 5 publications

References 58 publications

Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis

Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis

Research on the Optimization Problem of Agricultural Product Logistics based on Genetic Algorithm

Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks

Contact Info

Product

Resources

About