Integer programming‐based method for observability of singleton attractors in Boolean networks

Cheng, Xiaoqing; Qiu, Yin Long; Hou, Wenpin; Ching, Wai‐Ki

doi:10.1049/iet-syb.2016.0022

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…They developed necessary and sufficient conditions for dealing with the captured observability problem. Cheng et al [24] proposed an efficient and effective approach for solving singleton attractor observability in BNs.…”

Section: A Observability Of Attractorsmentioning

confidence: 99%

“…It has been showed that the minimal key problem is NP-hard even when dealing with the binary alphabet [23]. Cheng et al [24] have proposed to apply the ILP-based method to solve the observability problem which was limited to singleton attractors. However, cyclic attractors exist in the real-world and the issue of observability for cyclic attractors has not yet been fully addressed.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection

et al. 2019

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Boolean Network (BN) is a simple and popular mathematical model that has attracted significant attention from systems biology due to its capacity to reveal genetic regulatory network behavior. In addition, observability, as an important network feature, plays a vital role in deciphering the underlying mechanisms driving a genetic regulatory network and has been widely investigated. Prior studies examined observability of BNs and other complex networks. That said, observability of attractor, which can serve as a biomarker for disease, has not been fully examined in the literature. In this study, we formulated a new definition for singleton or cyclic attractor observability in BNs and developed an effective methodology to resolve the captured problem. We also showed complexity is of O(P m n), when the maximal period of cyclic attractor is P, the number of attractor is m and the number of genes is n. Importantly, we have confirmed our method can faithfully predict the expression pattern of segment polarity genes in Drosophila melanogaster and showed it can effectively and efficiently deal with the captured observability problem.

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Section: A Observability Of Attractorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection

et al. 2019

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“…It is known that finding singleton attractors, or fixed points as they are often called, is an NP-hard problem (Akutsu et al, 1998 ). Nevertheless, many studies exist in the literature on detecting and analyzing attractors in the framework of BNs; see, e.g., Helikar and Rogers ( 2009 ); Cheng et al ( 2011a ); Gonzalez et al ( 2006 ); Zheng et al ( 2013 ); Cheng et al ( 2017 ); Zheng et al ( 2016 ) and references therein.…”

Section: Introductionmentioning

confidence: 99%

Global Stabilization of Boolean Networks to Control the Heterogeneity of Cellular Responses

2018

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Boolean networks (BNs) have been widely used as a useful model for molecular regulatory networks in systems biology. In the state space of BNs, attractors represent particular cell phenotypes. For targeted therapy of cancer, there is a pressing need to control the heterogeneity of cellular responses to the targeted drug by reducing the number of attractors associated with the ill phenotypes of cancer cells. Here, we present a novel control scheme for global stabilization of BNs to a unique fixed point. Using a sufficient condition of global stabilization with respect to the adjacency matrix, we can determine a set of constant controls so that the controlled BN is steered toward an unspecified fixed point which can then be further transformed to a desired attractor by subsequent control. Our method is efficient in that it has polynomial complexity with respect to the number of state variables, while having exponential complexity with respect to in-degree of BNs. To demonstrate the applicability of the proposed control scheme, we conduct simulation studies using a regulation influence network describing the metastatic process of cells and the Mitogen-activated protein kinase (MAPK) signaling network that is crucial in cancer cell fate determination.

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“…However, their algorithm is complicated and impractical for us to use it. In this paper, we develop an efficient method based on Integer Linear Programming [ 20 – 22 ] which has a wide application in solving NP-hard problems. Furthermore, all instances of the original problem are needed to convert into integer programming formalization so as to apply the existing free solver called CPLEX [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Discovery of Boolean metabolic networks: integer linear programming based approach

et al. 2018

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BackgroundTraditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects.ResultsIn this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature.ConclusionsSide-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at “http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip”.

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Integer programming‐based method for observability of singleton attractors in Boolean networks

Cited by 7 publications

References 32 publications

Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection

Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection

Global Stabilization of Boolean Networks to Control the Heterogeneity of Cellular Responses

Discovery of Boolean metabolic networks: integer linear programming based approach

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