Stabilizing Control of Complex Biological Networks Based on Attractor-Specific Network Reduction

Yang, Jung–Min; Lee, Chun-Kyung; Cho, Kwang-Hyun

doi:10.1109/tcns.2020.3041423

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…In this paper, we reviewed various techniques for implementing target discovery and control of gene regulatory networks. Due to the growing nature of the field, there are always emerging, novel techniques to implement and we acknowledge that the methods included here are not fully exhaustive [58][59][60][61][62]. Even so, we have set out to provide a list of varying options, depending on the specific aims and information available to users, that represent a broad range of applicable theory.…”

Section: Discussionmentioning

confidence: 99%

“…The computational time to find a single FVS is reasonable, the issue arises when trying to find all possible FVSs. The global stabilization of BNs have been shown to have computational complexity that is exponential with respect to the number of state variables [55,56]. However, while the problem of exactly identifying the minimal FVS has complexity of NP-hard, a variety of fast algorithms exist to find close-tominimal solutions [31,57].…”

Section: Limitationsmentioning

confidence: 99%

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Phenotype control techniques for Boolean gene regulatory networks

Plaugher

Murrugarra

2023

Preprint

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Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What's more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory. In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte (T-LGL) Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Phenotype control techniques for Boolean gene regulatory networks

Plaugher

Murrugarra

2023

Preprint

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“…Complex networks represent the intricate connections among elements in many real-world systems and constitute an efficacious formalism, 1,2 such as the Internet, biological networks, 3,4 communication networks, 5,6 social and transport networks, 7,8 power networks, 9,10 and so on. It is a scientific research method that complex networks are modeled as graphs, where the elements are abstracted as nodes and the interactions among these elements are abstracted as edges.…”

Section: Introductionmentioning

confidence: 99%

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

Lin,

Shen,

Liu

et al. 2024

Intl J Robust & Nonlinear

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This paper studies the resilient set‐membership filtering for discrete‐time nonlinear complex networks with output‐coupled topologies. An event‐triggered mechanism (ETM) is employed to save communication resources from the sensor to the filter. A resilient set‐membership filter (RSMF) handles unknown bounded uncertainty, resists the gain perturbation and makes the estimation errors to a certain ellipsoid region. Sufficient conditions for the existence of the RSMF are derived using mathematical induction techniques. The filter gain can be determined on the basis of solving a specific set of recursive matrix inequalities. Then, by optimizing the constrained ellipsoid of the estimation error, the optimized filtering performance is ensured. Finally, a numerical example is given to demonstrate the effectiveness of the proposed RSMF.

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Phenotype Control techniques for Boolean gene regulatory networks

Plaugher,

Murrugarra

2023

Bull Math Biol

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Stabilizing Control of Complex Biological Networks Based on Attractor-Specific Network Reduction

Cited by 7 publications

References 37 publications

Phenotype control techniques for Boolean gene regulatory networks

Phenotype control techniques for Boolean gene regulatory networks

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

Phenotype Control techniques for Boolean gene regulatory networks

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