An S, Jang SY, Park SM, Lee CK, Kim HM, Cho KH. Global stabilizing control of large-scale biomolecular regulatory networks.
Bioinformatics 2023;
39:6998201. [PMID:
36688702 PMCID:
PMC9891247 DOI:
10.1093/bioinformatics/btad045]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION
Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on non-linear dynamics are not scalable.
RESULTS
Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on non-linear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity.
AVAILABILITY AND IMPLEMENTATION
We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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