Algorithms for Analysis and Control of Boolean Networks

2018 ◽  
pp. 3-7
Author(s):  
Tatsuya Akutsu
Keyword(s):  
Boolean Networks ◽  
And Control

Dynamics and control of singular boolean networks

2019 ◽  
Vol 21 (6) ◽  
pp. 2604-2613 ◽  
Author(s):  
Hongsheng Qi ◽  
Yupeng Qiao
Keyword(s):  
Boolean Networks ◽  
Dynamics And Control ◽  
And Control

scholarly journals Robust Output Controllability Analysis and Control Design for Incomplete Boolean Networks with Disturbance Inputs

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Lei Deng ◽  
Shihua Fu ◽  
Ying Li ◽  
Peiyong Zhu
Keyword(s):  
Sufficient Conditions ◽  
Boolean Networks ◽  
Algebraic Form ◽  
Output Control ◽  
Optimal Output ◽  
Product Technique ◽  
Output Controllability ◽  
The Cost ◽  
Necessary And Sufficient ◽  
And Control

This paper addresses the problems of robust-output-controllability and robust optimal output control for incomplete Boolean control networks with disturbance inputs. First, by resorting to the semi-tensor product technique, the system is expressed as an algebraic form, based on which several necessary and sufficient conditions for the robust output controllability are presented. Second, the Mayer-type robust optimal output control issue is studied and an algorithm is established to find a control scheme which can minimize the cost functional regardless of the effect of disturbance inputs. Finally, a numerical example is given to demonstrate the effectiveness of the obtained new results.

Solvability and control design for synchronization of Boolean networks

2013 ◽  
Vol 26 (6) ◽  
pp. 871-885 ◽  
Author(s):  
Xiangru Xu ◽  
Yiguang Hong
Keyword(s):  
Control Design ◽  
Boolean Networks ◽  
And Control

Complexity of the BN and the PBN Models of GRNs and Mappings for Complexity Reduction

2010 ◽  
pp. 334-351
Author(s):  
Ivan V. Ivanov
Keyword(s):  
Regulatory Networks ◽  
Computational Models ◽  
Dynamical Behavior ◽  
Network Models ◽  
Complexity Reduction ◽  
Boolean Networks ◽  
Intervention Strategies ◽  
Practical Applications ◽  
Genomic Regulation ◽  
And Control

Constructing computational models of genomic regulation faces several major challenges. While the advances in technology can help in obtaining more and better quality gene expression data, the complexity of the models that can be inferred from data is often high. This high complexity impedes the practical applications of such models, especially when one is interested in developing intervention strategies for disease control, for example, preventing tumor cells from entering a proliferative state. Thus, estimating the complexity of a model and designing strategies for complexity reduction become crucial in problems such as model selection, construction of tractable sub-network models, and control of the dynamical behavior of the model. In this chapter we discuss these issues in the setting of Boolean networks and probabilistic Boolean networks – two important classes of network models for genomic regulatory networks.

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