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Step-wise target controllability of driver nodes in biological networks

Abstract : The possibility of using mathematical tools to describe and influence complex interconneced systems is getting more and more attainable. Methods based on network controllability to identify the nodes able to impact the state of a whole system are nowadays increasingly studied. However, the problem has a high combinatorial and numerical complexity because of the huge number of a priori equivalent solutions. There has recently been a growing interest in finding the minimum number of inputs to control the whole or a part of the system, and in evaluating the ability of a single node in steering this process. However, specific problems have drawn less attention. In some biological settings it may be required to act on a single node, and it may be of interest to affect only a well-defined subset of the units, a target set. This leads to a single input target control problem, where we can exploit biological constraints to study the relative importance of different driver nodes. This dissertation aims to apply controllability theory to biological networks in an original way, to understand what insight mathematical controllability theory can bring to biological networks, and to study the importance of different driver nodes in controlling a target set. We develop a heuristic that we call step-wise target controllability, which measures the centrality of a driver node as the number of targets it can control and provides a controllable configuration of targets. We show that this method is efficient for sparse directed networks. Our method represents a practical answer to use to our advantage the complexity of the control problem, exploiting existing biological knowledge.
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Submitted on : Thursday, March 18, 2021 - 3:09:19 PM
Last modification on : Tuesday, March 30, 2021 - 8:15:15 AM


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  • HAL Id : tel-03022357, version 2


Giulia Bassignana. Step-wise target controllability of driver nodes in biological networks. Bioinformatics [q-bio.QM]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS044⟩. ⟨tel-03022357v2⟩



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