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Computational method for the inference of therapeutic targets and sequence of treatments

Abstract : Network controllability is a major challenge in network medicine. It consists in finding a way to rewire molecular networks to reprogram the cell fate. The reprogramming action is typically represented as the action of a control. In this thesis, we extended the single control action method by investigating the sequential control of Boolean networks. We present a theoretical framework for the formal study of control sequences.We consider freeze controls, under which the variables can only be frozen to 0, 1 or unfrozen. We define a model of controlled dynamics where the modification of the control only occurs at a stable state in the synchronous update mode. We refer to the inference problem of finding a control sequence modifying the dynamics to evolve towards a desired state or property as CoFaSe. Under this problem, a set of variables are uncontrollable. We prove that this problem is PSPACE-hard. We know from the complexity of CoFaSe that finding a minimal sequence of control by exhaustively exploring all possible control sequences is not practically tractable. By studying the dynamical properties of the CoFaSe problem, we found that the dynamical properties that imply the necessity of a sequence of control emerge from the update functions of uncontrollable variables. We found that the length of a minimal control sequence cannot be larger than twice the number of profiles of uncontrollable variables. From this result, we built two algorithms inferring minimal control sequences under synchronous dynamics. Finally, the study of the interdependencies between sequential control and the topology of the interaction graph of the Boolean network allowed us to investigate the causal relationships that exist between structure and control. Furthermore, accounting for the topological properties of the network gives additional tools for tightening the upper bounds on sequence length. This work sheds light on the key importance of non-negative cycles in the interaction graph for the emergence of minimal sequences of control of size greater than or equal to two.
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Submitted on : Tuesday, June 7, 2022 - 2:44:24 PM
Last modification on : Saturday, July 23, 2022 - 5:22:38 PM
Long-term archiving on: : Thursday, September 8, 2022 - 6:53:44 PM


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  • HAL Id : tel-03689701, version 1


Jérémie Pardo. Computational method for the inference of therapeutic targets and sequence of treatments. Bioinformatics [q-bio.QM]. Université Paris-Saclay, 2022. English. ⟨NNT : 2022UPASG011⟩. ⟨tel-03689701⟩



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