Control of Biological Systems
Z. Ghassemi Zahan; S. Ozgoli; S. Bolouki
Abstract
Background and Objectives: In genetic network control, RC-Centrality is introduced as a new control centrality measure to address the control of linear time-invariant networks. The objective of this study is to propose an optimal control centrality metric that quantifies the centrality of individual ...
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Background and Objectives: In genetic network control, RC-Centrality is introduced as a new control centrality measure to address the control of linear time-invariant networks. The objective of this study is to propose an optimal control centrality metric that quantifies the centrality of individual nodes or groups of nodes within a network. Specifically, RC-Centrality identifies key nodes or node groups that can act as controllers, such as genes regulating the gene expression process. To assess the effectiveness of this method, RC-Centrality is compared with standard centralities in a real genetic network. Additionally, the research delves into the role of uncertainty structure in altering the priority order of RC-Centrality.Methods: The RC-Centrality measure is introduced based on an optimal control problem to address weighted, directed, and signed networks. Robust controllers are designed to ensure Lyapunov stability under uncertainty. A cost function is introduced to measure the performance metric represented by input energy in the presence of uncertainty.Results: The study presents RC-Centrality as an effective measure for identifying key nodes in genetic networks suitable for control. In-silico simulations are conducted to evaluate its performance in comparison to standard centralities. The research highlights the impact of uncertainty structure on the priority of RC-Centrality.Conclusion: RC-Centrality offers a promising approach to identify essential nodes in genetic networks for control purposes. Its performance is demonstrated through simulations, and the study emphasizes the influence of uncertainty structure on the centrality measure's prioritization. This research has implications for understanding and controlling genetic networks, particularly in the presence of uncertainty.
Control of Biological Systems
M. Mohammadian
Abstract
Background and Objectives: Regulation of protein expression in cellular level are so challenging. In cellular scale, biochemical processes are intrinsically noisy and many convenient controllers aren’t physically implementable.Methods: In this paper, we consider standard Lyapunov function and by ...
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Background and Objectives: Regulation of protein expression in cellular level are so challenging. In cellular scale, biochemical processes are intrinsically noisy and many convenient controllers aren’t physically implementable.Methods: In this paper, we consider standard Lyapunov function and by using Ito formula and stochastic analysis, we derive sufficient conditions for noise to state stability presented in the form of matrix inequalities. In the next step, by defining appropriate change of variables, matrix inequalities are transformed to Linear matrix inequalities which can be used to synthesize controller with the desired structure.Results: This paper deals with the design of implementable controller for stochastic gene regulatory networks with multiplicative and additive noises. In particular, we consider structural limitations that are present in real cellular systems and design the decentralized feedback that guarantees noise to state stability. Since the proposed conditions for controller design are in the form of linear matrix inequalities, controller gains can be derived efficiently through solving presented LMIs numerically. It is noteworthy that Because of its simple structure, the proposed controller can be implemented universally in many cells. Moreover, we consider a synthetic gene regulatory networks and investigate the effectiveness of the proposed controller by simulations.Conclusion: Our results provide a new method for designing Decentralized controller in gene regulatory networks with intrinsic and extrinsic noises. the proposed controller can be easily implemented in cellular environment.
