Systems Biology, Synthetic Biology and Control Theory: A promising golden braid

Cury, J.E.R.; Baldissera, Fabio L.

doi:10.1016/j.arcontrol.2013.03.006

Cited by 19 publications

(9 citation statements)

References 51 publications

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“…Applications of control theory to biological systems have multiplied in recent years (Lenhart and Workman 2007 ; Todorov and Jordan 2002 ; Mitchinson et al. 2007 ), particularly in connection with systems biology and synthetic biology (Sontag 2004 ; Iglesias and Ingalls 2010 ; Cury and Baldissera 2013 ; Del Vecchio and Murray 2014 ). But control theoretical analysis is complicated by the presence of nonlinearities, nonsmooth bifurcations, nonstationarities, and stochastic effects ubiquitous in biological control systems (Davis 1984 ; Isidori 1995 ; Åström 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Robustness, flexibility, and sensitivity in a multifunctional motor control model

et al. 2016

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Motor systems must adapt to perturbations and changing conditions both within and outside the body. We refer to the ability of a system to maintain performance despite perturbations as “robustness,” and the ability of a system to deploy alternative strategies that improve fitness as “flexibility.” Different classes of pattern-generating circuits yield dynamics with differential sensitivities to perturbations and parameter variation. Depending on the task and the type of perturbation, high sensitivity can either facilitate or hinder robustness and flexibility. Here we explore the role of multiple coexisting oscillatory modes and sensory feedback in allowing multiphasic motor pattern generation to be both robust and flexible. As a concrete example, we focus on a nominal neuromechanical model of triphasic motor patterns in the feeding apparatus of the marine mollusk Aplysia californica. We find that the model can operate within two distinct oscillatory modes and that the system exhibits bistability between the two. In the “heteroclinic mode,” higher sensitivity makes the system more robust to changing mechanical loads, but less robust to internal parameter variations. In the “limit cycle mode,” lower sensitivity makes the system more robust to changes in internal parameter values, but less robust to changes in mechanical load. Finally, we show that overall performance on a variable feeding task is improved when the system can flexibly transition between oscillatory modes in response to the changing demands of the task. Thus, our results suggest that the interplay of sensory feedback and multiple oscillatory modes can allow motor systems to be both robust and flexible in a variable environment.

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Section: Discussionmentioning

confidence: 99%

Robustness, flexibility, and sensitivity in a multifunctional motor control model

et al. 2016

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“…We define the transition graph Υ = (V, A) as follow where the vertices V corresponds to each possible configuration of the region ω and A is the set of arcs. Let v 1 and v 2 be two vertices in V , there is an arc from the vertex v 1 to the vertex v 2 if there exists a control u = ( , r) ∈ {(0, 0); (0, 1); (1, 0); (1,1)…”

Section: Transition Graph Approach and Regional Controllability Problemmentioning

confidence: 99%

“…These concepts are being increasingly useful in a wide range of applications such as: biology, biochemistry, biomedical engineering, ecology, economics etc. [1,2]. Controllable and observable systems have been characterized so far using the Kalman condition in the linear case.…”

Section: Introductionmentioning

confidence: 99%

A graph theory approach for regional controllability of Boolean cellular automata

Dridi

Yacoubi

Bagnoli

et al. 2019

International Journal of Parallel, Emergent and Distributed Sys

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Controllability is one of the central concepts of modern control theory that allows a good understanding of a system's behaviour. It consists in constraining a system to reach the desired state from an initial state within a given time interval. When the desired objective affects only a sub-region of the domain, the control is said to be regional. The purpose of this paper is to study a particular case of regional control using cellular automata models since they are spatially extended systems where spatial properties can be easily defined thanks to their intrinsic locality. We investigate the case of boundary controls on the target region using an original approach based on graph theory. Necessary and sufficient conditions are given based on the Hamiltonian Circuit and strongly connected component. The controls are obtained using a preimage approach.

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“…When the biological systems have been modeled, the next task is to derive a control algorithm that forces the system to meet the required specification. Scientists are often faced with different challenges, such as non-observable or non-controllable biological events (Cury (2013)).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Biological Smooth Oscillators Inspired by the Relay Control Tuning Method

Zhang

Chen

2014

IFAC Proceedings Volumes

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Systems Biology, Synthetic Biology and Control Theory: A promising golden braid

Cited by 19 publications

References 51 publications

Robustness, flexibility, and sensitivity in a multifunctional motor control model

Robustness, flexibility, and sensitivity in a multifunctional motor control model

A graph theory approach for regional controllability of Boolean cellular automata

Analysis of Biological Smooth Oscillators Inspired by the Relay Control Tuning Method

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