A Closed-Loop Control Scheme for Steering Steady States of Glycolysis and Glycogenolysis Pathway

Biochemical networks normally operate in the neighborhood of one of its multiple steady states. It may reach from one steady state to other within a finite time span. In this paper, a closed-loop control scheme is proposed to steer states of the glycolysis and glycogenolysis (GG) pathway from one of...

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Bibliographic Details
Published inIEEE/ACM transactions on computational biology and bioinformatics Vol. 10; no. 4; pp. 858 - 868
Main Authors Panja, Surajit, Patra, Sourav, Mukherjee, Anirban, Basu, Madhumita, Sengupta, Sanghamitra, Dutta, Pranab K.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2013
Subjects
Biological system modeling
Computational biology
Computational Biology - methods
Computer Simulation
Controllability
Feedback
feedback linearization
glycogenolysis
Glycogenolysis - physiology
glycolysis
Glycolysis - physiology
Linear Models
linear quadratic gaussian regulator
Linearization techniques
Mathematical model
Models, Biological
Nonlinear systems
S-system
Steady-state
Vectors
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Summary:Biochemical networks normally operate in the neighborhood of one of its multiple steady states. It may reach from one steady state to other within a finite time span. In this paper, a closed-loop control scheme is proposed to steer states of the glycolysis and glycogenolysis (GG) pathway from one of its steady states to other. The GG pathway is modeled in the synergism and saturation system formalism, known as S-system. This S-system model is linearized into the controllable Brunovsky canonical form using a feedback linearization technique. For closed-loop control, the linear-quadratic regulator (LQR) and the linear-quadratic gaussian (LQG) regulator are invoked to design a controller for tracking prespecified steady states. In the feedback linearization technique, a global diffeomorphism function is proposed that facilitates in achieving the regulation requirement. The robustness of the regulated GG pathway is studied considering input perturbation and with measurement noise.
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2013.82