Energy-based Feedback Control of Biomolecular Systems with Cyclic Flow Modulation

Energy-based modelling brings engineering insight to the understanding of biomolecular systems. It is shown how well-established control engineering concepts, such as loop-gain, arise from energy feedback loops and are therefore amenable to control engineering insight. The approach is illustrated using a class of metabolic cycles with activation and inhibition leading the concept of Cyclic Flow Modulation.

[1]  Edmund J. Crampin,et al.  Hierarchical bond graph modelling of biochemical networks , 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  M. Esposito,et al.  Irreversible thermodynamics of open chemical networks. I. Emergent cycles and broken conservation laws. , 2014, The Journal of chemical physics.

[3]  David J. Wagg,et al.  Dynamically dual vibration absorbers: a bond graph approach to vibration control , 2015 .

[4]  Peter J. Gawthrop,et al.  Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction , 2016, IEEE Transactions on NanoBioscience.

[5]  B. Bhikkaji,et al.  Physical-model-based control of a piezoelectric tube for nano-scale positioning applications , 2010 .

[6]  David E. Hardt,et al.  Controller Design in the Physical Domain , 1989, 1989 American Control Conference.

[7]  Jinsu Kim,et al.  Absolutely robust controllers for chemical reaction networks , 2019, Journal of the Royal Society Interface.

[8]  H. Qian,et al.  Metabolic futile cycles and their functions: a systems analysis of energy and control. , 2005, Systems biology.

[9]  Edmund J Crampin,et al.  Energy-based analysis of biochemical cycles using bond graphs , 2014, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  N. Lane.  How energy flow shapes cell evolution , 2020, Current Biology.

[11]  G. Oster,et al.  Network Thermodynamics , 1971, Nature.

[12]  Edmund J. Crampin,et al.  Physically-Plausible Modelling of Biomolecular Systems: A Simplified, Energy-Based Model of the Mitochondrial Electron Transport Chain. , 2019, Journal of theoretical biology.

[13]  Martin Feinberg,et al.  Design principles for robust biochemical reaction networks: what works, what cannot work, and what might almost work. , 2011, Mathematical biosciences.

[14]  K. F. Tipton,et al.  Biochemical systems analysis: A study of function and design in molecular biology , 1978 .

[15]  Dean Karnopp,et al.  Bond graphs in control: Physical state variables and observers , 1979 .

[16]  P. Gawthrop.  Physical model-based control: A bond graph approach , 1995 .

[17]  Edmund J Crampin,et al.  Modular bond-graph modelling and analysis of biomolecular systems. , 2015, IET systems biology.

[18]  P. Gawthrop,et al.  Bond Graph Representation of Chemical Reaction Networks , 2018, IEEE Transactions on NanoBioscience.

[19]  D. Vecchio,et al.  Biomolecular Feedback Systems , 2014 .

[20]  B Crabtree,et al.  Substrate cycles: their role in improving sensitivity in metabolic control , 1984 .

[21]  M. Hoagland,et al.  Feedback Systems An Introduction for Scientists and Engineers SECOND EDITION , 2015 .

[22]  A. Perelson.  Network thermodynamics. An overview. , 1975, Biophysical journal.

[23]  M. Khammash,et al.  A universal biomolecular integral feedback controller for robust perfect adaptation , 2019, Nature.

[24]  E. Seibert,et al.  Fundamentals of enzyme kinetics. , 2014, Methods in molecular biology.

[25]  Arjan van der Schaft,et al.  On the Mathematical Structure of Balanced Chemical Reaction Networks Governed by Mass Action Kinetics , 2011, SIAM J. Appl. Math..

[26]  Orkun S. Soyer,et al.  Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis , 2019, bioRxiv.

[27]  Peter J. Gawthrop,et al.  Bond graph based control and substructuring , 2009, Simul. Model. Pract. Theory.

[28]  Mathieu Cloutier,et al.  The control systems structures of energy metabolism , 2010, Journal of The Royal Society Interface.