[1] 
P. J. Gawthrop.
Bond graph modeling of chemiosmotic biomolecular energy transduction.
IEEE Transactions on NanoBioscience, 16(3):177188, April
2017.
Available at arXiv:1611.04264.
[ bib 
DOI 
arXiv ]
Engineering systems modeling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faradayequivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by halfreactions, are key components of biological systems which involve both electrical and chemical domains. A bond graph interpretation of redox reactions is given which combines bond graphs with the Faradayequivalent chemical potential. This approach is particularly relevant when the biomolecular system implements chemoelectrical transduction – for example chemiosmosis within the key metabolic pathway of mitochondria: oxidative phosphorylation. An alternative way of implementing computational modularity using bond graphs is introduced and used to give a physically based model of the mitochondrial electron transport chain To illustrate the overall approach, this model is analyzed using the Faradayequivalent chemical potential approach and engineering intuition is used to guide affinity equalisation: a energy based analysis of the mitochondrial electron transport chain. Keywords: Analytical models;Biological system modeling;Chemicals;Computational modeling;Context;Electric potential;Protons;Biological system modeling;computational systems biology;systems biology 
[2] 
P. J. Gawthrop.
Sensitivity Properties of Intermittent Control.
ArXiv eprints, May 2017.
Available at arXiv:1705.08228.
[ bib 
arXiv ]
Keywords: Computer Science  Systems and Control, Quantitative Biology  Quantitative Methods 
[3] 
Peter J. Gawthrop and Edmund J. Crampin.
Energybased analysis of biomolecular pathways.
Proceedings of the Royal Society of London A: Mathematical,
Physical and Engineering Sciences, 473(2202), 2017.
Available at arXiv:1611.02332.
[ bib 
DOI 
arXiv ]
Decomposition of biomolecular reaction networks into pathways is a powerful approach to the analysis of metabolic and signalling networks. Current approaches based on analysis of the stoichiometric matrix reveal information about steadystate mass flows (reaction rates) through the network. In this work, we show how pathway analysis of biomolecular networks can be extended using an energybased approach to provide information about energy flows through the network. This energybased approach is developed using the engineeringinspired bond graph methodology to represent biomolecular reaction networks. The approach is introduced using glycolysis as an exemplar; and is then applied to analyse the efficiency of free energy transduction in a biomolecular cycle model of a transporter protein [sodiumglucose transport protein 1 (SGLT1)]. The overall aim of our work is to present a framework for modelling and analysis of biomolecular reactions and processes which considers energy flows and losses as well as mass transport.

[4] 
P. J. Gawthrop, I. Siekmann, T. Kameneva, S. Saha, M. R. Ibbotson, and E. J.
Crampin.
Bond graph modelling of chemoelectrical energy transduction.
IET Systems Biology, 11(5):127138, 2017.
Available at arXiv:1512.00956.
[ bib 
DOI 
arXiv ]
Energybased bond graph modelling of biomolecular systems is extended to include chemoelectrical transduction thus enabling integrated thermodynamically compliant modelling of chemoelectrical systems in general and excitable membranes in particular. Our general approach is illustrated by recreating a wellknown model of an excitable membrane. This model is used to investigate the energy consumed during a membrane action potential thus contributing to the current debate on the tradeoff between the speed of an action potential event and energy consumption. The influx of Na+ is often taken as a proxy for energy consumption; in contrast, this study presents an energybased model of action potentials. As the energybased approach avoids the assumptions underlying the proxy approach it can be directly used to compute energy consumption in both healthy and diseased neurons. These results are illustrated by comparing the energy consumption of healthy and degenerative retinal ganglion cells using both simulated and in vitro data. Keywords: biochemistry;bioelectric potentials;biomembrane transport;eye;molecular biophysics;neurophysiology;sodium;Na;biomolecular systems;chemoelectrical energy transduction;chemoelectrical systems;degenerative retinal ganglion cells;diseased neurons;energy consumption;energybased bond graph modelling;excitable membranes;healthy neurons;healthy retinal ganglion cells;integrated thermodynamically compliant modelling;membrane action potential 
[5] 
Henrik Gollee, Peter J. Gawthrop, Martin Lakie, and Ian D. Loram.
Visuomanual tracking: does intermittent control with aperiodic
sampling explain linear power and nonlinear remnant without sensorimotor
noise?
The Journal of Physiology, 595(21):67516770, 2017.
[ bib 
DOI ]
The human operator is described adequately by linear translation of sensory input to motor output. Motor output also always includes a nonlinear remnant resulting from random sensorimotor noise from multiple sources, and nonlinear input transformations, for example thresholds or refractory periods. Recent evidence showed that manual tracking incurs substantial, serial, refractoriness (insensitivity to sensory information of 350 and 550 ms for 1st and 2nd order systems respectively). Our two questions are: (i) What are the comparative merits of explaining the nonlinear remnant using noise or nonlinear transformations? (ii) Can nonlinear transformations represent serial motor decision making within the sensorimotor feedback loop intrinsic to tracking? Twelve participants (instructed to act in three prescribed ways) manually controlled two systems (1st and 2nd order) subject to a periodic multisine disturbance. Joystick power was analysed using three models, continuouslinearcontrol (CC), continuouslinearcontrol with calculated noise spectrum (CCN), and intermittent control with aperiodic sampling triggered by prediction error thresholds (IC). Unlike the linear mechanism, the intermittent control mechanism explained the majority of total power (linear and remnant) (77–87 vs. 8–48, IC vs. CC). Between conditions, IC used thresholds and distributions of open loop intervals consistent with, respectively, instructions and previous measured, model independent values; whereas CCN required changes in noise spectrum deviating from broadband, signal dependent noise. We conclude that manual tracking uses open loop predictive control with aperiodic sampling. Because aperiodic sampling is inherent to serial decision making within previously identified, specific frontal, striatal and parietal networks we suggest that these structures are intimately involved in visuomanual tracking. Keywords: motor control, intermittent control, variability 
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