journals2017.bib

@article{Gaw17a,
  author = {P. J. Gawthrop},
  journal = {IEEE Transactions on NanoBioscience},
  title = {Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction},
  year = 2017,
  volume = 16,
  number = 3,
  pages = {177-188},
  abstract = { Engineering systems modeling and analysis based on the bond
                  graph approach has been applied to biomolecular
                  systems. In this context, the notion of a
                  Faraday-equivalent 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 half-reactions, 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 Faraday-equivalent 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 Faraday-equivalent 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},
  doi = {10.1109/TNB.2017.2674683},
  issn = {1536-1241},
  month = {April},
  archiveprefix = {arXiv},
  eprint = {1611.04264},
  note = {Available at {arXiv:1611.04264}}
}
@article{GawCra17,
  author = {Gawthrop, Peter J. and Crampin, Edmund J.},
  title = {Energy-based analysis of biomolecular pathways},
  volume = 473,
  number = 2202,
  year = 2017,
  doi = {10.1098/rspa.2016.0825},
  publisher = {The Royal Society},
  archiveprefix = {arXiv},
  eprint = {1611.02332},
  note = {Available at {arXiv:1611.02332}},
  abstract = {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 steady-state mass flows (reaction rates) through the network. In this work, we show how pathway analysis of biomolecular networks can be extended using an energy-based approach to provide information about energy flows through the network. This energy-based approach is developed using the engineering-inspired 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 [sodium-glucose 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.},
  issn = {1364-5021},
  journal = {Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences}
}
@article{GawSieKam17,
  author = {P. J. Gawthrop and I. Siekmann and T. Kameneva and S. Saha and M. R. Ibbotson and E. J. Crampin},
  journal = {IET Systems Biology},
  title = {Bond graph modelling of chemoelectrical energy transduction},
  year = 2017,
  volume = 11,
  number = 5,
  pages = {127-138},
  abstract = {Energy-based 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 well-known 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 trade-off 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 energy-based model of action potentials. As the energy-based 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;energy-based bond graph modelling;excitable membranes;healthy neurons;healthy retinal ganglion cells;integrated thermodynamically compliant modelling;membrane action potential},
  doi = {10.1049/iet-syb.2017.0006},
  issn = {1751-8849},
  archiveprefix = {arXiv},
  eprint = {1512.00956},
  note = {Available at {arXiv:1512.00956}}
}
@article{GolGawLakLor17,
  author = {Gollee, Henrik and Gawthrop, Peter J. and Lakie, Martin and Loram, Ian D.},
  title = {Visuo-manual tracking: does intermittent control with aperiodic sampling explain linear power and non-linear remnant without sensorimotor noise?},
  journal = {The Journal of Physiology},
  volume = 595,
  number = 21,
  issn = {1469-7793},
  doi = {10.1113/JP274288},
  pages = {6751--6770},
  keywords = {motor control, intermittent control, variability},
  year = 2017,
  abstract = {
The human operator is described adequately by linear translation of sensory input to motor output. Motor output also always includes a non-linear remnant resulting from random sensorimotor noise from multiple sources, and non-linear 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 non-linear remnant using noise or non-linear transformations? (ii) Can non-linear 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 multi-sine disturbance. Joystick power was analysed using three models, continuous-linear-control (CC), continuous-linear-control 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 visuo-manual tracking.
}
}
@article{Gaw17cX,
  author = {{Gawthrop}, P.~J.},
  title = {{Sensitivity Properties of Intermittent Control}},
  journal = {ArXiv e-prints},
  archiveprefix = {arXiv},
  eprint = {1705.08228},
  keywords = {Computer Science - Systems and Control, Quantitative Biology - Quantitative Methods},
  year = 2017,
  month = may,
  note = {Available at {arXiv:1705.08228}}
}

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