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Computation, Volume 2, Issue 2 (June 2014), Pages 23-60

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Research

Open AccessArticle A 3-D Model of a Perennial Ryegrass Primary Cell Wall and Its Enzymatic Degradation
Computation 2014, 2(2), 23-46; doi:10.3390/computation2020023
Received: 23 December 2013 / Revised: 2 April 2014 / Accepted: 17 April 2014 / Published: 5 May 2014
Cited by 1 | PDF Full-text (3484 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We have developed a novel 3-D, agent-based model of cell-wall digestion to improve our understanding of ruminal cell-wall digestion. It offers a capability to study cell walls and their enzymatic modification, by providing a representation of cellulose microfibrils and non-cellulosic polysaccharides and [...] Read more.
We have developed a novel 3-D, agent-based model of cell-wall digestion to improve our understanding of ruminal cell-wall digestion. It offers a capability to study cell walls and their enzymatic modification, by providing a representation of cellulose microfibrils and non-cellulosic polysaccharides and by simulating their spatial and catalytic interactions with enzymes. One can vary cell-wall composition and the types and numbers of enzyme molecules, allowing the model to be applied to a range of systems where cell walls are degraded and to the modification of cell walls by endogenous enzymes. As a proof of principle, we have modelled the wall of a mesophyll cell from the leaf of perennial ryegrass and then simulated its enzymatic degradation. This is a primary, non-lignified cell wall and the model includes cellulose, hemicelluloses (glucuronoarabinoxylans, 1,3;1,4-β-glucans, and xyloglucans) and pectin. These polymers are represented at the level of constituent monosaccharides, and assembled to form a 3-D, meso-scale representation of the molecular structure of the cell wall. The composition of the cell wall can be parameterised to represent different walls in different cell types and taxa. The model can contain arbitrary combinations of different enzymes. It simulates their random diffusion through the polymer networks taking collisions into account, allowing steric hindrance from cell-wall polymers to be modelled. Steric considerations are included when target bonds are encountered, and breakdown products resulting from enzymatic activity are predicted. Full article
(This article belongs to the Special Issue Multiscale Modeling and Simulation in Computational Biology)
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Open AccessArticle Can the Thermodynamic Hodgkin-Huxley Model of Voltage-Dependent Conductance Extrapolate for Temperature?
Computation 2014, 2(2), 47-60; doi:10.3390/computation2020047
Received: 25 February 2014 / Revised: 12 April 2014 / Accepted: 4 May 2014 / Published: 14 May 2014
PDF Full-text (1625 KB) | HTML Full-text | XML Full-text
Abstract
Hodgkin and Huxley (H-H) fitted their model of voltage-dependent conductances to experimental data using empirical functions of voltage. The thermodynamic H-H model of voltage dependent conductances is more physically plausible, as it constrains and parameterises its empirical fit by assuming that ion [...] Read more.
Hodgkin and Huxley (H-H) fitted their model of voltage-dependent conductances to experimental data using empirical functions of voltage. The thermodynamic H-H model of voltage dependent conductances is more physically plausible, as it constrains and parameterises its empirical fit by assuming that ion channel transition rates depend exponentially on a free energy barrier that in turn, linearly or non-linearly, depends on voltage. The original H-H model contains no explicit temperature terms and requires Q10 factors to describe data at different temperatures. The thermodynamic H-H model does have explicit terms for temperature. Do these endow the model with extrapolation for temperature? We utilised voltage clamp data for a voltage-gated K+ current, recorded at three different temperatures. The thermodynamic H-H model’s free parameters were fitted (Marquardt-Levenberg algorithm) to a data set recorded at one (or more) temperature(s). Then we assessed whether it could describe another data set, recorded at a different temperature, with these same free parameter values and its temperature terms set to the new temperature. We found that it could not. Full article
(This article belongs to the Section Computational Biology)
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