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Search Results: 1 - 10 of 12099 matches for " Stephen Emmott "
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How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex
Yaki Setty, Chih-Chun Chen, Maria Secrier, Nikita Skoblov, Dimitrios Kalamatianos, Stephen Emmott
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-154
Abstract: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration.We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.Neuronal migration is a highly dynamic process that is essential for the normal development and function of the mammalian brain. The migration process is regulated by cell-extrinsic signaling pathways and cell-intrinsic regulation and
A Peptide Filtering Relation Quantifies MHC Class I Peptide Optimization
Neil Dalchau ,Andrew Phillips ,Leonard D. Goldstein,Mark Howarth,Luca Cardelli,Stephen Emmott,Tim Elliott,Joern M. Werner
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1002144
Abstract: Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein.
Perturbative Corrections to the Ohta-Jasnow-Kawasaki Theory of Phase-Ordering Dynamics
C. L. Emmott
Physics , 1998, DOI: 10.1103/PhysRevE.58.5508
Abstract: A perturbation expansion is considered about the Ohta-Jasnow-Kawasaki theory of phase-ordering dynamics; the non-linear terms neglected in the OJK calculation are reinstated and treated as a perturbation to the linearised equation. The first order correction term to the pair correlation function is calculated in the large-d limit and found to be of order 1/(d^2).
Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model
Michael B. J. Harfoot equal contributor ,Tim Newbold equal contributor,Derek P. Tittensor equal contributor,Stephen Emmott,Jon Hutton,Vassily Lyutsarev,Matthew J. Smith,J?rn P. W. Scharlemann,Drew W. Purves
PLOS Biology , 2014, DOI: 10.1371/journal.pbio.1001841
Abstract: Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 μg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures.
Coarsening Dynamics of a One-Dimensional Driven Cahn-Hilliard System
C. L. Emmott,A. J. Bray
Physics , 1996, DOI: 10.1103/PhysRevE.54.4568
Abstract: We study the one-dimensional Cahn-Hilliard equation with an additional driving term representing, say, the effect of gravity. We find that the driving field $E$ has an asymmetric effect on the solution for a single stationary domain wall (or `kink'), the direction of the field determining whether the analytic solutions found by Leung [J.Stat.Phys.{\bf 61}, 345 (1990)] are unique. The dynamics of a kink-antikink pair (`bubble') is then studied. The behaviour of a bubble is dependent on the relative sizes of a characteristic length scale $E^{-1}$, where $E$ is the driving field, and the separation, $L$, of the interfaces. For $EL \gg 1$ the velocities of the interfaces are negligible, while in the opposite limit a travelling-wave solution is found with a velocity $v \propto E/L$. For this latter case ($EL \ll 1$) a set of reduced equations, describing the evolution of the domain lengths, is obtained for a system with a large number of interfaces, and implies a characteristic length scale growing as $(Et)^{1/2}$. Numerical results for the domain-size distribution and structure factor confirm this behavior, and show that the system exhibits dynamical scaling from very early times.
Lifshitz-Slyozov Scaling For Late-Stage Coarsening With An Order-Parameter-Dependent Mobility
A. J. Bray,C. L. Emmott
Physics , 1995, DOI: 10.1103/PhysRevB.52.R685
Abstract: The coarsening dynamics of the Cahn-Hilliard equation with order-parameter dependent mobility, $\lambda(\phi) \propto (1-\phi^2)^\alpha$, is addressed at zero temperature in the Lifshitz-Slyozov limit where the minority phase occupies a vanishingly small volume fraction. Despite the absence of bulk diffusion for $\alpha>0$, the mean domain size is found to grow as $ \propto t^{1/(3+\alpha)}$, due to subdiffusive transport of the order parameter through the majority phase. The domain-size distribution is determined explicitly for the physically relevant case $\alpha = 1$.
Phase-Ordering Dynamics with an Order-Parameter-Dependent Mobility: The Large-n Limit
C. L. Emmott,A. J. Bray
Physics , 1998, DOI: 10.1103/PhysRevE.59.213
Abstract: The effect of an order-parameter dependent mobility (or kinetic coefficient), on the phase-ordering dynamics of a system described by an n-component vector order parameter is addressed at zero temperature in the large-n limit. We consider cases in which the mobility or kinetic coefficient vanishes when the magnitude of the order parameter takes its equilibrium value. In the large-n limit, the system is exactly soluble for both conserved and non-conserved order parameter. In the non-conserved case, the scaling form for the correlation function and it's Fourier transform, the structure factor, is established, with the characteristic length growing as a power of time. In the conserved case, the structure factor is evaluated and found to exhibit a multi-scaling behaviour, with two growing length scales differing by a logarithmic factor. In both cases, the rate of growth of the length scales depends on the manner in which the mobility or kinetic coefficient vanishes as the magnitude of the order parameter approaches its equilibrium value.
Ten Simple Rules for Effective Computational Research
James M. Osborne ,Miguel O. Bernabeu,Maria Bruna,Ben Calderhead,Jonathan Cooper,Neil Dalchau,Sara-Jane Dunn,Alexander G. Fletcher,Robin Freeman,Derek Groen,Bernhard Knapp,Greg J. McInerny,Gary R. Mirams,Joe Pitt-Francis,Biswa Sengupta,David W. Wright,Christian A. Yates,David J. Gavaghan,Stephen Emmott,Charlotte Deane
PLOS Computational Biology , 2014, DOI: doi/10.1371/journal.pcbi.1003506
The climate dependence of the terrestrial carbon cycle; including parameter and structural uncertainties
M. J. Smith,M. C. Vanderwel,V. Lyutsarev,S. Emmott
Biogeosciences Discussions , 2012, DOI: 10.5194/bgd-9-13439-2012
Abstract: The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System over the coming decades and centuries. However Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Essential to achieving this goal will be assessing the empirical support for alternative models of component processes, identifying key uncertainties and inconsistencies, and ultimately identifying the models that are most consistent with empirical evidence. To begin meeting these requirements we data-constrained all parameters of all component processes within a global terrestrial carbon model. Our goals were to assess the climate dependencies obtained for different component processes when all parameters have been inferred from empirical data, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model, and to identify a methodology by which alternative component models could be compared within the same framework in future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies although it was not possible to data-constrain all parameters indicating some potentially redundant details. Similar climate dependencies were obtained for most processes whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium p
An efficient biologically-inspired photocell enhanced by quantum coherence
C. Creatore,M. A. Parker,S. Emmott,A. W. Chin
Physics , 2013, DOI: 10.1103/PhysRevLett.111.253601
Abstract: Artificially reproducing the biological light reactions responsible for the remarkably efficient photon-to-charge conversion in photosynthetic complexes represents a new direction for the future development of photovoltaic devices. Here, we develop such a paradigm and present a model photocell based on the nanoscale architecture of photosynthetic reaction centres that explicitly harnesses the quantum mechanical effects recently discovered in photosynthetic complexes. Quantum interference of photon absorption/emission induced by the dipole-dipole interaction between molecular excited states guarantees an enhanced light-to-current conversion and power generation for a wide range of realistic parameters, opening a promising new route for designing artificial light-harvesting devices inspired by biological photosynthesis and quantum technologies.
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