How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

<p>Abstract</p> <p>Background</p> <p>Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Coarsening Dynamics of a One-Dimensional Driven Cahn-Hilliard System

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.Comment: 20 pages, revtex, 10 figures, submitted to Phys. Rev.

Off-grid solar photovoltaic systems for rural electrification and emissions mitigation in India

Over one billion people lack access to electricity and many of them in rural areas far from existing infrastructure. Off-grid systems can provide an alternative to extending the grid network and using renewable energy, for example solar photovoltaics (PV) and battery storage, can mitigate greenhouse gas emissions from electricity that would otherwise come from fossil fuel sources. This paper presents a model capable of comparing several mature and emerging PV technologies for rural electrification with diesel generation and grid extension for locations in India in terms of both the levelised cost and lifecycle emissions intensity of electricity. The levelised cost of used electricity, ranging from $0.46–1.20/kWh, and greenhouse gas emissions are highly dependent on the PV technology chosen, with battery storage contributing significantly to both metrics. The conditions under which PV and storage becomes more favourable than grid extension are calculated and hybrid systems of PV, storage and diesel generation are evaluated. Analysis of expected price evolutions suggest that the most cost-effective hybrid systems will be dominated by PV generation around 2018

Adenovirus type 5 E4 Orf3 protein targets promyelocytic leukaemia (PML) protein nuclear domains for disruption via a sequence in PML isoform II that is predicted as a protein interaction site by bioinformatic analysis

Human adenovirus type 5 infection causes the disruption of structures in the cell nucleus termed promyelocytic leukaemia (PML) protein nuclear domains or ND10, which contain the PML protein as a critical component. This disruption is achieved through the action of the viral E4 Orf3 protein, which forms track-like nuclear structures that associate with the PML protein. This association is mediated by a direct interaction of Orf3 with a specific PML isoform, PMLII. We show here that the Orf3 interaction properties of PMLII are conferred by a 40 aa residue segment of the unique C-terminal domain of the protein. This segment was sufficient to confer interaction on a heterologous protein. The analysis was informed by prior application of a bioinformatic tool for the prediction of potential protein interaction sites within unstructured protein sequences (predictors of naturally disordered region analysis; PONDR). This tool predicted three potential molecular recognition elements (MoRE) within the C-terminal domain of PMLII, one of which was found to form the core of the Orf3 interaction site, thus demonstrating the utility of this approach. The sequence of the mapped Orf3-binding site on PML protein was found to be relatively poorly conserved across other species; however, the overall organization of MoREs within unstructured sequence was retained, suggesting the potential for conservation of functional interactions

Norovirus-mediated modification of the translational landscape via virus and host-induced cleavage of translation initiation factors

Noroviruses produce viral RNAs lacking a 5' cap structure and instead use a virus-encoded VPg protein covalently linked to viral RNA to interact with translation initiation factors and drive viral protein synthesis. Norovirus infection results in the induction of the innate response leading to interferon stimulated gene (ISG) transcription. However the translation of the induced ISG mRNAs is suppressed. A SILAC-based mass spectrometry approach was employed to analyse changes to protein abundance in both whole cell and m7GTP-enriched samples to demonstrate that diminished host mRNA translation correlates with changes to the composition of the eukaryotic initiation factor complex. The suppression of host ISG translation correlates with the activity of the viral protease (NS6) and the activation of cellular caspases leading to the establishment of an apoptotic environment. These results indicate that noroviruses exploit the differences between viral VPg-dependent and cellular cap-dependent translation in order to diminish the host response to infection.This work was supported by grants from the Wellcome Trust (097997/Z/11/Z, 101602/Z/13/Z) and BBSRC (Refs: BB/N001176/1 and BB/K002465/1) to IG, and an equipment grant to KH, IG (and others) from the Wellcome Trust (104914/Z/14/Z). RL is supported by a grant from the National Institutes for Health of the United States of America (AI50237). NL is supported by a BBSRC grant (BB/I01232X/1). IG is a Wellcome Senior Fellow. This work was also supported by the Intramural Research Program of the NIH, NIAID

The Effect of Shear on Phase-Ordering Dynamics with Order-Parameter-Dependent Mobility: The Large-n Limit

The effect of shear on the ordering-kinetics of a conserved order-parameter system with O(n) symmetry and order-parameter-dependent mobility \Gamma({\vec\phi}) \propto (1- {\vec\phi} ^2/n)^\alpha is studied analytically within the large-n limit. In the late stage, the structure factor becomes anisotropic and exhibits multiscaling behavior with characteristic length scales (t^{2\alpha+5}/\ln t)^{1/2(\alpha+2)} in the flow direction and (t/\ln t)^{1/2(\alpha+2)} in directions perpendicular to the flow. As in the \alpha=0 case, the structure factor in the shear-flow plane has two parallel ridges.Comment: 6 pages, 2 figure

Enhancement and suppression effects resulting from information structuring in sentences

Information structuring through the use of cleft sentences increases the processing efficiency of references to elements within the scope of focus. Furthermore, there is evidence that putting certain types of emphasis on individual words not only enhances their subsequent processing, but also protects these words from becoming suppressed in the wake of subsequent information, suggesting mechanisms of enhancement and suppression. In Experiment 1, we showed that clefted constructions facilitate the integration of subsequent sentences that make reference to elements within the scope of focus, and that they decrease the efficiency with reference to elements outside of the scope of focus. In Experiment 2, using an auditory text-change-detection paradigm, we showed that focus has similar effects on the strength of memory representations. These results add to the evidence for enhancement and suppression as mechanisms of sentence processing and clarify that the effects occur within sentences having a marked focus structure

Corrections to Scaling in the Phase-Ordering Dynamics of a Vector Order Parameter

Corrections to scaling, associated with deviations of the order parameter from the scaling morphology in the initial state, are studied for systems with O(n) symmetry at zero temperature in phase-ordering kinetics. Including corrections to scaling, the equal-time pair correlation function has the form C(r,t) = f_0(r/L) + L^{-omega} f_1(r/L) + ..., where L is the coarsening length scale. The correction-to-scaling exponent, omega, and the correction-to-scaling function, f_1(x), are calculated for both nonconserved and conserved order parameter systems using the approximate Gaussian closure theory of Mazenko. In general, omega is a non-trivial exponent which depends on both the dimensionality, d, of the system and the number of components, n, of the order parameter. Corrections to scaling are also calculated for the nonconserved 1-d XY model, where an exact solution is possible.Comment: REVTeX, 20 pages, 2 figure

Changing how Earth System Modelling is done to provide more useful information for decision making, science and society

New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies. The present bias toward increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model tractability. This bias makes it difficult to quantify the uncertainty associated with the projections from any one model or to the distribution of projections from different models. This in turn limits the utility of climate model outputs for deriving useful information such as in the design of effective climate change mitigation and adaptation strategies or identifying and prioritizing sources of uncertainty for reduction. Here we argue that a new approach to model development is needed, focused on the delivery of information to support specific policy decisions or science questions. The central tenet of this approach is the assessment and justification of the overall balance of model detail that reflects the question posed, current knowledge, available data, and sources of uncertainty. This differs from contemporary practices by explicitly seeking to quantify both the benefits and costs of details at a systemic level, taking into account the precision and accuracy with which predictions are made when compared to existing empirical evidence. We specify changes to contemporary model development practices that would help in achieving this goal.</jats:p

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

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