Simulating the dynamics of the mechanochemical cycle of myosin-V.
Ontology highlight
ABSTRACT:
SUBMITTER: Mukherjee S
PROVIDER: S-EPMC5338489 | biostudies-other | 2017 Feb
REPOSITORIES: biostudies-other
Similar Datasets
Project description:To effectively navigate complex tissue microenvironments, immune cells sense molecular concentration gradients using G-protein coupled receptors. However, due to the complexity of receptor activity, and the multimodal nature of chemokine gradients in vivo, chemokine receptor activity in situ is poorly understood. To address this issue, we apply a modelling and simulation approach that permits analysis of the spatiotemporal dynamics of CXCR5 expression within an in silico B-follicle with single-cell resolution. Using this approach, we show that that in silico B-cell scanning is robust to changes in receptor numbers and changes in individual kinetic rates of receptor activity, but sensitive to global perturbations where multiple parameters are altered simultaneously. Through multi-objective optimization analysis we find that the rapid modulation of CXCR5 activity through receptor binding, desensitization and recycling is required for optimal antigen scanning rates. From these analyses we predict that chemokine receptor signaling dynamics regulate migration in complex tissue microenvironments to a greater extent than the total numbers of receptors on the cell surface.
| S-EPMC8452942 | biostudies-literature
Project description:The cell cycle is driven by precise temporal coordination among many molecular activities. To understand and explore this process, we developed the Cell Cycle Browser (CCB), an interactive web interface based on real-time reporter data collected in proliferating human cells. This tool facilitates visualizing, organizing, simulating, and predicting the outcomes of perturbing cell-cycle parameters. Time-series traces from individual cells can be combined to build a multi-layered timeline of molecular activities. Users can simulate the cell cycle using computational models that capture the dynamics of molecular activities and phase transitions. By adjusting individual expression levels and strengths of molecular relationships, users can predict effects on the cell cycle. Virtual assays, such as growth curves and flow cytometry, provide familiar outputs to compare cell-cycle behaviors for data and simulations. The CCB serves to unify our understanding of cell-cycle dynamics and provides a platform for generating hypotheses through virtual experiments.
| S-EPMC6214685 | biostudies-literature
Project description:MotivationMany important aspects of evolutionary dynamics can only be addressed through simulations. However, accurate simulations of realistically large populations over long periods of time needed for evolution to proceed are computationally expensive. Mutants can be present in very small numbers and yet (if they are more fit than others) be the key part of the evolutionary process. This leads to significant stochasticity that needs to be accounted for. Different evolutionary events occur at very different time scales: mutations are typically much rarer than reproduction and deaths.ResultsWe introduce a new exact algorithm for fast fully stochastic simulations of evolutionary dynamics that include birth, death and mutation events. It produces a significant speedup compared to direct stochastic simulations in a typical case when the population size is large and the mutation rates are much smaller than birth and death rates. The algorithm performance is illustrated by several examples that include evolution on a smooth and rugged fitness landscape. We also show how this algorithm can be adapted for approximate simulations of more complex evolutionary problems and illustrate it by simulations of a stochastic competitive growth model.
| S-EPMC3338019 | biostudies-literature
Project description:Atomistic molecular dynamics (MD) simulations have become an indispensable tool for investigating the structure, dynamics, and energetics of biomolecules. Continual optimization of software algorithms and hardware has enabled investigators to access biologically relevant time scales in feasible amounts of computing time. Given the widespread use and utility of MD simulations, there is considerable interest in learning essential skills in performing them. Here, we present a set of introductory tutorials for performing MD simulations of proteins in the popular, open-source GROMACS package. Three exercises are detailed, including simulating a single protein, setting up a protein complex, and performing umbrella sampling simulations to model the unfolding of a short polypeptide. Essential features and input settings are illustrated throughout. The purpose of these tutorials is to provide new users with a general understanding of foundational workflows, from which they can design their own simulations.
| S-EPMC11457149 | biostudies-literature
Project description:Cardiac fluid dynamics fundamentally involves interactions between complex blood flows and the structural deformations of the muscular heart walls and the thin valve leaflets. There has been longstanding scientific, engineering, and medical interest in creating mathematical models of the heart that capture, explain, and predict these fluid-structure interactions (FSIs). However, existing computational models that account for interactions among the blood, the actively contracting myocardium, and the valves are limited in their abilities to predict valve performance, capture fine-scale flow features, or use realistic descriptions of tissue biomechanics. Here we introduce and benchmark a comprehensive mathematical model of cardiac FSI in the human heart. A unique feature of our model is that it incorporates biomechanically detailed descriptions of all major cardiac structures that are calibrated using tensile tests of human tissue specimens to reflect the heart's microstructure. Further, it is the first FSI model of the heart that provides anatomically and physiologically detailed representations of all four cardiac valves. We demonstrate that this integrative model generates physiologic dynamics, including realistic pressure-volume loops that automatically capture isovolumetric contraction and relaxation, and that its responses to changes in loading conditions are consistent with the Frank-Starling mechanism. These complex relationships emerge intrinsically from interactions within our comprehensive description of cardiac physiology. Such models can serve as tools for predicting the impacts of medical interventions. They also can provide platforms for mechanistic studies of cardiac pathophysiology and dysfunction, including congenital defects, cardiomyopathies, and heart failure, that are difficult or impossible to perform in patients.
| S-EPMC11492567 | biostudies-literature
Project description:Anaphase in epithelia typically does not ensue until after spindles have achieved a characteristic position and orientation, but how or even if cells link spindle position to anaphase onset is unknown. Here, we show that myosin-10 (Myo10), a motor protein involved in epithelial spindle dynamics, binds to Wee1, a conserved regulator of cyclin-dependent kinase 1 (Cdk1). Wee1 inhibition accelerates progression through metaphase and disrupts normal spindle dynamics, whereas perturbing Myo10 function delays anaphase onset in a Wee1-dependent manner. Moreover, Myo10 perturbation increases Wee1-mediated inhibitory phosphorylation on Cdk1, which, unexpectedly, concentrates at cell-cell junctions. Based on these and other results, we propose a model in which the Myo10-Wee1 interaction coordinates attainment of spindle position and orientation with anaphase onset.
| S-EPMC5839792 | biostudies-literature
Project description:At the latest count the myosin family includes 35 distinct groups, all of which have the conserved myosin motor domain attached to a neck or lever arm, followed by a highly variable tail or cargo binding region. The motor domain has an ATPase activity that is activated by the presence of actin. One feature of the myosin ATPase cycle is that it involves an association/dissociation with actin for each ATP hydrolysed. The cycle has been described in detail for a large number of myosins from different classes. In each case the cycle is similar, but the balance between the different molecular events in the cycle has been altered to produce a range of very different mechanical activities. Myosin may spend most of the ATPase cycle attached to actin (high duty ratio), as in the processive myosin (e.g. myosin V) or the strain-sensing myosins (e.g. myosin 1c). In contrast, most muscle myosins spend 80% of their ATPase cycle detached from actin. Within the myosin IIs found in human muscle, there are 11 different sarcomeric myosin isoforms, two smooth muscle isoforms as well as three non-muscle isoforms. We have been exploring how the different myosin isoforms have adapted the cross-bridge cycle to generate different types of mechanical activity and how this goes wrong in inherited myopathies. The ideas are outlined here.
| S-EPMC6514470 | biostudies-literature
Project description:The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.
| S-EPMC7859219 | biostudies-literature
Project description:Motivation:Targeted sequencing using capture probes has become increasingly popular in clinical applications due to its scalability and cost-effectiveness. The approach also allows for higher sequencing coverage of the targeted regions resulting in better analysis statistical power. However, because of the dynamics of the hybridization process, it is difficult to evaluate the efficiency of the probe design prior to the experiments which are time consuming and costly. Results:We developed CapSim, a software package for simulation of targeted sequencing. Given a genome sequence and a set of probes, CapSim simulates the fragmentation, the dynamics of probe hybridization and the sequencing of the captured fragments on Illumina and PacBio sequencing platforms. The simulated data can be used for evaluating the performance of the analysis pipeline, as well as the efficiency of the probe design. Parameters of the various stages in the sequencing process can also be evaluated in order to optimize the experiments. Availability and implementation:CapSim is publicly available under BSD license at https://github.com/Devika1/capsim. Contact:l.coin@imb.uq.edu.au. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6192212 | biostudies-literature
Project description:We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution.
| S-EPMC3865918 | biostudies-literature