scholarly journals Stochastic dynamics of macromolecular‐assembly networks

2006 ◽  
Vol 2 (1) ◽  
Author(s):  
Leonor Saiz ◽  
Jose MG Vilar
Keyword(s):  
Stochastic Dynamics ◽  
Macromolecular Assembly

High resolution spot scan imaging of frozen, hydrated actin bundles with 400kV electrons

1992 ◽  
Vol 50 (1) ◽  
pp. 512-513
Author(s):  
J. Jakana ◽  
M.F. Schmid ◽  
P. Matsudaira ◽  
W. Chiu
Keyword(s):  
High Resolution ◽  
Binding Proteins ◽  
Actin Binding ◽  
Eukaryotic Cells ◽  
Dimensional Structure ◽  
Structural Basis ◽  
Actin Bundle ◽  
Actin Bundles ◽  
3 Dimensional ◽  
Macromolecular Assembly

Actin is a protein found in all eukaryotic cells. In its polymerized form, the cells use it for motility, cytokinesis and for cytoskeletal support. An example of this latter class is the actin bundle in the acrosomal process from the Limulus sperm. The different functions actin performs seem to arise from its interaction with the actin binding proteins. A 3-dimensional structure of this macromolecular assembly is essential to provide a structural basis for understanding this interaction in relationship to its development and functions.

Stochastic Dynamics, Filtering and Optimization

2017 ◽  
Author(s):  
Debasish Roy ◽  
G. Visweswara Rao
Keyword(s):  
Stochastic Dynamics

Development of Synchrotron Footprinting at NSLS and NSLS-II

2019 ◽  
Vol 26 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Jen Bohon
Keyword(s):  
Solvent Accessibility ◽  
Biological Systems ◽  
In Vivo Studies ◽  
Aqueous Samples ◽  
Biological Mechanisms ◽  
Macromolecular Assembly ◽  
Structure And Dynamics ◽  
Synchrotron Light ◽  
Ideal Technique

Background: First developed in the 1990’s at the National Synchrotron Light Source, xray synchrotron footprinting is an ideal technique for the analysis of solution-state structure and dynamics of macromolecules. Hydroxyl radicals generated in aqueous samples by intense x-ray beams serve as fine probes of solvent accessibility, rapidly and irreversibly reacting with solvent exposed residues to provide a “snapshot” of the sample state at the time of exposure. Over the last few decades, improvements in instrumentation to expand the technology have continuously pushed the boundaries of biological systems that can be studied using the technique. Conclusion: Dedicated synchrotron beamlines provide important resources for examining fundamental biological mechanisms of folding, ligand binding, catalysis, transcription, translation, and macromolecular assembly. The legacy of synchrotron footprinting at NSLS has led to significant improvement in our understanding of many biological systems, from identifying key structural components in enzymes and transporters to in vivo studies of ribosome assembly. This work continues at the XFP (17-BM) beamline at NSLS-II and facilities at ALS, which are currently accepting proposals for use.

Stochastic Particle Dynamics

2018 ◽  
Author(s):  
Sauro Succi
Keyword(s):  
Brownian Motion ◽  
Kinetic Theory ◽  
Stochastic Dynamics ◽  
Kinetic Equations ◽  
Planck Equation ◽  
Central Place ◽  
Conclusive Evidence ◽  
Dense Fluids ◽  
Complementary Role ◽  
Collisional Interactions

Dense fluids and liquids molecules are in constant interaction; hence, they do not fit into the Boltzmann’s picture of a clearcut separation between free-streaming and collisional interactions. Since the interactions are soft and do not involve large scattering angles, an effective way of describing dense fluids is to formulate stochastic models of particle motion, as pioneered by Einstein’s theory of Brownian motion and later extended by Paul Langevin. Besides its practical value for the study of the kinetic theory of dense fluids, Brownian motion bears a central place in the historical development of kinetic theory. Among others, it provided conclusive evidence in favor of the atomistic theory of matter. This chapter introduces the basic notions of stochastic dynamics and its connection with other important kinetic equations, primarily the Fokker–Planck equation, which bear a complementary role to the Boltzmann equation in the kinetic theory of dense fluids.

Stochastic Dynamics in Computational Biology

2020 ◽  
Author(s):  
Stefanie Winkelmann ◽  
Christof Schütte
Keyword(s):  
Computational Biology ◽  
Stochastic Dynamics

scholarly journals Interfacial Crystallization and Supramolecular Self-Assembly of Spider Silk Inspired Protein at the Water-Air Interface

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4239
Author(s):  
Pezhman Mohammadi ◽  
Fabian Zemke ◽  
Wolfgang Wagermaier ◽  
Markus B. Linder
Keyword(s):  
Self Assembly ◽  
Spider Silk ◽  
Assembly Process ◽  
Protein Solution ◽  
Time Resolved ◽  
Macromolecular Assembly ◽  
X Ray Scattering ◽  
Air Interface ◽  
Fundamental Research ◽  
Β Sheet

Macromolecular assembly into complex morphologies and architectural shapes is an area of fundamental research and technological innovation. In this work, we investigate the self-assembly process of recombinantly produced protein inspired by spider silk (spidroin). To elucidate the first steps of the assembly process, we examined highly concentrated and viscous pendant droplets of this protein in air. We show how the protein self-assembles and crystallizes at the water–air interface into a relatively thick and highly elastic skin. Using time-resolved in situ synchrotron X-ray scattering measurements during the drying process, we showed that the skin evolved to contain a high β-sheet amount over time. We also found that β-sheet formation strongly depended on protein concentration and relative humidity. These had a strong influence not only on the amount, but also on the ordering of these structures during the β-sheet formation process. We also showed how the skin around pendant droplets can serve as a reservoir for attaining liquid–liquid phase separation and coacervation from the dilute protein solution. Essentially, this study shows a new assembly route which could be optimized for the synthesis of new materials from a dilute protein solution and determine the properties of the final products.

scholarly journals Neural network aided approximation and parameter inference of non-Markovian models of gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingchao Jiang ◽  
Xiaoming Fu ◽  
Shifu Yan ◽  
Runlai Li ◽  
Wenli Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Stochastic Dynamics ◽  
Stochastic Simulations ◽  
Parameter Inference ◽  
Biochemical Processes ◽  
Markovian Models ◽  
The Neural Network ◽  
Large Numbers ◽  
Small Set ◽  
Noisy Measurements

AbstractNon-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

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