Oil-water interfaces with surfactants: A systematic approach to determine coarse-grained model parameters

2018 ◽  
Vol 148 (20) ◽  
pp. 204704 ◽  
Author(s):  
Tuan V. Vu ◽  
Dimitrios V. Papavassiliou
Keyword(s):  
Systematic Approach ◽  
Coarse Grained ◽  
Model Parameters ◽  
Coarse Grained Model ◽  
Oil Water

scholarly journals Causal Geometry

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 24
Author(s):  
Pavel Chvykov ◽  
Erik Hoel
Keyword(s):  
Causal Relationship ◽  
Causal Model ◽  
Information Geometry ◽  
Scientific Models ◽  
Coarse Grained ◽  
Model Parameters ◽  
Coarse Grained Model ◽  
Fundamental Part ◽  
The Impact ◽  
Causal Geometry

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore, we introduce a geometric version of “effective information”—a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that is well matched to those interventions. This is a consequence of “causal emergence,” wherein macroscopic causal relationships may carry more information than “fundamental” microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions—as we illustrate on toy examples.

scholarly journals Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty

2020 ◽  
Author(s):  
Elias Ventre ◽  
Thibault Espinasse ◽  
Charles-Edouard Bréhier ◽  
Vincent Calvez ◽  
Thomas Lepoutre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stochastic Model ◽  
Cell Types ◽  
Basins Of Attraction ◽  
Coarse Grained ◽  
Model Parameters ◽  
Toggle Switch ◽  
Lagrangian Dynamics ◽  
Coarse Grained Model ◽  
A Cell

AbstractDifferentiation is the process whereby a cell acquires a specific phenotype, by differential gene expression as a function of time. This is thought to result from the dynamical functioning of an underlying Gene Regulatory Network (GRN). The precise path from the stochastic GRN behavior to the resulting cell state is still an open question. In this work we propose to reduce a stochastic model of gene expression, where a cell is represented by a vector in a continuous space of gene expression, to a discrete coarse-grained model on a limited number of cell types. We develop analytical results and numerical tools to perform this reduction for a specific model characterizing the evolution of a cell by a system of piecewise deterministic Markov processes (PDMP). Solving a spectral problem, we find the explicit variational form of the rate function associated to a Large deviations principle, for any number of genes. The resulting Lagrangian dynamics allows us to define a deterministic limit, the basins of attraction of which can be identified to cellular types. In this context the quasipotential, describing the transitions between these basins in the weak noise limit, can be defined as the unique solution of an Hamilton-Jacobi equation under a particular constraint. We develop a numerical method for approximating the coarse-grained model parameters, and show its accuracy for a symmetric toggle-switch network. We deduce from the reduced model an analytical approximation of the stationary distribution of the PDMP system, which appears as a beta mixture. Altogether those results establish a rigorous frame for connecting GRN behavior to the resulting cellular behavior, including the calculation of the probability of jumps between cell types.

Insights Into Crowding Effects on Protein Stability From a Coarse-Grained Model

2009 ◽  
Vol 131 (7) ◽  
Author(s):  
Vincent K. Shen ◽  
Jason K. Cheung ◽  
Jeffrey R. Errington ◽  
Thomas M. Truskett
Keyword(s):  
Protein Stability ◽  
Coarse Grained ◽  
Protein Solutions ◽  
Native State ◽  
High Concentration ◽  
Coarse Grained Model ◽  
Excluded Volume Effects ◽  
Thermodynamic Phase ◽  
Broad Interest ◽  
Liquid Liquid Phase Separation

Proteins aggregate and precipitate from high concentration solutions in a wide variety of problems of natural and technological interest. Consequently, there is a broad interest in developing new ways to model the thermodynamic and kinetic aspects of protein stability in these crowded cellular or solution environments. We use a coarse-grained modeling approach to study the effects of different crowding agents on the conformational equilibria of proteins and the thermodynamic phase behavior of their solutions. At low to moderate protein concentrations, we find that crowding species can either stabilize or destabilize the native state, depending on the strength of their attractive interaction with the proteins. At high protein concentrations, crowders tend to stabilize the native state due to excluded volume effects, irrespective of the strength of the crowder-protein attraction. Crowding agents reduce the tendency of protein solutions to undergo a liquid-liquid phase separation driven by strong protein-protein attractions. The aforementioned equilibrium trends represent, to our knowledge, the first simulation predictions for how the properties of crowding species impact the global thermodynamic stability of proteins and their solutions.

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