Reversible Interacting-Particle Reaction Dynamics

2018

Preprint

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Interacting-particle reaction dynamics (iPRD) combines the simulation of dynamical trajectories of interacting particles as in molecular dynamics (MD) simulations with reaction kinetics, in which particles appear, disappear, or change their type and interactions based on a set of reaction rules. This combination facilitates the simulation of reaction kinetics in crowded environments, involving complex molecular geometries such as polymers, and employing complex reaction mechanisms such as breaking and fusion of polymers. iPRD simulations are ideal to simulate the detailed spatiotemporal reaction mechanism in complex and dense environments, such as in signalling processes at cellular membranes, or in nano- to microscale chemical reactors. Here we introduce the iPRD software ReaDDy 2, which provides a Python interface in which the simulation environment, particle interactions and reaction rules can be conveniently defined and the simulation can be run, stored and analyzed. A C++ interface is available to enable deeper and more flexible interactions with the framework. The main computational work of ReaDDy 2 is done in hardware-specific simulation kernels. While the version introduced here provides single- and multi-threading CPU kernels, the architecture is ready to implement GPU and multi-node kernels. We demonstrate the efficiency and validity of ReaDDy 2 using several benchmark examples. ReaDDy 2 is available at the https://readdy.github.io/ website.

Section: Interacting-particle Reaction Dynamics (Iprd)mentioning

confidence: 99%

“…ReaDDy 2 can treat reversible iPRD reactions by using steps that obey detailed balance, as described in [21] (iPRD-DB), and thus ensure correct thermodynamic behavior for such reactions (Sec. 4.1).…”

Section: Introductionmentioning

confidence: 99%

ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics

Hoffmann

Fröhner

2018

Preprint

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“…Red crosses denote reactions that were used as additional ansatz reactions. Blue circles are estimated by LSQ and orange circles depict rates which were obtained by solving the minimization problem (10). The latter rates are subject to a cutoff κ = 0.22 corresponding to the green circle's area under which a sparse solution with the correct processes can be recovered.…”

Section: A Learning the Reaction Network In The Low-noise Limitmentioning

confidence: 99%

“…Knowledge about the reaction network can be applied to parameterize other numerical methods to further investigate the processes at hand. Such methods include particlebased approaches derived from the chemical master equation [13,18,41,42], as well as highly detailed but parameter-rich methods such as particle-based or interacting-particle reaction dynamics [2,8,10,17,33,39,40] capable of fully resolving molecule positions in space and time -see [3,34] for recent reviews.…”

Section: Introductionmentioning

confidence: 99%

Reactive SINDy: Discovering governing reactions from concentration data

Hoffmann

Fröhner

2018

Preprint

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The inner workings of a biological cell or a chemical reaction can be rationalized by the network of reactions, whose structure reveals the most important functional mechanisms. For complex systems, these reaction networks are not known a priori and cannot be efficiently computed with ab initio methods, therefore an important approach goal is to estimate effective reaction networks from observations, such as time series of the main species. Reaction networks estimated with standard machine learning techniques such as least-squares regression may fit the observations, but will typically contain spurious reactions. Here we extend the sparse identification of nonlinear dynamics (SINDy) method to vector-valued ansatz functions, each describing a particular reaction process. The resulting sparse tensor regression method "reactive SINDy" is able to estimate a parsimonious reaction network. We illustrate that a gene regulation network can be correctly estimated from observed time series. * moritz.hoffmann@fu-berlin.de; Equal contributions † christoph.froehner@fu-berlin.de; Equal contributions ‡ frank.noe@fu-berlin.de;

“…Considering length-and time-scales involved in biological processes, coarse-graining has become an essential approach in membrane simulations [9][10][11][12]. Interacting particle reaction-dynamics (iPRD) models are extremely coarse-grained models that have been used to simulate a wide variety of cellular signalling pathways [13][14][15][16][17][18][19][20][21]. In such simulations, systems containing proteins, lipids, and metabolites are modelled with particles large enough to represent whole proteins or protein domains.…”

mentioning

confidence: 99%

Large-scale simulation of biomembranes: bringing realistic kinetics to coarse-grained models

Sadeghi

2019

Preprint

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Biomembranes are two-dimensional assemblies of phospholipids that are only a few nanometres thick, but form micrometer-sized structures vital to cellular function. Explicit modelling of biologically relevant membrane systems is computationally expensive, especially when the large number of solvent particles and slow membrane kinetics are taken into account. While highly coarse-grained solvent-free models are available to study equilibrium behaviour of membranes, their efficiency comes at the cost of sacrificing realistic kinetics, and thereby the ability to predict pathways and mechanisms of membrane processes. Here, we present a framework for integrating coarse-grained membrane models with anisotropic stochastic dynamics and continuum-based hydrodynamics, allowing us to simulate large biomembrane systems with realistic kinetics at low computational cost. This paves the way for whole-cell simulations that still include nanometer/nanosecond spatiotemporal resolutions. As a demonstration, we obtain and verify fluctuation spectrum of a full-sized human red blood cell in a 150-milliseconds-long single trajectory. We show how the kinetic effects of different cytoplasmic viscosities can be studied with such a simulation, with predictions that agree with single-cell experimental observations.