Search arXivSearch

arXiv · 1507.03621

Reactive Boundary Conditions as Limits of Interaction Potentials for Brownian and Langevin Dynamics

Abstract

A popular approach to modeling bimolecular reactions between diffusing molecules is through the use of reactive boundary conditions. One common model is the Smoluchowski partial absorption condition, which uses a Robin boundary condition in the separation coordinate between two possible reactants. This boundary condition can be interpreted as an idealization of a reactive interaction potential model, in which a potential barrier must be surmounted before reactions can occur. In this work we show how the reactive boundary condition arises as the limit of an interaction potential encoding a steep barrier within a shrinking region in the particle separation, where molecules react instantly upon reaching the peak of the barrier. The limiting boundary condition is derived by the method of matched asymptotic expansions, and shown to depend critically on the relative rate of increase of the barrier height as the width of the potential is decreased. Limiting boundary conditions for the same interaction potential in both the overdamped Fokker-Planck equation (Brownian Dynamics), and the Kramers equation (Langevin Dynamics) are investigated. It is shown that different scalings are required in the two models to recover reactive boundary conditions that are consistent in the high friction limit where the Kramers equation solution converges to the solution of the Fokker-Planck equation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S. Jonathan Chapman, Radek Erban, Samuel A. Isaacson. 2015-11-13. Reactive Boundary Conditions as Limits of Interaction Potentials for Brownian and Langevin Dynamics. https://arxiv.org/abs/1507.03621

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Spatially Resolved Nucleated Polymerization: A Free-Boundary Model of Protein Aggregation in Concentrated Solutions

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

physics.bio-ph

Deep Generative Markov State Models with Experimental Restraints

A central challenge in molecular modeling is reconciling simulations with experimental observables, as force-field inaccuracies can distort equilibrium populations and long-timescale kinetics. While time-dependent restraints can improve simulated kinetics, time-dependent structural observables remain limited. Thus, most experimental data are time-averaged, informing equilibrium ensembles but not directly dynamics. Although ensemble refinement can improve thermodynamic accuracy, incorporating time-averaged observables into kinetic models remains difficult. Here, we introduce BICePs-reweighted Reversible DeepMSMs, combining Bayesian Inference of Conformational Populations (BICePs), variational learning of Markov processes, and maximum entropy (MaxEnt)/maximum caliber (MaxCal) principles to infer consistent thermodynamics and minimally perturbed kinetics. At its core is a reversible DeepMSM prior, obtained by applying an orthogonal transformation post-hoc to a pre-trained dynamics model (e.g., VAMPnet). This preserves the eigenspectrum exactly while yielding a valid transition matrix that is nonnegative, row-stochastic, and reversible. The reweighted model refines stationary populations, transition dynamics, and state-conditioned configurational landing densities. We validate the approach on a quadruple-well toy system and alanine dipeptide using backbone dihedral angles and J-coupling constants. In both systems, perturbing the observables induces predictable changes in the equilibrium ensemble and corresponding inferred kinetics. The resulting relaxation timescales and dynamical modes closely agree with analytical and MaxCal reference models. Furthermore, a diffusion-based generative model trained on MaxEnt landing densities produces physically realistic alanine dipeptide trajectories that reproduce thermodynamics and kinetics while satisfying the imposed experimental restraints.

physics.bio-ph

Tracking and distinguishing slime mold solutions to traveling salesperson problems through synchronized amplification in the non-equilibrium steady state

The plasmodium of the true slime mold Physarum polycephalum-an ancient, unicellular, aneural organism-serves as a platform for studying the information-processing capacities of active matter. Previous experiments used Physarum's intricate morphological dynamics and photoavoidance in stellate chips to solve $N$-city traveling salesperson problems (TSPs) of up to eight cities, scaling linearly in time with TSP size. Optical feedback controlled by a modified Hopfield network illuminated specific lanes at regular intervals, prompting Physarum to elongate or retract selected branches. When the illumination pattern stabilized in a non-equilibrium steady state, branches bifurcated reproducibly into solution and non-solution groups, with the former exhibiting lower-frequency, higher-amplitude, and more synchronized oscillations than the latter across 41 trials with valid TSP solutions. Physarum's synchronization dynamics efficiently predict 100% of selected solutions by the midpoint of the optical-feedback interval, achieving statistically significant (paired t-test, $p<0.005$) discrimination from alternate tours well before the non-equilibrium steady state. Observed frequency downconversions and synchronized power amplifications scale linearly and quadratically, respectively, for small-to-moderate TSP size, as captured by a toy model of energy redistribution with saturating optical absorption. Tuning these features in native biomolecular chromophore networks may thus improve both the quality and efficiency of TSP solutions from Physarum-based biocomputers, which exploit the effects of organismal-scale coherence.

physics.bio-ph