A flexible architecture for modeling and simulation of diffusional association

TL;DR

This paper introduces FADA, a flexible architecture that standardizes and enhances the modeling, simulation, and validation of diffusional association processes, addressing limitations of existing methods.

Contribution

FADA provides a formal, adaptable framework for defining experiments, models, and simulations in diffusional association, improving comparison and validation capabilities.

Findings

FADA enables flexible experiment configuration for diffusional association.

Illustration of FADA based on the NAM method.

Discussion of future validation experiments and applications.

Abstract

Up to now, it is not possible to obtain analytical solutions for complex molecular association processes (e.g. Molecule recognition in Signaling or catalysis). Instead Brownian Dynamics (BD) simulations are commonly used to estimate the rate of diffusional association, e.g. to be later used in mesoscopic simulations. Meanwhile a portfolio of diffusional association (DA) methods have been developed that exploit BD. However, DA methods do not clearly distinguish between modeling, simulation, and experiment settings. This hampers to classify and compare the existing methods with respect to, for instance model assumptions, simulation approximations or specific optimization strategies for steering the computation of trajectories. To address this deficiency we propose FADA (Flexible Architecture for Diffusional Association) - an architecture that allows the flexible definition of the experiment comprising a formal description of the model in SpacePi, different simulators, as well as validation and analysis methods. Based on the NAM (Northrup-Allison-McCammon) method, which forms the basis of many existing DA methods, we illustrate the structure and functioning of FADA. A discussion of future validation experiments illuminates how the FADA can be exploited in order to estimate reaction rates and how validation techniques may be applied to validate additional features of the model.