Effective Data-Driven Collective Variables for Free Energy Calculations from Metadynamics of Paths

A variety of enhanced sampling methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets -- ideally incorporating information about physical pathways and transition states -- which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of enhanced sampling simulations in trajectory space via the metadynamics of paths [arXiv:2002.09281] algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in enhanced sampling simulations. We demonstrate our approach with two numerical examples, a two-dimensional model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice

Effective Data-Driven Collective Variables from Metadynamics of Paths

<p>This repository contains input files, data from OPES simulations in configuration space and MoP simulations in trajectory space for the 2D model potential and alanine dipeptide, as well as compile scripts and example code for the manuscript: <br><strong>"Effective Data-Driven Collective Variables from Metadynamics of Paths" </strong><br>Lukas Müllender, Andrea Rizzi, Michele Parrinello, Paolo Carloni, Davide Mandelli<br>[https://arxiv.org/abs/2311.05571]</p><p><br> </p&gt

The effect of sub-populations and input synchronicity on neuronal network properties and network event initiation

AimsSynchronous network activity, found in vivo and in vitro, is a basic neuronal mechanism for information processing. Computational studies suggest that functional communities in neuronal populations initiate synchronous network events (1). We investigate how stimulation of different neuronal sub-populations in designed networks affects synchronous network events and emergent network properties in vitro.MethodsPatterned networks of primary E18 rat neurons on microcontact-printed substrates were grown for ~4 weeks. Neuronal activity was simultaneously stimulated and recorded using AAV transduced optogenetic tools for depolarization and calcium imaging. Four stimulation regimes were tested: 1) near-simultaneous or 2) individual stimulation of neurons in a 3) main population or a 4) triangular sub-population (see Figure). ResultsPopulation-spanning network events are elicited more often by near-simultaneous stimulation, and by stimulating the main population. The temporal structure of network events is largely similar throughout stimulations. Graph theoretical network analysis shows that functional clustering and global efficiency increase during and decrease after stimulation, an effect more pronounced in individual stimulations. However, when stimulating neurons in the sub-population individually, the efficiency constantly decreases.ConclusionsSynchronous activation of neurons in a network seems to have a greater effect on network event initiation (but not event structure) than the initiation site. However, the initiation site influences – possibly lastingly – the emergent properties of the network activity. 1. D. Lonardoni et al., PLoS Comput. Biol. 13, e1005672 (2017)