Analysis and interpretation of first passage time distributions featuring rare events.

In this contribution we consider theory and associated computational tools to treat the kinetics associated with competing pathways on multifunnel energy landscapes. Multifunnel landscapes are associated with molecular switches and multifunctional materials, and are expected to exhibit multiple relaxation time scales and associated thermodynamic signatures in the heat capacity. Our focus here is on the first passage time distribution, which is encoded in a kinetic transition network containing all the locally stable states and the pathways between them. This network can be renormalised to reduce the dimensionality, while exactly conserving the mean first passage time and approximately conserving the full distribution. The structure of the reduced network can be visualised using disconnectivity graphs. We show how features in the first passage time distribution can be associated with specific kinetic traps, and how the appearance of competing relaxation time scales depends on the starting conditions. The theory is tested for two model landscapes and applied to an atomic cluster and a disordered peptide. Our most important contribution is probably the reconstruction of the full distribution for long time scales, where numerical problems prevent direct calculations. Here we combine accurate treatment of the mean first passage time with the reliable part of the distribution corresponding to faster time scales. Hence we now have a fundamental understanding of both thermodynamic and kinetic signatures of multifunnel landscapes.

On the Mechanism of Polaritonic Rate Suppression from Quantum Transition Paths.

Polariton chemistry holds promise for facilitating mode-selective chemical reactions, but the underlying mechanism behind the rate modifications observed under strong vibrational coupling is not well understood. Using the recently developed quantum transition path theory, we have uncovered a mechanism of resonant suppression of a thermal reaction rate in a simple model polaritonic system consisting of a reactive mode in a bath confined to a lossless microcavity with a single photon mode. We observed the formation of a polariton during rate-limiting transitions on reactive pathways and identified the concomitant rate suppression as being due to hybridization between the reactive mode and the cavity mode, which inhibits bath-mediated tunneling. The transition probabilities that define the quantum master equation can be directly translated into a visualization of the corresponding polariton energy landscape. This landscape exhibits a double funnel structure with a large barrier between the initial and final states.

Analysing ill-conditioned Markov chains.

Discrete state Markov chains in discrete or continuous time are widely used to model phenomena in the social, physical and life sciences. In many cases, the model can feature a large state space, with extreme differences between the fastest and slowest transition timescales. Analysis of such ill-conditioned models is often intractable with finite precision linear algebra techniques. In this contribution, we propose a solution to this problem, namely partial graph transformation, to iteratively eliminate and renormalize states, producing a low-rank Markov chain from an ill-conditioned initial model. We show that the error induced by this procedure can be minimized by retaining both the renormalized nodes that represent metastable superbasins, and those through which reactive pathways concentrate, i.e. the dividing surface in the discrete state space. This procedure typically returns a much lower rank model, where trajectories can be efficiently generated with kinetic path sampling. We apply this approach to an ill-conditioned Markov chain for a model multi-community system, measuring the accuracy by direct comparison with trajectories and transition statistics. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Multiscale modelling of chromatin organisation: Resolving nucleosomes at near-atomistic resolution inside genes.

The three-dimensional organisation of the genome modulates biological processes and is, in turn, transformed by the activity in the nucleus. Not surprisingly, understanding how the genome operates requires uncovering the fundamental biophysical and molecular mechanisms that establish and regulate its organisation. Genome organisation starts with the formation of chromatin: a polymer of nucleoprotein complexes, termed nucleosomes, that carry variable chemical signatures according to their biological context. The physicochemical heterogeneity of chromatin, the stochastic organisation it fosters, and the multiscale nature of genome organisation pose great technical challenges. Excitingly, advances in imaging and molecular biology techniques are addressing chromatin organisation at increasing resolutions. In tandem, computer models are testing and postulating hypotheses, interpreting the experimental data, and linking molecular properties of nucleosomes to the mesoscale organisation of chromatin. We discuss how coarse-grained models at varying resolutions are expanding our mechanistic understanding of chromatin organisation, and the challenges still remaining in the field.

Liquid-like chromatin in the cell: What can we learn from imaging and computational modeling?

Chromatin in eukaryotic cells is a negatively charged long polymer consisting of DNA, histones, and various associated proteins. With its highly charged and heterogeneous nature, chromatin structure varies greatly depending on various factors (e.g. chemical modifications and protein enrichment) and the surrounding environment (e.g. cations): from a 10-nm fiber, a folded 30-nm fiber, to chromatin condensates/droplets. Recent advanced imaging has observed that chromatin exhibits a dynamic liquid-like behavior and undergoes structural variations within the cell. Current computational modeling has made it possible to reconstruct the liquid-like chromatin in the cell by dealing with a number of nucleosomes on multiscale levels and has become a powerful technique to inspect the molecular mechanisms giving rise to the observed behavior, which imaging methods cannot do on their own. Based on new findings from both imaging and modeling studies, we discuss the dynamic aspect of chromatin in living cells and its functional relevance.

Nucleosome plasticity is a critical element of chromatin liquid-liquid phase separation and multivalent nucleosome interactions.

Liquid-liquid phase separation (LLPS) is an important mechanism that helps explain the membraneless compartmentalization of the nucleus. Because chromatin compaction and LLPS are collective phenomena, linking their modulation to the physicochemical features of nucleosomes is challenging. Here, we develop an advanced multiscale chromatin model-integrating atomistic representations, a chemically-specific coarse-grained model, and a minimal model-to resolve individual nucleosomes within sub-Mb chromatin domains and phase-separated systems. To overcome the difficulty of sampling chromatin at high resolution, we devise a transferable enhanced-sampling Debye-length replica-exchange molecular dynamics approach. We find that nucleosome thermal fluctuations become significant at physiological salt concentrations and destabilize the 30-nm fiber. Our simulations show that nucleosome breathing favors stochastic folding of chromatin and promotes LLPS by simultaneously boosting the transient nature and heterogeneity of nucleosome-nucleosome contacts, and the effective nucleosome valency. Our work puts forward the intrinsic plasticity of nucleosomes as a key element in the liquid-like behavior of nucleosomes within chromatin, and the regulation of chromatin LLPS.