Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers.

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets.

Adaptive nonequilibrium design of actin-based metamaterials: Fundamental and practical limits of control.

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning.

Ensuring thermodynamic consistency with invertible coarse-graining.

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insights by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared with atomistic models.

Unified, Geometric Framework for Nonequilibrium Protocol Optimization.

Controlling thermodynamic cycles to minimize the dissipated heat is a long-standing goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation.

Probing the theoretical and computational limits of dissipative design.

Self-assembly, the process by which interacting components form well-defined and often intricate structures, is typically thought of as a spontaneous process arising from equilibrium dynamics. When a system is driven by external nonequilibrium forces, states statistically inaccessible to the equilibrium dynamics can arise, a process sometimes termed direct self-assembly. However, if we fix a given target state and a set of external control variables, it is not well-understood (i) how to design a protocol to drive the system toward the desired state nor (ii) the cost of persistently perturbing the stationary distribution.

Kinetic modeling reveals additional regulation at co-transcriptional level by post-transcriptional sRNA regulators.

Small RNAs (sRNAs) are important gene regulators in bacteria. Many sRNAs act post-transcriptionally by affecting translation and degradation of the target mRNAs upon base-pairing interactions. Here we present a general approach combining imaging and mathematical modeling to determine kinetic parameters at different levels of sRNA-mediated gene regulation that contribute to overall regulation efficacy.

An Automated Image Analysis Method for Segmenting Fluorescent Bacteria in Three Dimensions.

Single-cell fluorescence imaging is a powerful technique for studying inherently heterogeneous biological processes. To correlate a genotype or phenotype to a specific cell, images containing a population of cells must first be properly segmented. However, a proper segmentation with minimal user input becomes challenging when cells are clustered or overlapping in three dimensions.