Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development.

Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional "shape-space" describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.

Rare-event sampling of epigenetic landscapes and phenotype transitions.

Stochastic simulation has been a powerful tool for studying the dynamics of gene regulatory networks, particularly in terms of understanding how cell-phenotype stability and fate-transitions are impacted by noisy gene expression. However, gene networks often have dynamics characterized by multiple attractors. Stochastic simulation is often inefficient for such systems, because most of the simulation time is spent waiting for rare, barrier-crossing events to occur. We present a rare-event simulation-based method for computing epigenetic landscapes and phenotype-transitions in metastable gene networks. Our computational pipeline was inspired by studies of metastability and barrier-crossing in protein folding, and provides an automated means of computing and visualizing essential stationary and dynamic information that is generally inaccessible to conventional simulation. Applied to a network model of pluripotency in Embryonic Stem Cells, our simulations revealed rare phenotypes and approximately Markovian transitions among phenotype-states, occurring with a broad range of timescales. The relative probabilities of phenotypes and the transition paths linking pluripotency and differentiation are sensitive to global kinetic parameters governing transcription factor-DNA binding kinetics. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which may help guide rational cell reprogramming strategies. Our approach is also generalizable to other types of molecular networks and stochastic dynamics frameworks.

Effects of Concentration and Temperature on DNA Hybridization by Two Closely Related Sequences via Large-Scale Coarse-Grained Simulations.

A newly developed coarse-grained model called BioModi is utilized to elucidate the effects of temperature and concentration on DNA hybridization in self-assembly. Large-scale simulations demonstrate that complementary strands of either the tetrablock sequence or randomized sequence with equivalent number of cytosine or guanine nucleotides can form completely hybridized double helices. Even though the end states are the same for the two sequences, there exist multiple kinetic pathways that are populated with a wider range of transient aggregates of different sizes in the system of random sequences compared to that of the tetrablock sequence. The ability of these aggregates to undergo the strand displacement mechanism to form only double helices depends upon the temperature and DNA concentration. On one hand, low temperatures and high concentrations drive the formation and enhance stability of large aggregating species. On the other hand, high temperatures destabilize base-pair interactions and large aggregates. There exists an optimal range of moderate temperatures and low concentrations that allow minimization of large aggregate formation and maximization of fully hybridized dimers. Such investigation on structural dynamics of aggregating species by two closely related sequences during the self-assembly process demonstrates the importance of sequence design in avoiding the formation of metastable species. Finally, from kinetic modeling of self-assembly dynamics, the activation energy for the formation of double helices was found to be in agreement with experimental results. The framework developed in this work can be applied to the future design of DNA nanostructures in both fields of structural DNA nanotechnology and dynamic DNA nanotechnology wherein equilibrium end states and nonequilibrium dynamics are equally important requiring investigation in cooperation.