The small number of reactant molecules involved in gene regulation can lead to significant fluctuations in intracellular mRNA and protein concentrations, and there have been numerous recent studies devoted to the consequences of such noise at the regulatory level. Theoretical and computational work on stochastic gene expression has tended to focus on instantaneous transcriptional and translational events, whereas the role of realistic delay times in these stochastic processes has received little attention. Here, we explore the combined effects of time delay and intrinsic noise on gene regulation. Beginning with a set of biochemical reactions, some of which are delayed, we deduce a truncated master equation for the reactive system and derive an analytical expression for the correlation function and power spectrum. We develop a generalized Gillespie algorithm that accounts for the non-Markovian properties of random biochemical events with delay and compare our analytical findings with simulations. We show how time delay in gene expression can cause a system to be oscillatory even when its deterministic counterpart exhibits no oscillations. We demonstrate how such delay-induced instabilities can compromise the ability of a negative feedback loop to reduce the deleterious effects of noise. Given the prevalence of negative feedback in gene regulation, our findings may lead to new insights related to expression variability at the whole-genome scale.master equation ͉ stochastic delay equations ͉ noise ͉ time delay ͉ systems biology T here is considerable experimental evidence that noise can play a major role in gene regulation (1-10). These fluctuations can arise from either intrinsic sources, which are related to the small numbers of reactant biomolecules, or extrinsic sources, which are attributable to a noisy cellular environment. Although the importance of fluctuations in gene regulation was stressed Ͼ30 years ago (11), recent experimental advances have renewed interest in the stochastic modeling of the biochemical reactions that underlie gene regulatory networks (12-16). The most typical approaches are the utilization of the Gillespie algorithms (17)(18)(19)(20), the direct analysis of the master equation, or the development of simplified descriptions based on the Fokker-Planck or Langevin equations (see ref. 21 for a review). A common thread in many of these approaches has been to consider intrinsic noise as the dominant source of variability in gene expression.One major difficulty often encountered in the analysis of gene regulatory networks is the vast separation of time scales between what are typically the fast reactions (dimerization, protein-DNA binding͞unbinding) and the slow reactions (transcription, translation, degradation). There have been many studies devoted to the development of reduced descriptions of these systems using the idea of quasiequilibrium for the fast processes compared with the slow dynamics (cf. ref. 22 and references therein). These approaches have thus far implicitly assumed that all...

The structure of bacterial populations is governed by the interplay of many physical and biological factors, ranging from properties of surrounding aqueous media and substrates to cell-cell communication and gene expression in individual cells. The biomechanical interactions arising from the growth and division of individual cells in confined environments are ubiquitous, yet little work has focused on this fundamental aspect of colony formation. We analyze the spatial organization of Escherichia coli growing in a microfluidic chemostat. We find that growth and expansion of a dense colony of cells leads to a dynamical transition from an isotropic disordered phase to a nematic phase characterized by orientational alignment of rod-like cells. We develop a continuum model of collective cell dynamics based on equations for local cell density, velocity, and the tensor order parameter. We use this model and discrete element simulations to elucidate the mechanism of cell ordering and quantify the relationship between the dynamics of cell proliferation and the spatial structure of the population.bacteria ͉ microfluidics ͉ nematodynamics ͉ biofilms M orphogenesis is a highly important theme in both biology and nonequilibrium physics. The fundamental issue is to understand how the local interactions of elementary components lead to collective behavior and the formation of highly organized systems. In nature, this self-organization can lead to significant selective advantages for living organisms and is found on many levels, from biomolecules and single cells to schools of fish and herds of animals. Recent findings indicate that bacteria actively migrate toward surfaces and small enclosed spaces, where they form high-density microcolonies to facilitate quorum sensing (1). To resist environmental stresses, some species of bacteria form biofilms (2-4), which are commonly present in both natural environments (including living tissues, soils, and aquatic systems) and on synthetic surfaces (such as industrial piping and device implants). Generally, the collective dynamics of such cell populations involve a complex interplay of various physical, chemical, and biological phenomena such as chemotaxis (5), motility (6), cell-cell signaling (7), adhesion (8), and gene regulation (9).An important unexplored consequence of the formation of high-density bacterial colonies is spatial organization caused by the ''contact biomechanics'' arising from cellular growth and division. At low density, communication among cells occurs mainly through chemotaxis, but as bacteria aggregate and form dense communities, direct biomechanical interaction plays an increasingly strong role in colony organization. Although previous studies have explored the complex signaling mechanisms involved in the early stages of biofilm formation, the biomechanics of direct cellular contacts have received little attention. To address this issue, we focus here on the essential structure and dynamics of a growing 2D colony of nonmotile bacteria within a controlled microfl...

Using experiments with anisotropic vibrated rods and quasi-2D numerical simulations, we show that shape plays an important role in the collective dynamics of self-propelled (SP) particles. We demonstrate that SP rods exhibit local ordering, aggregation at the side walls, and clustering absent in round SP particles. Furthermore, we find that at sufficiently strong excitation SP rods engage in a persistent swirling motion in which the velocity is strongly correlated with particle orientation.

Development of therapeutics for genetically complex neurodegenerative diseases such as sporadic amyotrophic lateral sclerosis (ALS) has largely been hampered by lack of relevant disease models. Reprogramming of sporadic ALS patients’ fibroblasts into induced pluripotent stem cells (iPSC) and differentiation into affected neurons that show a disease phenotype could provide a cellular model for disease mechanism studies and drug discovery. Here we report the reprogramming to pluripotency of fibroblasts from a large cohort of healthy controls and ALS patients and their differentiation into motor neurons. We demonstrate that motor neurons derived from three sALS patients show de novo TDP-43 aggregation and that the aggregates recapitulate pathology in postmortem tissue from one of the same patients from which the iPSC were derived. We configured a high-content chemical screen using the TDP-43 aggregate endpoint both in lower motor neurons and upper motor neuron like cells and identified FDA-approved small molecule modulators including Digoxin demonstrating the feasibility of patient-derived iPSC-based disease modelling for drug screening.