Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback.

Here we investigate single-cell stochastic gene expression kinetics in a minimal coupled gene circuit with positive-plus-negative feedback. A triphasic stochastic bifurcation is observed upon increasing the ratio of the positive and negative feedback strengths, which reveals a strong synergistic interaction between positive and negative feedback loops. We discover that coupled positive-plus-negative feedback amplifies gene expression mean but reduces gene expression noise over a wide range of feedback strengths when promoter switching is relatively slow, stabilizing gene expression around a relatively high level.

Distributed Dual Subgradient Algorithms With Iterate-Averaging Feedback for Convex Optimization With Coupled Constraints.

This article considers a general model of distributed convex optimization with possibly local constraints, coupled equality constraints, and coupled inequality constraints, where the coupled equality constraints are affine and the coupled inequality constraints can be nonaffine. To solve this problem, we present two algorithms. The first algorithm is similar to a dual subgradient algorithm that requires a center node in the network.

Harvesting of interacting stochastic populations.

We analyze the optimal harvesting problem for an ecosystem of species that experience environmental stochasticity. Our work generalizes the current literature significantly by taking into account non-linear interactions between species, state-dependent prices, and species seeding. The key generalization is making it possible to not only harvest, but also 'seed' individuals into the ecosystem.

Stochastic population growth in spatially heterogeneous environments: the density-dependent case.

This work is devoted to studying the dynamics of a structured population that is subject to the combined effects of environmental stochasticity, competition for resources, spatio-temporal heterogeneity and dispersal. The population is spread throughout n patches whose population abundances are modeled as the solutions of a system of nonlinear stochastic differential equations living on [Formula: see text]. We prove that r, the stochastic growth rate of the total population in the absence of competition, determines the long-term behaviour of the population.

Approximate method for stochastic chemical kinetics with two-time scales by chemical Langevin equations.

The frequently used reduction technique is based on the chemical master equation for stochastic chemical kinetics with two-time scales, which yields the modified stochastic simulation algorithm (SSA). For the chemical reaction processes involving a large number of molecular species and reactions, the collection of slow reactions may still include a large number of molecular species and reactions. Consequently, the SSA is still computationally expensive.

Controlled hidden markov models for dynamically adapting patch clamp experiment to estimate Nernst potential of single-ion channels.

This paper presents novel kernel-based stochastic learning algorithms for controlling the kinetics of single-ion channels in a patch clamp experiment. The algorithms yield efficient estimates of the equilibrium (Nernst) potential of an ion channel. The equilibrium potential of an ion channel is the applied external potential difference required to maintain electrochemical equilibrium across the ion channel.

Muscarinic antagonist control of myopia: a molecular search for the M1 receptor in chick.

Purpose: Pirenzepine, an M1 selective muscarinic antagonist, is effective in slowing the progression of myopia in both humans and experimental animals, including chick. As an M1 selective antagonist, pirenzepine is considered to mediate its effect through M1 receptors. However, there is currently no report of the M1 receptor in chicken.