Analysis of Stochastic Strategies in Bacterial Competence: A Master Equation Approach

Competence is a transiently differentiated state that certain bacterial cells reach when faced with a stressful environment. Entrance into competence can be attributed to the excitability of the dynamics governing the genetic circuit that regulates this cellular behavior. Like many biological behaviors, entrance into competence is a stochastic event. In this case cellular noise is responsible for driving the cell from a vegetative state into competence and back. In this work we present a novel numerical method for the analysis of stochastic biochemical events and use it to study the excitable dynamics responsible for competence in Bacillus subtilis. Starting with a Finite State Projection (FSP) solution of the chemical master equation (CME), we develop efficient numerical tools for accurately computing competence probability. Additionally, we propose a new approach for the sensitivity analysis of stochastic events and utilize it to elucidate the robustness properties of the competence regulatory genetic circuit. We also propose and implement a numerical method to calculate the expected time it takes a cell to return from competence. Although this study is focused on an example of cell-differentiation in Bacillus subtilis, our approach can be applied to a wide range of stochastic phenomena in biological systems

Stability of Adaptive Delta Modulators with a Forgetting Factor and Constant Inputs

Motivated by applications to feedback control over communication networks where the actuation and feedback signals are transmitted over communication channels, we study the stability of Adaptive Delta Modulators (ADM) when the coded signal is a constant

Stability of adaptive delta modulators with forgetting factor and constant inputs

Motivated by applications to feedback control over communication networks where the actuation and feedback signals are transmitted over communication channels, we study the stability of Adaptive Delta Modulators (ADM) when the coded signal is a constant

CIAT en la década de los ochenta: segunda aproximación del plan a largo plazo: para discusión con dirigentes de instituciones agrícolas nacionales en un seminario especial en el CIAT, 7-9 abril, 1981

<p>This figure shows the probability of entering in competence when is varied. The three plots show simulations from the full model using SSA (<i>black</i>), the reduced model using FSP (<i>blue</i>) and the reduced model using SSA (<i>red</i>). SSA results were generated by averaging over runs. For each data point, the error indicated by the errorbar is no larger than with a certainty no smaller than . This is to be compared to an upper bound of when using FSP.</p

Analysis of Stochastic Strategies in Bacterial Competence: A Master Equation Approach

Competence is a transiently differentiated state that certain bacterial cells reach when faced with a stressful environment. Entrance into competence can be attributed to the excitability of the dynamics governing the genetic circuit that regulates this cellular behavior. Like many biological behaviors, entrance into competence is a stochastic event. In this case cellular noise is responsible for driving the cell from a vegetative state into competence and back. In this work we present a novel numerical method for the analysis of stochastic biochemical events and use it to study the excitable dynamics responsible for competence in Bacillus subtilis. Starting with a Finite State Projection (FSP) solution of the chemical master equation (CME), we develop efficient numerical tools for accurately computing competence probability. Additionally, we propose a new approach for the sensitivity analysis of stochastic events and utilize it to elucidate the robustness properties of the competence regulatory genetic circuit. We also propose and implement a numerical method to calculate the expected time it takes a cell to return from competence. Although this study is focused on an example of cell-differentiation in Bacillus subtilis, our approach can be applied to a wide range of stochastic phenomena in biological systems.ISSN:1553-734XISSN:1553-735

Stability of Adaptive Delta Modulators with a Forgetting Factor and Constant Inputs

Motivated by applications to feedback control over communication networks where the actuation and feedback signals are transmitted over communication channels, we study the stability of Adaptive Delta Modulators (ADM) when the coded signal is a constant

Projection of infinite lattice into a finite subspace.

<p>The probability density vector evolves on an infinite integer lattice as shown by the arrows. A boundary region of interest is chosen (shown as a <i>box</i> in the figure). In this region all the reactions are maintained. Outside the region all the states are aggregated into one absorbing state, and the reactions leaving the region are maintained, while return from the outside to the inside of the region is prohibited by deletion of the reactions. We chose the maximum value of , so that we detect the probability of leaving this boundary region within the reactions run time we are interested in.</p