Dizzy: Stochastic Simulation of Large-scale Genetic Regulatory Networks

نویسندگان

  • Stephen Ramsey
  • David Orrell
  • Hamid Bolouri
چکیده

We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Simulation of Genetic Regulatory Networks

Dizzy is a chemical kinetics simulation software framework. On up gradating this package to simulate the dynamics of complex gene regulatory networks. Using Tauleap simplex and Tauleap complex algorithms, implemented in Java. Procedure have been improved for determining the maximum leap size which accelerates the speed of simulation. This paper focuses mainly on simulating Genetic Regulatory Ne...

متن کامل

H∞ Sampled-Data Controller Design for Stochastic Genetic Regulatory Networks

Artificially regulating gene expression is an important step in developing new treatment for system-level disease such as cancer. In this paper, we propose a method to regulate gene expression based on sampled-data measurements of gene products concentrations. Inherent noisy behaviour of Gene regulatory networks are modeled with stochastic nonlinear differential equation. To synthesize feed...

متن کامل

Dizzy: Stochastic Simulation of Large-scale Genetic Regulatory Networks (supplementary Material)

In the past five years, there has been significant progress in developing high-speed algorithms for solving the stochastic kinetics of complex biochemical networks. In this section, we briefly survey some of these algorithms. Gillespie proposed a discrete-event Monte Carlo technique for generating approximate solutions to the chemical master equation for the grand probability function. During e...

متن کامل

Stochastic neural network models for gene regulatory networks

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale ge...

متن کامل

Analyzing the Effects of Coarse-scale Modeling of Genetic Regulatory Networks

Fine-scale models such as stochastic master equations can provide a very accurate description of the real genetic regulatory system but inadequate time series data and limitations on cell specific measurements in biological experiments prevent the accurate inference of the parameters of such a fine-scale model. Furthermore, the use of fine-scale stochastic models is restricted by the inherent c...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • Journal of bioinformatics and computational biology

دوره 3 2  شماره 

صفحات  -

تاریخ انتشار 2005