Réka Albert's BooleanNet package

라벨: Informatics
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Source: Google Code

booleannet

Boolean Network Modeling
 

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Code License:	MIT License
Labels:	boolean, simulation, modeling

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BooleanNet-1.2.4.win32.exe BooleanNet-1.2.4.zip

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Project owners:
	istvan.albert
Project members:
	reka.albert

Boolean Network Modeling

The goal of this software package is to provide intuitive and accessible tools for simulating biological regulatory networksin a boolean formalism. Using this simulator biologist andbioinformaticians can specify their system in a simple textual languagethen explore various dynamic behaviors via a web interface or anapplication programming interface (API) each designed to facilitatescientific discovery, data collection and reporting.
The software is primarly distributed as Python source code and requires that Python 2.5 (or later) be installed on the target computer.

Introduction

Whentrying to understand the role and functioning of a regulatory network,the first step is to assemble the components of the network and theinteractions between them. The experimental advances in the large scalemapping of regulatory networks are fairly recent, but modeling effortsdate back to the end of 1960s thanks to the pioneering work of StuartKauffman and Rene Thomas. In aBoolean representation we assume that nodes are equivalent, and theirinteractions form a directed graph in which each node receives inputsfrom its neighbors (nodes that are connected to it). The state of nodesis described by binary (ON/OFF) variables, and the dynamic behavior ofeach variable, that is, whether it will be ON or OFF at next moment, isgoverned by a Boolean function. In general, a Boolean or logicalfunction is written as a statement acting on the inputs using thelogical operators and, or and not and its output is ON(OFF) if the statement is true (false).

Documentation

A detailed documentation is available on our external website. This documentation is also included in the source code distribution. See the:

• Installation
• Tutorials

for a quick overview on how the make the library work for you.

About the library

Noteas of version 1.2 (Nov 12, 2008) the library has acquired several newfeatures (see below) and underwent a substantial reorganization. Tokeep old code from interfering with new one the library namespace hasbeen changed to boolean2

In this software package, we implement mutliple advanced methodologieswhich combine discrete logical rules with more realistic assumptionsregarding the relative timescales of the processes:

• synchronous updates are performed in listed order, but the states change only at the end of the update round
• asynchronous updates are performed in random order and the states immediately reflect the changes, this makes the model stochastic.
• ranked asynchronous updates are performed in rank order, within the same rank the updates take place asynchronously
• time synchronous update are performed in time. Each rule has a time delay associated with them, updates take place at multiples of these delays
• piece wise differentialupdates associate a set of continuous variables (concentrations, decayrates and thresholds) to each discrete variable in the system. Thedynamics of these continuous variables is determined by a linear system of piecewise differential equations, leading to a hybrid model in the manner first suggested by Leon Glass and collaborators.

Example Rules

Here are a few example rules for BooleanNetcovering various research projects. Most of these rules were collectedvia a thorough literature search and were manually curated into theirfinal form. These rules are available in the examples/samples directory of the source code distribution.

• Rules for abscicis acid induced stomatal closure in plants aba.txt published as Song Li, Sarah Assmann and Réka Albert Predicting essential components of signal transduction networks: dynamic model of guard cell abscisic acid signaling PLoS Biology 4 (10): e312, 2006

• Rules for T-cell large granular lymphocyte leukemia simulation LGL.txt published as Ranran Zhang, Mithun Vinod Shah, Jun Yang, Susan B. Nyland, Xin Liu, Jong K. Yun, Réka Albert, and Thomas P. Loughran, Jr. Network Model of Survival Signaling in LGL Leukemia PNAS, 2008 (to appear)

• Rules for mammalian immune response to B. bronchiseptica infection immune.txt published as J. Thakar, M. Pilione, G. Kirimanjeswara, E. Harvill and R. Albert Modeling Systems-Level Regulation of Host Immune Responses PLoS Computational Biology 6, e109 (2007

Some of the resulting plots based on the rules above:


None of us would be thinking about Boolean Networks if it weren't for Réka Albert. Our software relies on the following third party libraries that are distributed with the software:

• PLY by David Beazley

The following library must be also installed if the engine will be used in plde mode (piece-wise linear differential equations):

• matplotlib by Dave Hunter

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라벨: Informatics