BooleNet

BooleNet is a software tool for computing attractors in the synchronous Boolean network model [1] of genetic regulatory networks. It implement the algorithm presented in [2] which is based on Binary Decision Diagrams.

BooleNet reads in a Boolean network description represented in a .net format similar to the Berkeley Logic Interchange Format (BLIF) format commonly used in synthesis and verification tools and prints out the set of network's attractors.

BooleNet binaries are available for the following platforms:

Linux: BooleNet Tested on Red Hat Enterprise kernel 2.6.18, Ubuntu 8.04 (64 bit), and Fedora Core 8.
Windows Cygwin: BooleNet Tested on cygwin1.dll version 1.5.25.

The source code of BooleNet is available here.

User manual for BooleNet.

TEST INPUT FILES

You can use the following files to test BooleNet:

The Boolean network model of of the control of flower morphogenesis in Arabidopsis thaliana [3]. It has 10 attractors of length 1.
The Boolean network model of the control of T-helper cell differentiation [4]. It has 3 attractors of length 1.
The Boolean network model of the T-cell receptor signaling pathway [5]. It has 8 attractors of length 1 and one attractor of length 6.
The Boolean network model of the control of the mammalian cell cycle [6]. It has one attractor of length 1 and one attractor of length 7.
The Boolean network model of the control of the fission yeast cell cycle regulation [7]. It has 13 attractors of length 1.
The Boolean network model of the control of the budding yeast cell cycle regulation [8]. It has 7 attractors of length 1.

REFERENCES:

[1] Boolean Dynamics with Random Couplings, M. Aldana, S. Coopersmith, L. P. Kadanoff, 2002, http://arXiv.org/abs/nlin/0204062.

[2] "Kauffman Networks: Analysis and Applications", E. Dubrova, M. Teslenko, A. Martinelli, Proceedings of International Conference on Computer-Aided Design (ICCAD'2005), November 6-10, 2005, San Jose, CA, USA, pp. 479-484.

[3] "From Genes to Flower Patterns and Evolution: Dynamic Models of Gene Regulatory Networks", A. Chaos, M. Aldana, C. Espinosa-Soto, B. G. P. de Leon, A. G. Arroyo, E. R. Alvarez-Buylla, Journal of Plant Growth Regulation, vol. 25, n. 4, 2006, pp. 278-289.

[4] "A Method for the Generation of Standardized Qualitative Dynamical Systems of Regulatory Networks", L. Mendoza and I. Xenarios, Journal of Theoretical Biology and Medical Modeling, 2006, vol. 3, no. 13.

[5] "A Methodology for the Structural and Functional Analysis of Signaling and Regulatory Networks", S. Klamt, J. Saez-Rodriguez, J. A. Lindquist, L. Simeoni, E. D. Gilles, JBMC Bioinformatics, 2006, vol. 7, no. 56.

[6] "Dynamical Analysis of a Generic Boolean Model for the Control of the Mammalian Cell Cycle", A. Faure, A. Naldi, C. Chaouiya, D. Thieffry, Bioinformatics, 2006, vol. 22, no. 14, pp. e124-e131.

[7] "Boolean Network Model Predicts Cell Cycle Sequence of Fission Yeast", M. I. Davidich, S. Bornholdt, PLoS ONE. 2008 Feb 27, 3(2):e1672.

[8] "The Yeast Cell-Cycle Network is Robustly Designed", Fangting Li, Tao Long, Ying Lu, Qi Ouyang, Chao Tang, PNAS April 6, 2004, vol. 101 no. 14, 4781-4786.

CONTACT PERSON:

Elena Dubrova
Department of Electronics, Computer and Software
School of Information and Communication Technology
dubrova@kth.se