A Reduction of Logical Regulatory Graphs Preserving Essential Dynamical Properties
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
An outlook on design technologies for future integrated systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Determining a singleton attractor of an AND/OR Boolean network in O (1.587n) time
Information Processing Letters
Dynamically consistent reduction of logical regulatory graphs
Theoretical Computer Science
Emulation of biological networks in reconfigurable hardware
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A second-order learning algorithm for computing optimal regulatory pathways
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Logic programming for Boolean networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Survey: Computational challenges in systems biology
Computer Science Review
Finding a Periodic Attractor of a Boolean Network
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Oscillating behavior of logic programs
Correct Reasoning
Observability of Boolean networks: A graph-theoretic approach
Automatica (Journal of IFAC)
Learning from interpretation transition
Machine Learning
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Motivation:In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process. Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html. Contact: abhishek.garg@epfl.ch