Biomaterial Database

Automated large-scale control of gene regulatory networks. 2010

Mehmet Tan, and Reda Alhajj, and Faruk Polat

Department of Computer Engineering, Middle East Technical University, Ankara 06531, Turkey. mtan@ucalgary.ca

Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem (FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.

UI	MeSH Term	Description	Entries
D008322	Mammals	Warm-blooded vertebrate animals belonging to the class Mammalia, including all that possess hair and suckle their young.	Mammalia,Mammal
D008390	Markov Chains	A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.	Markov Process,Markov Chain,Chain, Markov,Chains, Markov,Markov Processes,Process, Markov,Processes, Markov
D008957	Models, Genetic	Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.	Genetic Models,Genetic Model,Model, Genetic
D002453	Cell Cycle	The complex series of phenomena, occurring between the end of one CELL DIVISION and the end of the next, by which cellular material is duplicated and then divided between two daughter cells. The cell cycle includes INTERPHASE, which includes G0 PHASE; G1 PHASE; S PHASE; and G2 PHASE, and CELL DIVISION PHASE.	Cell Division Cycle,Cell Cycles,Cell Division Cycles,Cycle, Cell,Cycle, Cell Division,Cycles, Cell,Cycles, Cell Division,Division Cycle, Cell,Division Cycles, Cell
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D049490	Systems Biology	Comprehensive, methodical analysis of complex biological systems by monitoring responses to perturbations of biological processes. Large scale, computerized collection and analysis of the data are used to develop and test models of biological systems.	Biology, Systems
D053263	Gene Regulatory Networks	Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations.	Gene Circuits,Gene Modules,Gene Networks,Transcriptional Networks,Gene Module,Circuit, Gene,Circuits, Gene,Gene Circuit,Gene Network,Gene Regulatory Network,Module, Gene,Modules, Gene,Network, Gene,Network, Gene Regulatory,Network, Transcriptional,Networks, Gene,Networks, Gene Regulatory,Networks, Transcriptional,Regulatory Network, Gene,Regulatory Networks, Gene,Transcriptional Network
D055808	Drug Discovery	The process of finding chemicals for potential therapeutic use.	Drug Prospecting,Discovery, Drug,Prospecting, Drug