Partially Observable Gene Regulatory Network Control Without a Boundary on Horizon

Erdogdu, Utku
Polat, Faruk
Alhajj, Reda
Gene regulatory networks (GRNs) govern the protein transcription process in the cell and interactions among genes play a vital role in determining the biosynthesis rate of proteins. By using intervention techniques discovered by biological research it is possible to control a GRN, thus promoting or demoting the expression rate of a certain gene. In this work, this control task is studied in a partially observable setting where interventions lack perfect knowledge of the expression level of all genes. Moreover, we formulated the task as a lifelong control problem and developed a more flexible and scalable method than the alternatives described in the literature.


Integer linear programming based solutions for construction of biological networks
Eren Özsoy, Öykü; Can, Tolga; Department of Health Informatics (2014)
Inference of gene regulatory or signaling networks from perturbation experiments and gene expression assays is one of the challenging problems in bioinformatics. Recently, the inference problem has been formulated as a reference network editing problem and it has been show that finding the minimum number of edit operations on a reference network in order to comply with perturbation experiments is an NP-complete problem. In this dissertation, we propose linear programming based solutions for reconstruction o...
Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2015-11-12)
Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to ...
Employing decomposable partially observable Markov decision processes to control gene regulatory networks
Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2017-11-01)
Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
Mathematical Modeling and Approximation of Gene Expression Patterns
Yılmaz, Fatih; Öktem, Hüseyin Avni (2004-09-03)
This study concerns modeling, approximation and inference of gene regulatory dynamics on the basis of gene expression patterns. The dynamical behavior of gene expressions is represented by a system of ordinary differential equations. We introduce a gene-interaction matrix with some nonlinear entries, in particular, quadratic polynomials of the expression levels to keep the system solvable. The model parameters are determined by using optimization. Then, we provide the time-discrete approximation of our time...
Efficient partially observable markov decision process based formulation of gene regulatory network control problem
Erdoğdu, Utku; Polat, Faruk; Alhajj, Reda; Department of Computer Engineering (2012)
The need to analyze and closely study the gene related mechanisms motivated the research on the modeling and control of gene regulatory networks (GRN). Di erent approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between genes. Though it turns into a more challenging problem, we argue that partial observability would be a more natural and realistic method for handling the control of GRNs. Partial observability is a fundamental aspect of the problem; ...
Citation Formats
U. Erdogdu, F. Polat, and R. Alhajj, “Partially Observable Gene Regulatory Network Control Without a Boundary on Horizon,” 2012, Accessed: 00, 2020. [Online]. Available: