Partially Observable Gene Regulatory Network Control Without a Boundary on Horizon

2012-11-09
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.

Suggestions

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 ...
Functional Constraints on Replacing an Essential Gene with Its Ancient and Modern Homologs
Kacar, Betul; Garmendia, Eva; Tunçbağ, Nurcan; Andersson, Dan I.; Hughes, Diarmaid (2017-07-01)
Genes encoding proteins that carry out essential informational tasks in the cell, in particular where multiple interaction partners are involved, are less likely to be transferable to a foreign organism. Here, we investigated the constraints on transfer of a gene encoding a highly conserved informational protein, translation elongation factor Tu (EF-Tu), by systematically replacing the endogenous tufA gene in the Escherichia coli genome with its extant and ancestral homologs. The extant homologs represented...
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).
Application of copulas in graphical models for inference of biological systems
Dokuzoğlu, Damla; Purutçuoğlu Gazi, Vilda; Department of Statistics (2016)
Naturally, genes interact with each other by forming a complicated network and the relationship between groups of genes can be showed by different functions as gene networks. Recently, there has been a growing concern in uncovering these complex structures from gene expression data by modeling them mathematically. The Gaussian graphical model (GGM) is one of the very popular parametric approaches for modelling the underlying types of biochemical systems. In this study, we evaluate the performance of this pr...
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: https://hdl.handle.net/11511/35611.