Controlling discrete genetic regulatory networks

Download

index.pdf

Date

2005

Author

Abul, Osman

Metadata

Show full item record

Item Usage Stats

185
views

96
downloads

Genetic regulatory networks can model dynamics of cells. They also allow for studying the effect of internal or external interventions. Selectively applying interventions towards a certain objective is known as controlling network dynamics. In this thesis work, the issue of how the external interventions af fect the network is studied. The effects are determined using differential gene expression analysis. The differential gene expression problem is further studied to improve the power of the given method. Control problem for dynamic discrete regulatory networks is formulated. This also addresses the needs for various control strategies, e.g., finite horizon, infinite horizon, and various accounting of state and intervention costs. Control schemes for small to large networks are proposed and experimented. A case study is provided to show how the proposals are exploited; also given is the need for and effectiveness of various control schemes.

Subject Keywords

Computer software.

URI

http://etd.lib.metu.edu.tr/upload/12605739/index.pdf
https://hdl.handle.net/11511/14972

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A control system using behaviour hierarchies and neuro-fuzzy approach Arslan, Dilek; Alpaslan, Ferda Nur; Department of Computer Engineering (2005) In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task usin...
Subsequence feature maps for protein function annotation Saraç, Ömer Sinan; Atalay, Mehmet Volkan; Department of Computer Engineering (2008) With the advances in sequencing technologies, the number of protein sequences with unknown function increases rapidly. Hence, computational methods for functional annotation of these protein sequences become of the upmost importance. In this thesis, we first defined a feature space mapping of protein primary sequences to fixed dimensional numerical vectors. This mapping, which is called the Subsequence Profile Map (SPMap), takes into account the models of the subsequences of protein sequences. The resulting...
A clustering method for the problem of protein subcellular localization Bezek, Perit; Atalay, Mehmet Volkan; Department of Computer Engineering (2006) In this study, the focus is on predicting the subcellular localization of a protein, since subcellular localization is helpful in understanding a protein’s functions. Function of a protein may be estimated from its sequence. Motifs or conserved subsequences are strong indicators of function. In a given sample set of protein sequences known to perform the same function, a certain subsequence or group of subsequences should be common; that is, occurrence (frequency) of common subsequences should be high. Our ...
Modelling and predicting binding affinity of PCP-like compounds using machine learning methods Erdaş, Özlem; Alpaslan, Ferda Nur; Department of Computer Engineering (2007) Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values...
Automated Large-Scale Control of Gene Regulatory Networks Tan, Mehmet; Alhajj, Reda; Polat, Faruk (Institute of Electrical and Electronics Engineers (IEEE), 2010-04-01) 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...

Citation Formats

O. Abul, “Controlling discrete genetic regulatory networks,” Ph.D. - Doctoral Program, Middle East Technical University, 2005.