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Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.ORCID iD: 0000-0003-0528-9782
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
2005 (English)In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 2, no 3, 254-261 p.Article in journal (Refereed) Published
Abstract [en]

We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.

Place, publisher, year, edition, pages
2005. Vol. 2, no 3, 254-261 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-29432DOI: 10.1109/TCBB.2005.35ISI: 000235704200008Local ID: 14778OAI: oai:DiVA.org:liu-29432DiVA: diva2:250246
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-12-12
In thesis
1. Gene networks from high-throughput data: Reverse engineering and analysis
Open this publication in new window or tab >>Gene networks from high-throughput data: Reverse engineering and analysis
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Experimental innovations starting in the 1990’s leading to the advent of high-throughput experiments in cellular biology have made it possible to measure thousands of genes simultaneously at a modest cost. This enables the discovery of new unexpected relationships between genes in addition to the possibility of falsify existing. To benefit as much as possible from these experiments the new inter disciplinary research field of systems biology have materialized. Systems biology goes beyond the conventional reductionist approach and aims at learning the whole system under the assumption that the system is greater than the sum of its parts. One emerging enterprise in systems biology is to use the high-throughput data to reverse engineer the web of gene regulatory interactions governing the cellular dynamics. This relatively new endeavor goes further than clustering genes with similar expression patterns and requires the separation of cause of gene expression from the effect. Despite the rapid data increase we then face the problem of having too few experiments to determine which regulations are active as the number of putative interactions has increased dramatic as the number of units in the system has increased. One possibility to overcome this problem is to impose more biologically motivated constraints. However, what is a biological fact or not is often not obvious and may be condition dependent. Moreover, investigations have suggested several statistical facts about gene regulatory networks, which motivate the development of new reverse engineering algorithms, relying on different model assumptions. As a result numerous new reverse engineering algorithms for gene regulatory networks has been proposed. As a consequent, there has grown an interest in the community to assess the performance of different attempts in fair trials on “real” biological problems. This resulted in the annually held DREAM conference which contains computational challenges that can be solved by the prosing researchers directly, and are evaluated by the chairs of the conference after the submission deadline.

This thesis contains the evolution of regularization schemes to reverse engineer gene networks from high-throughput data within the framework of ordinary differential equations. Furthermore, to understand gene networks a substantial part of it also concerns statistical analysis of gene networks. First, we reverse engineer a genome-wide regulatory network based solely on microarray data utilizing an extremely simple strategy assuming sparseness (LASSO). To validate and analyze this network we also develop some statistical tools. Then we present a refinement of the initial strategy which is the algorithm for which we achieved best performer at the DREAM2 conference. This strategy is further refined into a reverse engineering scheme which also can include external high-throughput data, which we confirm to be of relevance as we achieved best performer in the DREAM3 conference as well. Finally, the tools we developed to analyze stability and flexibility in linearized ordinary differential equations representing gene regulatory networks is further discussed.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 36 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1301
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-54089 (URN)978-91-7393-442-8 (ISBN)
Public defence
2010-03-26, K3, Kåkenshus, Campus Norrköping, Linköpings universitet, Norköping, 13:15 (English)
Opponent
Supervisors
Available from: 2010-02-25 Created: 2010-02-22 Last updated: 2013-09-12Bibliographically approved
2. Large-scale topology, stability and biology of gene networks
Open this publication in new window or tab >>Large-scale topology, stability and biology of gene networks
2006 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Experimental innovations in cell biology have provided a huge amount of genomescale data sets, settling the stage for understanding organisms on a system level. Recently, complex networks have been widely adopted and serve as a unifying language for widely different systems, including social, technological and biological systems. Still- in most biological cases-the number of interacting units vastly exceeds the number of measurements, hence large-scale models must still be very simple or non-specific. In this thesis we explore the limits of a linear (Lasso) network model on a genomic-scale for the Saccharomyces cerevisae organism and the limits of some analysis tools from the research field of complex networks. The former study (Paper I and Paper III) mainly regards validation issues, but also stipulate novel statistical system hypotheses, e.g., the system is significantly more stable than random, but still flexible to target stimuli. The latter study (Paper II) explores different heuristics in the search for communities (i.e., dense sub-graphs) within large complex networks and how the concept relates to functional modules.

Place, publisher, year, edition, pages
Linköpings Universitet: Linköpings universitet, 2006. 30 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1256
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-38819 (URN)45774 (Local ID)91-85523-53-4 (ISBN)45774 (Archive number)45774 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-12-12

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Gustafsson, MikaHörnquist, MichaelLombardi, Anna

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