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Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
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
Karolinska University Sjukhuset.
Linköping University, Department of Physics, Chemistry and Biology, Computational Biology . Linköping University, The Institute of Technology.
2009 (English)In: IET SYSTEMS BIOLOGY, ISSN 1751-8849, Vol. 3, no 4, 219-228 p.Article in journal (Refereed) Published
Abstract [en]

Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

Place, publisher, year, edition, pages
2009. Vol. 3, no 4, 219-228 p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-19799DOI: 10.1049/iet-syb.2008.0112OAI: oai:DiVA.org:liu-19799DiVA: diva2:229055
Note
This paper is a postprint of a paper submitted to and accepted for publication in IET SYSTEMS BIOLOGY and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library Original Publication: Mika Gustafsson, Michael Hörnquist, J Bjorkegren and Jesper Tegnér, Genome-wide system analysis reveals stable yet flexible network dynamics in yeast, 2009, IET SYSTEMS BIOLOGY, (3), 4, 219-228. http://dx.doi.org/10.1049/iet-syb.2008.0112 Copyright: The Institution of Engineering and Technology http://www.theiet.org/ Available from: 2009-08-28 Created: 2009-08-10 Last updated: 2013-12-12Bibliographically approved
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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