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Integrating various data sources for improved quality in reverse engineering of gene regulatory networks
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.ORCID iD: 0000-0003-0528-9782
2009 (English)In: Handbook of Research on Computational Methodologies in Gene Regulatory Networks / [ed] Sanjoy Das, Doina Caragea, Stephen M. Welch and William H. Hsu, IGI Global , 2009, 1, 476-496 p.Chapter in book (Other academic)
Abstract [en]

In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programming formulation. This LP problem is solved repeatedly with small modifications, from which we can benefit by restarting the primal simplex method from nearby solutions, which enables a computational efficient execution. We perform a case study for data from the yeast cell cycle, where all verified genes are putative regulators and the a priori knowledge consists of several types of binding data, text-mining and annotation knowledge.

Place, publisher, year, edition, pages
IGI Global , 2009, 1. 476-496 p.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-54096ISBN: 978-1-60566-685-3ISBN: 978-1-60566-686-0OAI: diva2:299507
Available from: 2010-02-23 Created: 2010-02-23 Last updated: 2013-09-12Bibliographically 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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1301
National Category
Natural Sciences
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)
Available from: 2010-02-25 Created: 2010-02-22 Last updated: 2013-09-12Bibliographically approved

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