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Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences. (Cancer Pharmacology and Informatics)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group. (Cancer Pharmacology and Informatics)
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2007 (English)In: BMC Systems Biology, ISSN 1752-0509, Vol. 1, 45- p.Article in journal (Refereed) Published
Description
Abstract [en]

Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

Place, publisher, year, edition, pages
2007. Vol. 1, 45- p.
National Category
Biological Sciences Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-96786DOI: 10.1186/1752-0509-1-45ISI: 000252363100001PubMedID: 17939860OAI: oai:DiVA.org:uu-96786DiVA: diva2:171474
Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2016-09-25Bibliographically approved
In thesis
1. Fusing Domain Knowledge with Data: Applications in Bioinformatics
Open this publication in new window or tab >>Fusing Domain Knowledge with Data: Applications in Bioinformatics
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 55 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 401
Keyword
Bioinformatics, cell cycle, cancer chemotherapy, predictive tests, performance estimation, bioinformatics, Bioinformatik
Identifiers
urn:nbn:se:uu:diva-8477 (URN)978-91-554-7094-4 (ISBN)
Public defence
2008-03-13, Fåhraeussalen, Rudbecklaboratoriet, hus C:5, Dag Hammarskjölds väg 20, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2009-05-12Bibliographically approved

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Isaksson, AndersGustafsson, Mats G.

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