Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed the landscape of aging and disease biology. They have revealed novel molecular markers of aging, disease state, and drug response. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and the underlying biology of aging and disease is heterogeneous and complex. Methods from integrative computational biology can help diminish these challenges by creating new analytical methods and software tools that leverage the large and diverse quantity of publicly available HTP data. In this thesis I report on four projects that develop and apply strategies from integrative computational biology to identify improved biomarkers and therapeutics for aging and disease. In Chapter 2, I proposed a new network analysis method to identify gene expression biomarkers of aging, and applied it to study the pathway-level effects of aging and infer the functions of poorly-characterized longevity genes. In Chapter 4, I adapted gene-level HTP chemogenomic data to study drug response at the systems level; I connected drugs to pathways, phenotypes and networks, and built the NetwoRx web portal to make these data publicly available. And in Chapters 3 and 5, I developed a novel meta-analysis pipeline to identify new drugs that mimic the beneficial gene expression changes seen with calorie restriction (Chapter 3), or that reverse the pathological gene changes associated with lung cancer (Chapter 5). The projects described in this thesis will help provide a systems-level understanding of the causes and consequences of aging and disease, as well as new tools for diagnosis (biomarkers) and treatment (therapeutics).

The aim of this project was to present a cost-efficient quality control routine to ensure thefunctionality of HS-Lenti RT Activity Kit, an ELISA-based kit produced by Cavidi AB, thatscreens for lentiviruses such as HIV. Two main methods for quality control are presented inthis report; acceptance sampling and Statistical Process Control (SPC). Acceptance samplingis a process where only parts of a batch are tested in order to determine whether or not thewhole batch should be accepted or rejected. SPC centers around monitoring an ongoingprocess by using statistical measures and charts which visualize variations in these measuresover time. Initially, using an acceptance sampling plan is recommended as the primaryapproach. SPC charts can then be set up using the data generated from the acceptancesampling, and be used in parallel with the acceptance sampling for some time until they canbe implemented to a wider extent. This report also presents different options forimmunoassay data processing. Bayesian methods of estimating analyte concentrations inunknown samples are highlighted as promising candidates in improving the performance andusability of the kit. The report also includes a customer requirements analysis, based on aconducted survey, that investigates the demands researchers within Uppsala University placeon products similar to HS-Lenti RT Activity Kit. The data which the analysis is based on wasobtained from an online questionnaire and three interviews. An ethical analysis regarding thequality control approach and survey is included as well.

The advent of microarray technology, allowing measurement of gene expression levels for thousands of genes in parallel, has made possible experiments designed to investigate the genetic control of variation in gene expression level (described in the literature as ???genetical genomics??? or ???eQTL??? experiments). Published results from these studies, in yeast and in mice, show that genetic variation is an important factor in gene regulation, and furthermore that individual polymorphisms modify the expression level of many genes. The concern of this thesis is the bioinformatic analyses of the expression level and genotype data sets that are the raw material for these studies. In particular this thesis addresses the two issues of detection of artefactual effects, and maximizing the information that can be extracted from the data. It is shown that while a polymorphism affecting the expression of many genes may be readily detected, care must be taken to determine whether the detected effect is genuinely one of genetic control of expression level, rather than the effect of correlations in measured expression level not of genetic cause. A significance test is devised to distinguish between these cases. The detection of artefactual correlation is explored further in the reanalysis of the published data from a large yeast study. A critique is given of the permutation method used to ascribe genetic control as the cause of inter gene expression level correlation. The presence of some degree of artefactual correlation is shown, and novel methods are presented for identifying such artefacts. To extend the analyses that may be applied to eQTL data, an algorithm is presented for determining secondary eQTLs for gene expression level (as opposed to a single primary QTL), along with a significance test for the putative QTL found. The technique is demonstrated on a large public data set. In addition to the use for which they are intended, the data sets generated for eQTL studies provide opportunities for additional analyses. In this thesis a method is developed for calculating a genome wide map of meiotic recombination frequency from the genotype data for multiple segregant strains. The method is demonstrated on the published genotype data generated for a large yeast eQTL study.

In 2003, Pomeroy et al. published a research study that described a gene expression based prediction of central nervous system embryonal tumour (CNS) outcome. Over a half of decade, many models and approaches have been developed based on experimental data consisting of 99 samples with 7,129 genes. The way, how meaningful knowledge from these models can be extracted, and how this knowledge for further research is still a hot topic. This thesis addresses this and has developed an information method that includes modelling of interactive patterns, important genes discovery and visualisation of the obtained knowledge. The major goal of this thesis is to discover important genes responsible for CNS tumour and import these genes into a well structured knowledge framework system, called Brain-Gene-Ontology. In this thesis, we take the first step towards finding the most accurate model for analysing the CNS tumour by offering a comparative study of global, local and personalised modelling. Five traditional modelling approaches and a new personalised method – WWKNN (weighted distance, weighted variables K-nearest neighbours) – are investigated. To increase the classification accuracy and one-vs.-all based signal to- noise ratio is also developed for pre-processing experimental data. For the knowledge discovery, CNS-based ontology system is developed. Through ontology analysis, 21 discriminate genes are found to be relevant for different CNS tumour classes, medulloblastoma tumour subclass and medulloblastoma treatment outcome. All the findings in this thesis contribute for expanding the information space of the BGO framework.

Licentiatavhandling Uppsala, Univ : 2004.

Thesis (Ph.D.)--University of California, San Francisco, 2008. / Source: Dissertation Abstracts International, Volume: 69-06, Section: B, page: 3531. Adviser: Kathleen M. Giacomini.

Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007. / Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6740. Adviser: Elizabeth A. Housworth. Title from dissertation home page (viewed May 20, 2008).

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. / Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7040. Adviser: Steven J. Clough. Includes supplementary digital materials. Includes bibliographical references (leaves 86-98) Available on microfilm from Pro Quest Information and Learning.

Thesis (M.S.)--George Mason University, 2008. / Vita: p. 29. Thesis director: Jennifer Weller. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics. Title from PDF t.p. (viewed July 7, 2008). Includes bibliographical references (p. 28). Also issued in print.

Thesis (Ph. D., Bioinformatics and Computational Biology)--University of Idaho, August 2008. / Major professor: James A. Foster. Includes bibliographical references (leaves 90-94). Also available online (PDF file) by subscription or by purchasing the individual file.