Thesis (M.Comp.Sc.)--University of Wollongong, 2003. / Typescript. Bibliographical references: leaf 93-96.

Thesis (Ph. D.)--Stockholm School of Economics, 1998. / Includes bibliographical references.

Thesis (doctoral)--Technische Universität, Kaiserslautern, 2003. / "'infix' is a joint imprint of Akademische Verlagsgesellschaft Aka GmbH and IOS Press BV (Amsterdam)"--T.p. verso. Includes bibliographical references and index.

Thesis (Ph. D.)--University of California, San Diego, 1997. / Vita. Includes bibliographical references (leaves 194-202).

Thesis (Ph. D.)--University of California, Santa Cruz, 1993. / Typescript. Includes bibliographical references (leaves 75-80).

Thesis (Ph. D.)--University of California, Santa Cruz, 1988. / Typescript. Includes bibliographical references (leaves 79-83).

Thesis (doctoral)--Uppsala. / Includes bibliographical references (p. 37-42).

Thesis (Ph. D.)--Stockholm School of Economics, 1998. / Includes bibliographical references.

A system requirements specification (SyRS) defines a set of functionalities that a system is expected to fulfil. A requirement may be “it is always the case that actualFuelLevel is greater than or equal to 0” for an industrial system. Inconsistencies in a SyRS may require the system to be redesigned or reimplemented, which can drastically increase costs. With the increased size and complexity of SyRS it is important to assess new methods for verifying their correctness with respect to some criteria such as consistency. PROPAS is a tool for automated consistency checking of SyRS developed within the VeriSpec project, a cooperation between Mälardalen University, Scania and Volvo GTT. The tool is based on satisfiability modulo theories (SMT) techniques and operates on SyRS encoded in formal notation, that is timed computation tree logic (TCTL). In this thesis we extend the functionality of the PROPAS tool by implementing a type-inferencing mechanism such that variable types in SyRS can be automatically inferred. For validation, we apply the extended PROPAS tool on a set of industrial requirements. The results show that the type-inferencing mechanism can correctly infer the types of the variables from the set of requirements in most cases, while in the same time not introducing significant computational overhead to the existing solution.

Tumour development has long been recognised as an evolutionary process during which cells accumulate mutations and evolve into a mix of genetically distinct cell subpopulations. The resulting genetic intra-tumour heterogeneity poses a major challenge to cancer therapy, as it increases the chance of drug resistance. To study tumour evolution in more detail, reliable approaches to infer the life histories of tumours are needed. This dissertation focuses on computational methods for inferring trees of tumour evolution from single-cell and multi-sample sequencing data. Recent advances in single-cell sequencing technologies have promised to reveal tumour heterogeneity at a much higher resolution, but single-cell sequencing data is inherently noisy, making it unsuitable for analysis with classic phylogenetic methods. The first part of the dissertation describes OncoNEM, a novel probabilistic method to infer clonal lineage trees from noisy single nucleotide variants of single cells. Simulation studies are used to validate the method and to compare its performance to that of other methods. Finally, OncoNEM is applied in two case studies. In the second part of the dissertation, a comprehensive collection of existing multi-sample approaches is used to infer the phylogenies of metastatic breast cancers from ten patients. In particular, shallow whole-genome, whole exome and targeted deep sequencing data are analysed. The inference methods comprise copy number and point mutation based approaches, as well as a method that utilises a combination of the two. To improve the copy number based inference, a novel allele-specific multi-sample segmentation algorithm is presented. The results are compared across methods and data types to assess the reliability of the different methods. In summary, this thesis presents substantial methodological advances to understand tumour evolution from genomic profiles of single cells or related bulk samples.