Feature extraction by digital image analysis and cell classification is an important task for cell culture automation. In High Throughput Screening (HTS) where thousands of data points are generated and processed at once, features will be extracted and cells will be classified to make a decision whether the cell-culture is going on smoothly or not. The culture is restarted if a problem is detected. In this thesis project HeLa cells, which are human epithelial cancer cells, are selected for the experiment. The purpose is to classify two types of HeLa cells in culture: Cells in cleavage that are round floating cells (stressed or dead cells are also round and floating) and another is, normal growing cells that are attached to the substrate. As the number of cells in cleavage will always be smaller than the number of cells which are growing normally and attached to the substrate, the cell-count of attached cells should be higher than the round cells. There are five different HeLa cell images that are used. For each image, every single cell is obtained by image segmentation and isolation. Different mathematical features are found for each cell. The feature set for this experiment is chosen in such a way that features are robust, discriminative and have good generalisation quality for classification. Almost all the features presented in this thesis are rotation, translation and scale invariant so that they are expected to perform well in discriminating objects or cells by any classification algorithm. There are some new features added which are believed to improve the classification result. The feature set is considerably broad rather than in contrast with the restricted sets which have been used in previous work. These features are used based on a common interface so that the library can be extended and integrated into other applications. These features are fed into a machine learning algorithm called Linear Discriminant Analysis (LDA) for classification. Cells are then classified as ‘Cells attached to the substrate’ or Cell Class A and ‘Cells in cleavage’ or Cell Class B. LDA considers features by leaving and adding shape features for increased performance. On average there is higher than ninety five percent accuracy obtained in the classification result which is validated by visual classification.

Background: Computational simulation of complex biological networks lies at the heart of systems biology since it can confirm the conclusions drawn by experimental studies of biological networks and guide researchers to produce fresh hypotheses for further experimental validation. Since this iterative process helps in development of more realistic system models a variety of computational tools have been developed. In the absence of a common format for representation of models these tools were developed in different formats. As a result these tools became unable to exchange models amongst them, leading to development of SBML, a standard exchange format for computational models of biochemical networks. Here the formats of SBML and one of the commercial tools of systems biology are being compared to study the issues which may arise during conversion between their respective formats. A tool StoP has been developed to convert the format of SBML to the format of the selected tool. Results: The basic format of SBML representation which is in the form of listings of various elements of a biochemical reaction system differs from the representation of the selected tool which is location oriented. In spite of this difference the various components of biochemical pathways including multiple compartments, global parameters, reactants, products, modifiers, reactions, kinetic formulas and reaction parameters could be converted from the SBML representation to the representation of the selected tool. The MathML representation of the kinetic formula in an SBML model can be converted to the string format of the selected tool. Some features of the SBML are not present in the selected tool. Similarly, the ability of the selected tool to declare parameters for locations, which are global to those locations and their children, is not present in the SBML. Conclusions: Differences in representations of pathway models may include differences in terminologies, basic architecture, differences in capabilities of software’s, and adoption of different standards for similar things. But the overall similarity of domain of pathway models enables us to interconvert these representations. The selected tool should develop support for unit definitions, events and rules. Development of facility for parameter declaration at compartment level by SBML and facility for function declaration by the selected tool is recommended.

Guanine nucleotide binding protein (G-protein) coupled receptors (GPCRs), the largest receptor family, is enormously important for the pharmaceutical industry as they are the target of 50-60% of all existing medicines. Discovery of many new GPCR receptors by the “human genome project”, open up new opportunities for developing novel therapeutics. High throughput screening (HTS) of chemical libraries is a well established method for finding new lead compounds in drug discovery. Despite some success this approach has suffered from the near absence of more focused and specific targeted libraries. To improve the hit rates and to maximally exploit the full potential of current corporate screening collections, in this thesis work, identification and analysis of the critical drug-binding positions within the GPCRs were done, based on their overall sequence, their transmembrane regions and their drug binding fingerprints. A proper classification based on drug binding fingerprints on the basis for a successful pharmacophore modelling and virtual screening were done, which facilities in the development of more specific and focused targeted libraries for HTS.

Background: Modelling and simulation are important tools for understanding biological systems. Numerous modelling and simulation software tools have been developed for integrating knowledge regarding the behaviour of a dynamic biological system described in mathematical form. The Systems Biology Markup Language (SBML) was created as a standard format for exchanging biochemical network models among tools. However, it is not certain yet whether actual usage and exchange of SBML models among the tools of different purpose and interfaces is assessable. Particularly, it is not clear whether dynamic simulations of SBML models using different modelling and simulation packages are replicable. Results: Time series simulations of published biological models in SBML format are performed using four modelling and simulation tools which support SBML to evaluate whether the tools correctly replicate the simulation results. Some of the tools do not successfully integrate some models. In the time series output of the successful simulations, there are differences between the tools. Conclusions: Although SBML is widely supported among biochemical modelling and simulation tools, not all simulators can replicate time-course simulations of SBML models exactly. This incapability of replicating simulation results may harm the peer-review process of biological modelling and simulation activities and should be addressed accordingly, for example by specifying in the SBML model the exact algorithm or simulator used for replicating the simulation result.

Protein interaction is an important mechanism for cellular functionality. Predicting protein interactions is available in many cases as computational methods in publicly available resources (for example Scansite). These predictions can be further combined with other information sources to generate hypothetical pathways. However, when using computational methods for building pathways, the process may become time consuming, as it requires multiple iterations and consolidating data from different sources. We have tested whether it is possible to generate graphs of protein-protein interaction by using only domain-motif interaction data and the degree to which it is possible to automate this process by developing a program that is able to aggregate, under user guidance, query results from different information sources. The data sources used are Scansite and SwissProt. Visualisation of the graphs is done with an external program freely available for academic purposes, Osprey. The graphs obtained by running the software show that although it is possible to combine publicly available data and theoretical protein-protein interaction predictions from Scansite, further efforts are needed to increase the biological plausibility of these collections of data. It is possible, however, to reduce the dimensionality of the obtained graphs by focusing the searches on a certain tissue of interest.

Background Non coding RNAs are the RNA molecules that do not code for proteins but play structural, catalytic or regulatory roles in the organisms in which they are found. These RNAs generally conserve their secondary structure more than their primary sequence. It is possible to look for protein coding genes using sequence signals like promoters, terminators, start and stop codons etc. However, this is not the case with non coding RNAs since these signals are weakly conserved in them. Hence the situation with non coding RNAs is more challenging. Therefore a protocol is devised to identify U1 RNA in species not previously known to have it. Results It is sufficient to use the covariance models to identify non coding RNAs but they are very slow and hence a filtering step is needed before using the covariance models to reduce the search space for identifying these genes. The protocol for identifying U1 RNA genes employs for the filtering a pattern matcher RNABOB that can conduct secondary structure pattern searches. The descriptor for RNABOB is made automatically such that it can also represent the bulges and interior loops in helices of RNA. The protocol is compared with the Rfam and Weinberg & Ruzzo approaches and has been able to identify new U1 RNA homologues in the Apicomplexan group where it has not previously been found. Conclusions The method has been used to identify the gene for U1 RNA in certain species in which it has not been detected previously. The identified genes may be further analyzed by wet laboratory techniques for the confirmation of their existence. 4

Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.

Endometrial adenocarcinoma (EAC) is the fourth leading cause of carcinoma in woman worldwide, but not much is known about genetic factors involved in this complex disease. During the EAC process, it is well known that losses and gains of chromosomal regions do not occur completely at random, but partly through some flow of causality. In this work, we used three different algorithms based on frequency of genomic alterations to construct 27 tree models of oncogenesis. So far, no study about applying pathway models to microsatellite marker data had been reported. Data from genome–wide scans with microsatellite markers were classified into 9 data sets, according to two biological approaches (solid tumor cell and corresponding tissue culture) and three different genetic backgrounds provided by intercrossing the susceptible rat BDII strain and two normal rat strains. Compared to previous study, similar conclusions were drawn from tree models that three main important regions (I, II and III) and two subordinate regions (IV and V) are likely to be involved in EAC development. Further information about these regions such as their likely order and relationships was produced by the tree models. A high consistency in tree models and the relationship among p19, Tp53 and Tp53 inducible protein genes provided supportive evidence for the reliability of results.

QuantitativeTraitLocus (QTL) is a statistical method used to restrict genomic regions contributing to specific phenotypes. To further localize genes in such regions a web tool called “Candidate Gene Capture” (CGC) was developed by Andersson et al. (2005). The CGC tool was based on the textual description of genes defined in the human phenotype database OMIM. Even though the CGC tool works well, the tool was limited by a number of inconsistencies in the underlying database structure, static web pages and some gene descriptions without properly defined function in the OMIM database. Hence, in this work the CGC tool was improved by redesigning its database structure, adding dynamic web pages and improving the prediction of unknown gene function by using exon analysis. The changes in database structure diminished the number of tables considerably, eliminated redundancies and made data retrieval more efficient. A new method for prediction of gene function was proposed, based on the assumption that similarity between exon sequences is associated with biochemical function. Using Blast with 20380 exon protein sequences and a threshold E-value of 0.01, 639 exon groups were obtained with an average of 11 exons per group. When estimating the functional similarity, it was found that on the average 72% of the exons in a group had at least one Gene Ontology (GO) term in common.

This thesis concerns numerical methods for mapping of multiple quantitative trait loci, QTL. Interactions between multiple genetic loci influencing important traits, such as growth rate in farm animals and predisposition to cancer in humans, make it necessary to search for several QTL simultaneously. Simultaneous search for n QTL involves solving an n-dimensional global optimization problem, where each evaluation of the objective function consists of solving a generalized least squares problem. In Paper A we present efficient algorithms, mainly based on updated QR factorizations, for evaluating the objective functions of different parametric QTL mapping methods. One of these algorithms reduces the computational work required for an important function class by one order of magnitude compared with the best of the methods used by other authors. In Paper B previously utilized techniques for finding the global optimum of the objective function are compared with a new approach based on the DIRECT algorithm of Jones et al. The new method gives accurate results in one order of magnitude less time than the best of the formerly employed algorithms. Using the algorithms presented in Papers A and B, simultaneous search for at least three QTL, including computation of the relevant empirical significance thresholds, can be performed routinely.