<p>The number of experimentally determined protein structures in the protein data bank (PDB) is continuously increasing. The common features like; cellular location, function, topology, primary structure, secondary structure, tertiary structure, domains or fold are used to classify them. Therefore, there are various methods available for classification of proteins. In this work we are attempting an additional method for making appropriate classification, i.e. solvent energy. Solvation is one of the most important properties of macromolecules and biological membranes by which they remain stabilized in different environments. The energy required for solvation can be measured in term of solvent energy. Proteins from similar environments are investigated for similar solvent energy. That is, the solvent energy can be used as a measure to analyze and classify proteins. In this project solvent energy of proteins present in the Protein Data Bank (PDB) was calculated by using Jones’ algorithm. The proteins were classified into two classes; transmembrane and globular. The results of statistical analysis showed that the values of solvent energy obtained for two main classes (globular and transmebrane) were from different sets of populations. Thus, by adopting classification based on solvent energy will definitely help for prediction of cellular placement.</p><p> </p>

<p>Background:</p><p>The origin of vertebrate sensory systems still contains many mysteries and thus challenges to bioinformatics. Especially the evolution of the sense of smell maintains important puzzles, namely the question whether or not the vomeronasal system is older than the main olfactory system. Here I compare receptor sequences of the two distinct systems in a phylogenetic study, to determine their relationships among several different species of the vertebrates.</p><p>Results:</p><p>Receptors of the two olfactory systems share little sequence similarity and prove to be a challenge in multiple sequence alignment. However, recent dramatical improvements in the area of alignment tools allow for better results and high confidence. Different strategies and tools were employed and compared to derive a</p><p>high quality alignment that holds information about the evolutionary relationships between the different receptor types. The resulting Maximum-Likelihood tree supports the theory that the vomeronasal system is rather an ancestor of the main olfactory system instead of being an evolutionary novelty of tetrapods.</p><p>Conclusions:</p><p>The connections between the two systems of smell perception might be much more fundamental than the common architecture of receptors. A better understanding of these parallels is desirable, not only with respect to our view on evolution, but also in the context of the further exploration of the functionality and complexity of odor perception. Along the way, this work offers a practical protocol through the jungle of programs concerned with sequence data and phylogenetic reconstruction.</p>

<p>The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.</p>

<p>Motivation:</p><p>As genome-scale efforts are ongoing to investigate metabolic networks of miscellaneous organisms the amount of pathway data is growing. Simultaneously an increasing amount of gene expression data from micro arrays becomes available for reverse engineering, delivering e.g. hypothetical regulatory pathway data. To avoid outgrowing of data and keep control of real new informations the need of analysis tools arises. One vital task is the comparison of pathways for detection of similar functionalities, overlaps, or in case of reverse engineering, detection of known data corroborating a hypothetical pathway. A comparison method using ontological knowledge about molecules and reactions will feature a more biological point of view which graph theoretical approaches missed so far. Such a comparison attempt based on an ontology is described in this report.</p><p>Results:</p><p>An algorithm is introduced that performs a comparison of pathways component by component. The method was performed on two selected databases and the results proved it to be not satisfying using it as stand-alone method. Further development possibilities are suggested and steps toward an integrated method using several approaches are recommended.</p><p>Availability:</p><p>The source code, used database snapshots and pictures can be requested from the author.</p>

<p>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.</p><p>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</p><p>simulations, there are differences between the tools.</p><p>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.</p>

<p>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.</p>

<p>Background</p><p>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.</p><p>Results</p><p>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.</p><p>Conclusions</p><p>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.</p><p>4</p>

<p>Background: In P. falciparum a similarity between the transit peptides of apicoplast and mitochondrial proteins in the context of net positive charge has previously been observed in few proteins. Existing P. falciparum protein localization prediction tools were leveraged in this study to study this similarity in larger sets of these proteins.</p><p>Results: The online public-domain malarial repository PlasmoDB was utilized as the source of apicoplast and mitochondrial protein sequences for the similarity study of the two types of transit peptides. It was found that</p><p>many of the 551 apicoplast-targeted proteins (NEAT proteins) of PlasmoDB may have been wrongly annotated to localize to the apicoplast, as some of these proteins lacked annotations for signal peptides, while others also had annotations for localization to the mitochondrion (NEMT proteins). Also around 50 NEAT proteins could contain signal anchors instead of signal peptides in their N-termini, something that could have an impact on the current theory that explains localization to the apicoplast [1].</p><p>The P. falciparum localization prediction tools were then used to study the similarity in net positive charge between the transit peptides of NEAT and NEMT proteins. It was found that NEAT protein prediction tools like PlasmoAP and PATS could be made to recognize NEMT proteins as NEAT proteins, while the NEMT predicting tool PlasMit could be made to recognize a significant number of NEAT proteins as NEMT. Based on these results a conjecture was proposed that a single technique may be sufficient to predict both apicoplast and mitochondrial transit peptides. An implementation in PERL called TargetPf was implemented to test this conjecture (using PlasmoAP rules), and it reported a total of 408 NEAT</p><p>proteins and 1504 NEMT proteins. This number of predicted NEMT proteins (1504) was significantly higher than the annotated 258 NEMT proteins of plasmoDB, but more in line with the 1200 predictions of the tool PlasMit.</p><p>Conclusions: Some possible ambiguities in the PlasmoDB annotations related to NEAT protein localization were identified in this study. It was also found that existing P. falciparum localization prediction tools can be made to detect transit peptides for which they have not been trained or built for.</p>

<p>Proteins combine in various ways to execute different essential functions. Cellular processes are enormously complex and it is a great challenge to explain the underlying organization. Various methods have been applied in attempt to reveal the organization of the cell. Gene expression analysis uses the mRNA levels in the cell to predict which proteins are present in the cell simultaneously. This method is useful but also known to sometimes fail. Proteins that are known to be functionally related do not always show a significant correlation in gene expression. This fact might be explained by the dynamic organization of the proteome. Proteins can have diverse functions and might interact with some proteins only during a few time points, which would probably not result in significant correlation in their gene expression. In this work we tried to address this problem by combining gene expression data with data for physical interactions between proteins. We used a method for modular decomposition introduced by Gagneur et al. (2004) that aims to reveal the logical organization in protein-protein networks. We extended the interpretation of the modular decomposition to localize the dynamics in the protein organization. We found evidence that protein-interactions supported by gene expression data are very likely to be related in function and thus can be used to predict function for unknown proteins. We also identified negative correlation in gene expression as an overlooked area. Several hypotheses were generated using combination of these methods. Some could be verified by the literature and others might shed light on new pathways after additional experimental testing.</p>

Diss. - Abo Akademi University, 2008. / Includes bibliographical references.