<p>This dissertation focuses on Boolean networks with a view to their applications in Systems Biology. We study two notions of stability, based on Hamming distance and on maintenance of a stable period length. Algorithms are given for the determination of Boolean networks from both complete and partial dynamics. The dynamics of ring networks are systematically studied. An algebraic structure is developed for derivation of adjacency matrices for the dynamics of Boolean networks from simple building blocks, both by edge-swapping and by gluing simple building blocks. Some results are implemented in Python and conclusions drawn for theta networks, a class of networks only slightly more complex than rings. A short section on applications to a known biological system closes the dissertation.</p> / Dissertation

<p>Periodic oscillations underlie many intracellular functions, such as circadian time keeping, cell cycle control and locomotor pattern generation in nerve cells. These intracellular oscillations are generated in intricate biochemical reaction networks involving genes, proteins and other biochemical components. In most cases, robust oscillations are of pivotal importance for the organism,<i> i.e.</i>, the oscillations must be maintained in the presence of internal and external perturbations.</p><p>Model based analysis of robustness in intracellular oscillators has attracted considerable attention in recent years. The analysis has almost exclusively been based on either complete removal of network components,<i> e.g.</i>, single genes, or perturbation of model parameters. In this thesis, a control theoretic approach to analyze structural robustness of intracellular oscillators is proposed. The method is based on adding dynamic perturbations to the network interactions. Determination of the smallest perturbation translating the underlying steady-state into a Hopf bifurcation point is used to quantify the robustness. The method can be used to determine critical substructures within the overall network and to identify specific network fragilities. Also, an approach to nonlinear model reduction based on the robustness analysis is proposed.</p><p>The proposed robustness analysis method is applied to elucidate mechanisms underlying robust oscillations in circadian clocks. Circadian clocks, molecular oscillators generating 24 hour rhythms in many organisms, are known to display a striking robustness towards internal and external perturbations. The underlying networks involve a large number of genes that are transcribed into mRNA which produce proteins subsequently regulating the activity of other genes, together forming an intricate network with a large number of embedded feedback loops. An often recurring hypothesis is that the interlocked feedback loop structure of circadian clocks serves the purpose of robustness. From analysis of several recently published models of circadian clocks, it is found in this thesis that the robustness of circadian clocks primarily results from a high gain in a single gene regulatory feedback loop generating the oscillations. This gain can be elevated by additional feedback loops, involving either gene regulation or post-translational feedback, but a similar robustness can be achieved by simply increasing the amplification within the master feedback loop.</p>

<p>We work on constructing mathematical models of gene regulatory networks for periodic processes, such as the cell cycle in budding yeast, using biological data sets and applying or developing analysis methods in the areas of mathematics, statistics, and computer science. We identify genes with periodic expression and then the interactions between periodic genes, which defines the structure of the network. This network is then translated into a mathematical model, using Ordinary Differential Equations (ODEs), to describe these entities and their interactions. The models currently describe gene regulatory interactions, but we are expanding to capture other events, such as phosphorylation and ubiquitination. To model the behavior, we must then find appropriate parameters for the mathematical model that allow its dynamics to approximate the biological data. </p><p>This pipeline for model construction is not focused on a specific algorithm or data set for each step, but instead on leveraging several sources of data and analysis from several algorithms. For example, we are incorporating data from multiple time series experiments, genome-wide binding experiments, computationally predicted binding, and regulation inference to identify potential regulatory interactions.</p><p>These approaches are designed to be applicable to various periodic processes in different species. While we have worked most extensively on models for the cell cycle in <italic>Saccharomyces cerevisiae</italic>, we have also begun working with data sets for the metabolic cycle in <italic>S. cerevisiae</italic>, and the circadian rhythm in <italic>Mus musculus</italic>.</p> / Dissertation

Each cancer is unique: it reflects the underlying genetic make-up of the patient and the stochastic mutational processes that occur within the tumour. This uniqueness suggests that each patient should receive a personalized type of therapy. Current predictions of a cancer patient’s outcome or prognosis are highly inaccurate. To aid in the prediction of patient prognosis based on highthroughput molecular datasets I have worked to optimize each step of the experimental pipeline: platform annotation, experimental design, consideration of tumour heterogeneity, data pre-processing and statistical analysis, and feature selection. First, a 12k CpG Island clone library was sequenced and annotated using a BLAT analysis. Second, microarrays built using this library were used in a fully-saturated study to evaluate the importance of ChIP-chip experimental design parameters. Third, intra-tumour heterogeneity was shown to influence specific pathways in a large fraction of genes. Fourth, a systematic empirical evaluation of 19,446 combinations of microarray analysis methods identified key steps of the analysis process and provided insight into their optimization. Finally, the combination of a two-stage experimental design and a novel semi-supervised algorithm yielded a six-gene, mRNA abundance-based classifier that could divide non-small cell lung cancer patients into two groups with significantly different outcomes in four independent validation cohorts. Further, a permutation study showed that millions of six-gene markers exist, but that ours ranked amongst the top 99.98% of all six-gene markers. The knowledge gained from these studies provides a key foundation for the development of personalized therapies for cancer patients.

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.

Thesis (Ph.D.)--Drexel University, 2009. / Includes abstract and vita. Includes bibliographical references (leaves 112-122).

Proteins are essential in all living organisms. They are involved in various critical activities and are also structural components of cells and tissues. Lactose permease a membrane protein has become a prototype for the major facilitator super family and utilises an existing electrochemical proton gradient to shuttle galactoside sugars to the cell. Therefore it exists in two principle states exposing the internal binding site to either side of the membrane. From previous studies it has been suggested that protonation precedes substrate binding but it is still unclear why this has to occur in the event of substrate binding. Therefore this study aimed to bridge this gap and to determine the chemical characteristics of the transport pathway. Molecular dynamics simulation methods and specialised simulation hardware were employed to elucidate the dependency of substrate binding on the protonation nature of Lactose permease. Protein models that differed in their conformation as well as their protonation states were defined from their respective X-ray structures. Targeted molecular dynamics was implemented to drive the substrate to the binding site and umbrella sampling was used to define the free energy of the transport pathway. It was therefore suggested that protonation for sugar binding is due to the switch-like mechanism of Glu325 in the residue-residue interaction (His322 and Glu269) that leads to sugar binding only in the protonated state of LacY. Furthermore, the free energy profile of sugar transport path way was lower only in the protonated state which indicates stability of sugar binding in the protonated state.

Celiac disease (CD) is a chronic autoimmune disease, caused by the consumption of gluten in genetically predisposed individuals. Celiac patients develop many clinical features include; weight loss, diarrhea, and Intestinal damage, and if left untreated, CD patient may face an increased risk of malignancies. Materials and methods403 patient were admitted to the study. These patients were divided into three groups; celiac cases, controls, and latent celiac cases. Gene expression analysis was performed for intestinal biopsies and blood samples (leukocytes) using a quantitative PCR technique. The second section of the study was studying the effect of PRODH enzyme on Drosophila Melanogaster intestines. To achieve that PRODH enzyme and different amino acids were added to the fly food.  One way ANOVA and Wilcoxon tests were applied to find out the significant genes. ResultsMost of the differentially expressed genes in celiac disease are involved in the inflammatory response. However, many genes have significantly altered expression in the latent celiac group but not altered significantly in CD group. These genes are CXCL1, IL15RA, IL2RB, MAPK11, and TGM2. They are involved in the TNF signaling pathway and in inflammatory cytokines. It was noticed that in celiac disease there is a significant alteration in PRODH expression in the intestines, and the addition of PRODH enzyme to glutamine has a similar effect on the intestinal gene expression as gluten does. ConclusionWe can conclude that Non-HLA genes are important in activating the immune system, increasing proline level, and developing the clinical features of celiac disease. Secondly,  Proline metabolism has an important role in tumor suppression and in augmenting tumor growth, which makes it an important therapeutic target in tumor therapy.

The green alga Chlamydomonas reinhardtii can be grown phototrophically using light as an energy source or mixotrophically using reduced carbon in the form of acetate in addition to light. Acetate, despite increasing biomass, also inhibits photosynthesis as compared to cells grown phototrophically. A better understanding of acetate assimilation and how it regulates photosynthesis would enable a more efficient conversion of carbon into valuable products such as biofuels. In this thesis constraint-based modelling techniques are used in conjunction with a genome-scale model of the organism and experimental data to understand this phenomenon. Using flux balance analysis we show that the preferred route of acetate assimilation is likely to be via the enzyme acetyl-CoA synthase, and that exogenous acetate feeds into a modified tricarboxylic acid cycle, which bypasses the CO2 evolution steps. This is consistent with experimental data and explains increases in biomass with mixotrophic growth on acetate in comparison to phototrophic metabolism. Using a cycle decomposition algorithm with a mass-consistent adaptation of the model we were able to examine the role of cycles that further theoretically explain the down-regulation of photosynthesis observed when cells are grown in the presence of acetate. These results suggest that acetate modulates changes in the oxidative pentose phosphate pathway and increases mitochondrial respiration activity. Label-free proteomics was used to quantify 2951 polypeptides with various roles including the assimilatory route of acetate, photosynthesis, the Calvin–Benson cycle, central carbon metabolism and oxidative phosphorylation. We show how acetate assimilation induces a shift in central carbon metabolism to activate the oxidative pentose phosphate pathway. This results in the cycling of electrons around Photosystem I, which accounts for the down-regulation of photosynthesis.

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