<p>  </p> <p>Ebola virus (EBOV) infection remains to be a challenge to human health by its high mortality rate. Though it has been discovered for almost 50 years, there are only two antibody-based therapies approved today, and the mortality rate is still greater than 30% with the treatment. Authentic EBOV studies are strictly limited to biosafety level-4 (BSL-4) labs, which slows the development of treatment. While more simple and safer systems have been developed to understand different stages of EBOV infection, such as the matrix protein (VP40) virus-like particle (VLP) and minigenome systems, we still lack a systematical view of EBOV infection. On the other hand, mathematical modeling has been used to assist biological and medical studies for many years, as it has the advantage of integrating data and providing quantitative insight to a biosystem. In our study, we took advantage of mathematical modeling and build the primary ordinary differential equation-based (ODE-based) model of EBOV at subcellular level step by step. We built the budding pathway of EBOV VP40 first, calibrated and validated our model with experimental data. We proposed that phosphatidylserine (PS) can directly influence the stability of VP40 filaments and the budding process of VLPs. Also, the oligomerization of VP40 filaments may follow the nucleation-elongation process. Next, we conducted in-silico simulation to evaluate the treatment efficiency of fendiline, a drug lowering cell membrane PS level, in treating EBOV. We found that while in general, fendiline can decrease VLP production, there can be fendiline-induced VLP production increases at certain time points due to slow filament growth or fast VLP budding rates. Also, we concluded that fendiline is relatively more effective when applied in the budding stage of EBOV life cycle. Moreover, fendiline efficacy may increase when applied with a VLP budding step targeted treatment. Finally, we integrated nucleoprotein (NP) into our model. We reproduced the two-stage interaction between NP and VP40 and predict that NP increases VLP production through influencing filament oligomerization and VLP budding steps. Also, the dual-effect of NP on VLP production may exist, as a too high NP/VP40 production ratio can decrease VLP production. From the aspect of protein expression time, we found that a bit earlier NP production than VP40 production is beneficial for both inclusion body-containing (IB-containing) VLP production and prevention in energy waste on production of VLPs without IBs. Overall, we have built a solid foundation towards a mathematical EBOV model and demonstrated the value of models in assisting experimental EBOV studies.</p>

Biological data topic modeling has become a very prevalent topic among researchers in recent times. However, analysing countless research papers and gathering consensus regarding biomedicine is a near-impossible task for any researcher due to the complexity and quantity of material that is published. This thesis is devised to focus on two objectives that can help the researchers in this domain based on data related to five major DNA repair pathways. The first objective is to propose an unsupervised approach to examine the hidden structures and analyse research trends in temporal biomedical text data. The second objective is to find DNA repair markers involved in immune defense and retrieve potential PPIs, GIs, and disease-gene associations reported in the literature. We have used latent Dirichlet Allocation (LDA) to discover hidden themes and semantically coherent topics from text. We have clustered the documents based on LDA topic models to analyse the research trend and used the Mann- Kendall test to understand the trends of the topics. Hybridization of text mining methods with classical co-occurrence statistical approach and association rule mining was used to discover potential PPIs, GIs, and disease-gene association in the text. The results for PPIs and GIs were then evaluated with an external biological database of PPIs.

This thesis presents a novel approach to reconstructing ancestral genomes of a number of descendant species related by a phylogeny. Traditional methods face challenges due to cycles of whole genome doubling followed by fractionation in plant lineages. In response, the thesis proposes a new approach that first accumulates a large number of candidate gene adjacencies specific to each ancestor in a phylogeny. A subset of these which to produces long ancestral contigs are chosen through maximum weight matching. The strategy results in more complete reconstructions than existing methods, and a number of quality measures are deployed to assess the results. The thesis also presents a new computational technique for estimating the ancestral monoploid number of chromosomes, involving a "g-mer" analysis to resolve a bias due to long contigs and gap statistics to estimate the number. The method is applied to a set of phylogenetically related descendant species, and the monoploid number is found to be 9 for all rosid and asterid orders. Additionally, the thesis demonstrates that this result is not an artifact of the method, by deriving a monoploid number of approximately 20 for the metazoan ancestor. The reconstructed ancestral genomes are functionally annotated and visualized through painting ancestral projections on descendant genomes and highlighting syntenic ancestor-descendant relationships. The proposed method is applied to genomes drawn from a broad range of plant orders. The Raccroche pipeline reconstructs ancestral gene orders and chromosomal contents of the ancestral genomes at all internal vertices of a phylogenetic tree, and constructs chromosomes by counting the frequencies of ancestral contig co-occurrence on the extant genomes, clustering these for each ancestor, and ordering them. Overall, this thesis presents a significant contribution to the field of ancestral genome reconstruction, offering a new approach that produces more complete reconstructions and provides valuable insights into the evolutionary process giving rise to the gene content and order of extant genomes.

Quorum sensing (QS) is a type of microbial communication used by bacteria to coordinate their behavior based on population density, regulating complex processes like biofilm formation and virulence, among other behaviors. Quorum quenching (QQ), on the other hand, disrupts this communication, usually by degradation of the QS signaling molecule. QQ offers a potential strategy for controlling bacterial behaviors linked to pathogenicity and biofouling. Despite significant advances in understanding and modeling the spatial-temporal behavior of QS, predictive modeling of QQ remains nascent, with a notable gap in the quantitative assessment of QQ's impact on QS. Here we show quantitative evaluation and characterization of the effect of QQ on QS in agar-based experiments, combined with an experimentally validated computational model. This research utilizes green fluorescence in E. coli MG 1655 as an indicator of QS activation, focusing on the degradation of Acyl-Homoserine Lactone (AHL), a key QS molecule in Gram-negative bacteria linked to pathogenicity, by the AiiA enzyme in engineered AiiA-producing Salmonella Typhimurium 14028. Our findings suggest that QQ more effectively influences QS in spatial configurations of the populations with larger interaction surfaces and shorter diffusion distances. Contrary to our initially held hypothesis, the primary effect of QQ is not a delay in QS onset but rather an attenuation of QS activity, with the area-under-the-curve of fluorescence serving as a quantitative metric. This study also introduces, to the best of our knowledge, one of the first instances of experimentally validated predictive modeling for QQ, applied to agar-based experimental setups. We posit that the quantitative experimental characterization and modeling framework presented in this research will enhance the understanding of bacterial community interactions. Enhanced comprehension of QQ and QS behaviors holds significant promise for advancing practical applications, particularly in mitigating or diminishing undesirable QS-associated activities. This is especially relevant in areas like biofouling, waste treatment, and the reduction of infections and progression of diseases in plants and animals, areas increasingly important as concerns about drug resistance in microbes and food security escalates. / Master of Science / One of the ways bacteria communicate with each other is called quorum sensing (QS), where they use chemical signals to organize and time group behavior, including forming communities encapsulated in protective layers, called biofilms, and engaging in virulent attacks against hosts. Quorum quenching (QQ) in bacteria, however, disrupts this communication system, usually by breaking down the chemical signals that bacteria use to send messages to each other. Even though QS has been studied extensively, determining how to predict and control QQ is still a nascent area of research. Here, we studied and characterized how QQ affects QS by doing experiments with bacteria populations in agar (a jelly-like substance) and applied a computational model to explain and ultimately predict the experimental observations. Engineered QS population (E. coli MG 1655) produced Acyl-Homoserine Lactone (AHL) signaling molecules, and engineered QQ bacteria (S. Tm 14028) used the Autoinducer Inactivation A (AiiA) enzyme to break down the AHL. According to our results, QQ doesn't delay the QS bacteria's group behaviors (in our case, green fluorescent signal production); it weakens the signal instead. Understanding QQ and QS better, especially through measurements and modeling, could lead to expanded methods of deterring harmful bacterial behavior, managing waste better, and stopping diseases in plants, animals, and humans, especially with the concerning rise of drug-resistant microbes and food security. One exciting possibility is using QQ to protect plants from bacterial infections. This could be a way to shield our crops without always relying on antibiotics.

Yes / Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms. / NSFC Project (61105055, 81230086), China 973 Program (2014CB542903), The National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), the National S&T Major Special Project on Major New Drug Innovation (2012ZX09503-001-003), and the Fundamental Research Funds for the Central Universities.

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Short open reading frames (sORFs) are codon sequences with a start and stop codon within atmost 100 codons. Cells produce many transcripts from them and some sORFs have been found to have function. sORFs have been associated with embryogenesis, myogenesis, immunity and various diseases including cancers. Cell culture screening is a common method to study function in sORFs. By inserting mutations in known sORF locations one can affect their translation by removing start codons, inserting premature stop codons, or removing native stop codons. A new tool set to do this isCRISPR technology, where single guide RNA (gRNA) can be used to make more precise genome edits. Unfortunately, such design is nontrivial and suggests a lot of variants for testing. It results in a back-and-forth testing process involving different available design tools. In this project, a comprehensive way was developed to see and iterate over the many test combinations. This intends to ease the process and decrease the likelihood for errors. The developed solution is a tool that integrates the currently best design tools. It also introduces a method in the form of a new quality summary score that can evaluate the estimated outcomes of the various designed guide variants. The tool was tested, and it was found that the score simplifies and amplifies the earlier usedscore methods. The pipeline is simple to install and use, integrates the currently most actively developed tools, and an installation is as future proof as can be made in a rapidly evolving field.

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