This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties.

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Quantitative Structure Activity Relationship (QSAR) is a very useful computa-tional method which has facilitated great progress in drug development [1]. Thismethod can be used to predict a molecule’s activity against a certain target justby comparing its structural characteristics (i.e., molecular descriptors) with thosebelonging to molecules of known activity. QSAR modeling is fueled by online freedatabases consisting of millions of active and inactive molecules and by MachineLearning (ML) Methods that enable data analysis. To ensure successful implemen-tation of ML models, there is a range of evaluation methods to estimate their perfor-mance and applicability domain. So far, a great deal of research has focused on theuse of Support Vector Machines (SVMs) to classify molecules with the use of theirMolecular Signature Fingerprints as descriptors [2]. However, another MachineLearning algorithm, Deep Neural Networks (DNNs), an improvement of single-layer Neural Networks, is rising in popularity in various fields including moleculeclassification. The two models were compared using CPSign software which intro-duces Conformal Prediction, to evaluate the reliability of model predictions basedon performance for individual compounds rather than mean performance on agiven test set. Three types of descriptors were used: Molecular Signature Finger-prints, Extended Connectivity Fingerprints and physicochemical descriptors. Thecomparison showed that Multilayer Perceptron (MLP) which was used as a DNNrepresentative in current context, had performance similar to the shallower SVMmodels but additionally demanded longer training times [3]. It can be concludedthat in the field of QSAR with the aforementioned descriptors, when the numberof examples used for training is not immense, Support Vector Machines might per-form equally well and demand less resources and time than the more sophisticated MLPs.

In this project, a model based on a convolutional neural network have been developed with the aim of imputing missing genotype data. This model was based on an already existing autoencoder that was modified into a U-Net structure. The network was trained and used iteratively with the intention that the result would improve in each iteration. In order to do this, the output of the model was used as the input in the next iteration. The data used in this project was diploid genotype data, which was phased into haploids and then run separately through the network. In each iteration, the new haploids were generated based on the output haploids. These were used as in input in the next iteration. The result showed that the accuracy of the imputation improved slightly in every iteration. However, it did not surpass the same model that was trained for one single iteration. Further work is needed to make the model more useful.

<p>We introduce new software (ISAAC: Improved Structural Annotation of attC) to annotate cassette arrays in bacterial integrons by finding attI and attC sites, and to provide detailed annotation of the attC sites for analysis. We demonstrate an initial application of ISAAC by annotating the cassette complements of all the integrons we identified in the RefSeq bacterial genome database, and providing an analysis of the patterns of nucleotide frequencies at the structurally important positions in the attCs we’ve found.</p> / Master of Science (MSc)

NIBP is a prototype member of a novel protein family. It forms a novel subcomplex of NIK-NIBP-IKKB and enhances cytokine-induced IKKB-mediated NFKB activation. It is also named TRAPPC9 as a key member of trafficking particle protein (TRAPP) complex II, which is essential in trans-Golgi networking (TGN). The signaling pathways and molecular mechanisms for NIBP actions remain largely unknown. The aim of this research is to identify potential proteins interacting with NIBP, resulting in the regulation of NFKB signaling pathways and other unknown signaling pathways. At the laboratory of Dr. Wenhui Hu in the Department of Neuroscience, Temple University, sixteen partner proteins were experimentally identified that potentially bind to NIBP. NIBP is a novel protein with no entry in the Protein Data Bank. From a computational and bioinformatics standpoint, we use prediction of secondary structure and protein disorder as well as homology-based structural modeling approaches to create a hypothesis on protein-protein interaction between NIBP and the partner proteins. Structurally, NIBP contains three distinct regions. The first region, consisting of 200 amino acids, forms a hybrid helix and beta sheet-based domain possibly similar to Sybindin domain. The second region comprised of approximately 310 residues, forms a tetratrico peptide repeat (TPR) zone. The third region is a 675 residue long all beta sheet and loops zone with as many as 35 strands and only 2 helices, shared by Gryzun-domain containing proteins. It is likely to form two or three beta sheet sandwiches. The TPR regions of many proteins tend to bind to the peptides from disordered regions of other proteins. Many of the 16 potential binding proteins have high levels of disorder. These data suggest that the TPR region in NIBP most likely binds with many of these 16 proteins through peptides and other domains. It is also possible that the Sybindin-like domain and the Gryzun-like domain containing beta sheet sandwiches bind to some of these proteins. / Bioengineering

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This dissertation proposes tools and analysis of mutational signatures in human cancer and their application to the stratification of patients for drug response. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, I created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the COSMIC database, embed tumors in two dimensions with UMAP, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups, and plot exposure distributions across user-defined annotations such as tumor type. I then use musicatk to analyze the largest tumor sequencing dataset from a Chinese population to date. I identified differences in the levels of signature exposures compared to similar data from a Western cohort. Specifically, COSMIC signature SBS25 was higher in the Chinese dataset for Melanoma and Renal Cell Carcinoma patients and Melanoma patients had lower levels of SBS7a/b (Ultraviolet Light). My analysis also revealed a putative novel signature enriched in pancreatic cancers. Lastly, I assess the ability of mutational signatures to identify patients who may respond to irofulven, a drug for late-stage cancer patients who have defects in the Transcription Coupled Nucleotide Excision Repair (TC-NER) pathway. As the functional understanding of which mutations successfully disrupt this pathway is incomplete, I develop an approach that classifies patients based on evidence of this pathway being disrupted based on levels of mutational signatures. I build a model that successfully predicts patients who will respond to treatment without a known relevant mutation in the TC-NER pathway. The work from this study furthers our understanding of mutational signatures in different populations and demonstrates the feasibility of using mutational signatures to identify patients eligible for drug trials.

One of the principal impediments to the broad success of conventional chemotherapy is poor delivery to and transport within the tumor microenvironment (TME), caused by irregular and leaky vasculature, the lack of functional lymphatics, and underscored by the overproduction of extracellular matrix (ECM) proteins such as collagen. Coupled with limited specificity, the high chemotherapeutic doses needed to effectively treat tumors often lead to unacceptable levels of damage to healthy tissues. Bacteria-based cancer therapy (BBCT) is an innovative alternative. Attenuated strains of species such as Salmonella Typhimurium have been shown to preferentially replicate in the TME, competing for cellular resources and imparting intrinsic and immune-mediated cytotoxic effects on cancer cells. Nevertheless, the immense successes observed in in vitro and immunocompromised murine models have not translated to the clinic, attributable to the lack of sufficient tumor colonization. Synthetic biology today enables the precision engineering of microbes with traits for improved survival, penetration, and replication in the TME, rationally optimizable through computational modeling. In this dissertation, we explore several facets of rationally engineering of bacteria toward augmenting bacterial penetration and retention in the TME. Namely, we (1) develop a novel assay to interrogate the neutrophil migratory response to pathogens and characterize the effects of modifying the molecular structure of the outer membrane (OM) of S. Typhimurium, (2) develop a mathematical model of bacterial intratumoral transport and growth and explore the effects of nutrient availability and the tumor ECM on colonization, (3) engineer bacteria that constitutively secrete collagenase and show significantly augmented transport in collagen hydrogels and collagen-rich tumor spheroids, and (4) develop computational models to explore control schemes for the programmed behavior of bacteria-based biohybrid systems, which will leverage the engineered bacteria to deliver therapeutics to the TME. Our work serves as the foundation for the logical and efficient design of the next generation of BBCTs. / Doctor of Philosophy / Cancer is one of the deadliest diseases facing our world today not because of a lack of effective medications in most cases, but because of our inability to target the diseased sites with those treatments. Many tumors lie in deep and sensitive regions that render them untouchable by direct physical means. Poor vascularization leads to only small fractions of toxic, systemically injected drugs being deposited in tumors. State-of-the-art treatments such as so-called "nano-medicines" that can target features of the diseased tissues and immunotherapies that train the immune system to attack tumor cells have made tremendous strides, but for many types of cancer, the underlying challenge of reaching cells far from blood vessels and targeting immunologically cold tumors remains. Bacteria-based cancer therapy (BBCT) presents an exciting opportunity to address these challenges. Based on microorganisms that can self-propel, proliferate, and display a preference for diseased tissues, their potential not only to carry chemotherapeutic payloads but also to elicit directly toxic or immunotherapeutic effects on cancer cells is clear from experimental work. Nevertheless, the same delivery and transport barriers facing other treatments, as well as immune-mediated clearance, have limited BBCTs' clinical success. Advances in synthetic biology and computational modeling today make the precision engineering of BBCT for traits that favor targeted cancer therapy a reality. The central hypothesis of this dissertation is that endowing tumor-targeting bacteria with a tissue-degrading enzyme has the potential to enhance tumor penetration and colonization. This dissertation work has led to development of computational and experimental frameworks for the design, testing, and optimization of BBCTs through direct quantitative assessment of the immune response, simulations to both optimize nutrient consumption for optimal growth and for programming genetic control strategies, and characterization of transport in tissue. Our work serves as a foundation for engineering "intelligent" BBCT.