Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that is otherwise obscured. The objective of this dissertation is to develop machine learning based effective tools to predict disordered protein, its properties and dynamics, and interaction paradigm by systematically mining and analyzing large-scale biological data. In this dissertation, we propose a robust framework to predict disordered proteins given only sequence information, using an optimized SVM with RBF kernel. Through appropriate reasoning, we highlight the structure-like behavior of IDPs in disease-associated complexes. Further, we develop a fast and effective predictor of Accessible Surface Area (ASA) of protein residues, a useful structural property that defines protein’s exposure to partners, using regularized regression with 3rd-degree polynomial kernel function and genetic algorithm. As a key outcome of this research, we then introduce a novel method to extract position specific energy (PSEE) of protein residues by modeling the pairwise thermodynamic interactions and hydrophobic effect. PSEE is found to be an effective feature in identifying the enthalpy-gain of the folded state of a protein and otherwise the neutral state of the unstructured proteins. Moreover, we study the peptide-protein transient interactions that involve the induced folding of short peptides through disorder-to-order conformational changes to bind to an appropriate partner. A suite of predictors is developed to identify the residue-patterns of Peptide-Recognition Domains from protein sequence that can recognize and bind to the peptide-motifs and phospho-peptides with post-translational-modifications (PTMs) of amino acid, responsible for critical human diseases, using the stacked generalization ensemble technique. The involved biologically relevant case-studies demonstrate possibilities of discovering new knowledge using the developed tools.

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics.

Ce travail présente un modèle théorique de morphogenèse animale, sous la forme d’un système complexe émergeant de nombreux comportements, processus internes, expressions et interactions cellulaires. Son implémentation repose sur un automate cellulaire orienté système multi-agents avec un couplage énergico-génétique entre les dynamiques cellulaires et les ressources.Notre objectif est de proposer des outils permettant l’étude numérique du développement de tissus cellulaires à travers une approche hybride (discrète/continue et qualitative/quantitative) pour modéliser les aspects génétiques, énergétiques et comportementaux des cellules. La modélisation de ces aspects s’inspire des principes de la théorie de la viabilité et des données expérimentales sur les premiers stades de division de l’embryon du poisson-zèbre.La théorie de la viabilité appliquée à la morphogenèse pose cependant de nouveaux défis en informatique pour pouvoir implémenter des algorithmes dédiés aux dynamiques morphologiques. Le choix de données biologiques pertinentes à considérer dans le modèle à proposer, la conception d’un modèle basé sur une théorie nouvelle, l’implémentation d’algorithmes adaptés reposant sur des processeurs puissants et le choix d’expérimentations pour éprouver nos propositions sont les enjeux fondamentaux de ces travaux. Les hypothèses que nous proposons sont discutées au moyen d’expérimentations in silico qui ont porté principalement sur l’atteignabilité et la capturabilité de formes de tissus ; sur la viabilité de l’évolution d’un tissu pour un horizon de temps ; sur la mise en évidence de nouvelles propriétés de tissus et la simulation de mécanismes tissulaires essentiels pour leur contrôlabilité face à des perturbations ; sur de nouvelles méthodes de caractérisation de tissus pathologiques, etc. De telles propositions doivent venir en appoint aux expérimentations in vitro et in vivo et permettre à terme de mieux comprendre les mécanismes régissant le développement de tissus. Plus particulièrement, nous avons mis en évidence lors du calcul de noyaux de viabilité les relations de causalité ascendante reliant la maintenance des cellules en fonction des ressources énergétiques disponibles et la viabilité du tissu en croissance. La dynamique de chaque cellule est associée à sa constitution énergétique et génétique. Le modèle est paramétré à travers une interface permettant de prendre en compte le nombre de coeurs à solliciter pour la simulation afin d’exploiter la puissance de calcul offerte par les matériels multi-coeurs. / This work presents a theoretical model of animal morphogenesis, as a complex system from which emerge cellular behaviors, internal processes, interactions and expressions. Its implementation is based on a cellular automaton oriented multi-agent system with an energico-genetic coupling between the cellular dynamics and resources. Our main purpose is to provide tools for the numerical study of tissue development through a hybrid approach (discrete/continuous and qualitative/quantitative) that models genetic, behavioral and energetic aspects of cells. The modeling of these aspects is based on the principles of viability theory and on experimental data on the early stages of the zebrafish embryo division. The viability theory applied to the morphogenesis, however, raises new challenges in computer science to implement algorithms dedicated to morphological dynamics. The choice of relevant biological data to be considered in the model to propose, the design of a model based on a new theory, the implementation of suitable algorithms based on powerful processors and the choice of experiments to test our proposals are fundamental issues of this work. The assumptions we offer are discussed using in silico experiments that focused on the reachability and catchability of tissue forms ; on the viability of the evolution of a tissue for a time horizon ; on the discovery of new tissue properties and simulation of tissue mechanisms that are fondamental for their controllability face to disruptions ; on new pathological tissue characterization methods, etc. Such proposals must come extra to support experiments in vitro and in vivo and eventually allow a better understanding of the mechanisms governing the development of tissues.In particular, we have highlighted through the computing of viability kernels the bottom causal relationship between the maintenance of cells according to available energy resources and the viability of the tissue in growth. The model is set through an interface that takes into account the number of cores to solicit for simulation in order to exploit the computing power offered by multicore hardware.

An object-oriented framework is presented for developing and simulating individual-based models of cell populations. The framework supplies classes to define objects called simulation channels that encapsulate the algorithms that make up a simulation model. These may govern state-updating events at the individual level, perform global state changes, or trigger cell division. Simulation engines control the scheduling and execution of collections of simulation channels, while a simulation manager coordinates the engines according to one of two scheduling protocols. When the ensemble of cells being simulated reaches a specified maximum size, a procedure is introduced whereby random cells are ejected from the simulation and replaced by newborn cells to keep the sample population size constant but representative in composition. The framework permits recording of population snapshot data and/or cell lineage histories. Use of the framework is demonstrated through validation benchmarks and two case studies based on experiments from the literature.

O processo de auto-organização da estrutura proteica a partir da cadeia de aminoácidos é conhecido como enovelamento. Apesar de conhecermos a estrutura tridimencional de muitas proteínas, para a maioria delas, não possuímos uma compreensão suficiente para descrever em detalhes como a estrutura se organiza a partir da sequência de aminoácidos. É bem conhecido que a formação de núcleos de estruturas locais, conhecida como estrutura secundária, apresenta papel fundamental no enovelamento final da proteína. Desta forma, o desenvolvimento de métodos que permitam não somente predizer a estrutura secundária adotada por um dado resíduo, mas também, a maneira como esse processo deve ocorrer ao longo do tempo é muito relevante em várias áreas da biologia estrutural. Neste trabalho, desenvolvemos dois métodos de predição de estruturas secundárias utilizando modelos com o potencial de fornecer informações mais detalhadas sobre o processo de predição. Um desses modelos foi construído utilizando autômatos celulares, um tipo de modelo dinâmico onde é possível obtermos informações espaciais e temporais. O outro modelo foi desenvolvido utilizando redes neurais residuais profundas. Com este modelo é possível extrair informações espaciais e probabilísticas de suas múltiplas camadas internas de convolução, o que parece refletir, em algum sentido, os estados de formação da estrutura secundária durante o enovelamento. A acurácia da predição obtida por esse modelo foi de ~78% para os resíduos que apresentaram consenso na estrutura atribuída pelos métodos DSSP, STRIDE, KAKSI e PROSS. Tal acurácia, apesar de inferior à obtida pelo PSIPRED, o qual utiliza matrizes PSSM como entrada, é superior à obtida por outros métodos que realizam a predição de estruturas secundárias diretamente a partir da sequência de aminoácidos. / The process of self-organization of the protein structure is known as folding. Although we know the structure of many proteins, for a majority of them, we do not have enough understanding to describe in details how the structure is organized from its amino acid sequence. In this work, we developed two methods for secondary structure prediction using models that have the potential to provide detailed information about the prediction process. One of these models was constructed using cellular automata, a type of dynamic model where it is possible to obtain spatial and temporal information. The other model was developed using deep residual neural networks. With this model it is possible to extract spatial and probabilistic information from its multiple internal layers of convolution. The accuracy of the prediction obtained by this model was ~ 78% for residues that showed consensus in the structure assigned by the DSSP, STRIDE, KAKSI and PROSS methods. Such value is higher than that obtained by other methods which perform the prediction of secondary structures from the amino acid sequence only.

Les interfaces protéine-protéine sont au cœur de nombreux mécanismes physiologiques du vivant. Les caractériser au niveau moléculaire est un donc enjeu crucial pour la recherche de nouveaux candidat-médicaments. Nous proposons ici de nouvelles méthodes d’analyse des interfaces protéine-protéine à visée pharmaceutique. Notre protocole automatisé détecte les interfaces au sein des structures de la Protein Data Bank afin de définir les zones d’interactions à potentiel pharmacologique, les cavités droguables, les ligands présents à l’interface ainsi que les pharmacophores directement déduits à partir des cavités. Notre méthode permet de réaliser un état de l’art des informations disponibles autour des interfaces protéine-protéine ainsi que de prédire de nouvelles cibles potentielles pour des molécules candidats médicaments. / Protein-protein interfaces are involved in many physiological mechanisms of living cells. Their characterization at the molecular level is therefore crucial in drug discovery.We propose here new methods for the analysis protein-protein interfaces of pharmaceutical interest. Our automated protocol detects the biologicaly relevant interfaces within the Protein Data Bank structures, droguables cavities, ligands present at the interface and pharmacophores derived directly from the cavities. Our method enables a state-of- the-art of all available structural information about protein-protein interfaces and predicts potential new targets for drug candidates.

<div>Chromosomal mechanisms of sex determination vary greatly in phylogenetically closely related species, indicative of rapid evolutionary rates. Sex chromosome karyotypes are generally conserved within families; however, many species have derived sex chromosome configurations. Insects display a plethora of sex chromosome systems due to rapid diversification caused by changes in evolutionary processes within and between species. A good example of such a system are insects in the blow fly family Calliphoridae. While cytogenetic studies observe that the karyotype in blow flies is highly conserved (five pairs of autosomal chromosomes and one pair sex chromosome), there is variation in sex determining mechanisms and sex chromosome structure within closely related species in blow flies. The evolutionary history of sex chromosomes in blow fly species have not been fully explored. Therefore, the objective of this research was to characterize the sex chromosome structures in four species of blow flies and investigate the selective forces which have played a role in shaping the diverse sex chromosome system observed in blow flies. The blow fly species used in this study are Phormia regina, Lucilia cuprina, Chrysomya rufifacies and Chrysomya albiceps. Phormia regina,and Lucilia cuprina have a heteromorphic sex chromosome system and are amphogenic (females produce both male and female offspring in equal ratio). In contrast, Chrysomya rufifacies and Chrysomya albiceps, have a homomorphic sex chromosome system, are monogenic (females produce unisexual progeny), have two types of females (arrhenogenic females – male producers and thelygenic females – female producers), and sex of the offspring is determined by the maternal genotype. </div><div>To accomplish these tasks, a total of nine male and female individual draft genomes for each of the four species (including three individual draft genomes of Chrysomya rufifacies – male, and the two females) were sequenced and assembled providing genomic data to explore sex chromosome evolution in blow flies. Whole genome analysis was utilized to characterize and identify putative sex chromosomal sequences of the four blow fly species. Genomic evidence confirmed the presence of genetically differentiated sex chromosomes in P. regina and L. cuprina; and genetically undifferentiated sex chromosomes in C. rufifacies and C. albiceps. Furthermore, comparative analysis of the ancestral Dipteran sex chromosome (Muller element F in Drosophila) was determined to be X-linked in P. regina and L. cuprina contributing to sex chromosome differentiation but not sex-linked in C. rufifacies and C. albiceps. Evolutionary pressures are often quantified by the ratio of substitution rates at non-synonymous (dN) and synonymous (dS) sites. Substitution rate ratio analysis (dN/dS) of homologous genes indicated a weaker purifying selection may have contributed to the loss of sex-linked genes in Muller element F genes of the undifferentiated sex chromosome as compared to the differentiated sex chromosome system. Overall, the results presented herein greatly expands our knowledge in sex chromosome evolution within blow flies and will reinforce the study of sex chromosome evolution in other species with diverse sex chromosome systems.</div><div><br></div>

The rationale behind targeted molecular therapy in cancer, oncogene addiction, is that tumors rely on driver oncogenes to control their proliferation and survival. Therefore, an efficacious targeted therapy should induce a dual, detrimental response to the tumor. While there have been clinical success stories using targeted therapies, even tumors that are initially sensitive invariably develop resistance. In the case of triple negative breast cancer (TNBC), despite extensive evidence pointing to its driver oncogene status, inhibitors of the Epidermal Growth Factor Receptor (EGFR) are considered clinically inefficacious. Resistance to EGFR inhibition has been predominantly described as due to genetic alterations. Yet it remains unclear why patients exhibiting the same dysregulated status of a driver oncogene react to targeted therapy, as in the case of EGFR-mutant non-small cell lung cancer, while others do not at all (i.e., TNBC). Furthermore, not all of resistance can be described by genetic alterations to EGFR, to its pathway effectors, or to compensatory pathways. Emerging data reveals that drugs can induce resistance by rewiring epigenomic, transcriptional, and translational regulatory mechanisms. Unfortunately, a major limitation in designing efficacious treatments is our inability to predict whether cell types can rewire in response to drug exposure. Therefore, it is necessary to elucidate mechanisms of growth and survival in cells that have undergone rewiring. This study characterized intrinsic resistance to EGFR inhibition in TNBC. We found that EGFR inhibition induces rewiring, which results in a resistant growth state that bypasses the EGFR-MAPK pathway as a whole. Additionally, we found that a tRNA-modifying complex masks the oncogene addiction status of EGFR in TNBC by stabilizing the protein abundance of a pro-survival protein. Importantly, this happens solely in the context of EGFR inhibition. Taken together, this study highlights potential therapeutic strategies for TNBC and strategies that can be used to improve our understanding of targeted therapy resistance, especially intrinsic resistance.

Magister Scientiae (Medical Bioscience) - MSc(MBS) / Background: Despite the effort of scientific research on HIV therapies and to reduce the rate of HIV infection, AIDS remains one of the major causes of death in the world and mostly in sub-Saharan Africa. To date, neither a cure nor an HIV vaccine had been found and the disease can only be managed by using High Active Antiretroviral Therapy (HAART) if detected early. The need for an effective early diagnostic and non-toxic treatment has brought about the necessity for the discovery of additional HIV diagnostic methods and treatment regimens to lower mortality rates. Antimicrobial Peptides (AMPs) are components of the first line of defense of prokaryotes and eukaryotes and have been proven to be promising therapeutic agents against HIV. Methods: With the utility of computational biology, this work proposes the use of profile search methods combined with structural modeling to identify putative AMPs with diagnostic and anti-HIV activity. Firstly, experimentally validated anti-HIV AMPs were retrieved from various publicly available AMP databases, APD, CAMP, Bactibase and UniProtKB and classified according to super-families. Hidden Markov Model (HMMER) and Gap Local Alignment of Motifs (GLAM2) profiles were built for each super-family of anti- HIV AMPs. Putative anti-HIV AMPs were identified after scanning genome sequence databases using the trained models, retrieved AMPs, and ranked based on their E-values. The 3-D structures of the 10 peptides that were ranked highest were predicted using 1-TASSER. These peptides were docked against various HIV proteins using PatchDock and putative AMPs showing the highest affinity and having the correct orientation to the HIV -1 proteins gp120 and p24 were selected for future work to establish their function in HIV therapy and diagnosis. Results: The results of the in silica analysis showed that the constructed models using the HMMER algorithm had better performances compare to that of the models built by the GLAM2 algorithm. Furthermore, the former tool has a better statistical and probability explanation compared to the latter tool. Thus only the HMMER scanning results were considered for further study. Out of 1059 species scanned by the HMMER models, 30 putative anti-HIV AMPs were identified from genome scans with the family-specific profile models after the elimination of duplicate peptides. Docking analysis of putative AMPs against HIV proteins showed that from the 10 best performing anti-HIV AMPs with the highest E-scores, molecules 1,3, 8, and 10 firmly bind the gp120 binding pocket at the VIN2 domain and the point of interaction between gp120 and T cells, with the 1st and 3rd highest scoring anti-HIV AMPs having the highest binding affinities. However, all 10 putative anti-HIV AMPs bind to the N-terminal domain of p24 with large surface interaction, rather than the C-terminal. Conclusion: The in silica approach has made it possible to construct computational models having high performances, and which enabled the identification of putative anti-HIV peptides from genome sequence scans. The in silica validation of these putative peptides through docking studies has shown that some of these AMPs may be involved in HIV/AIDS therapeutics and diagnostics. The molecular validation of these findings will be the way forward for the development of an early diagnostic tool and as a consequence initiate early treatment. This will prevent the invasion of the immune system by blocking the VIN2 domain and thus designing of a successful vaccine with broad neutralizing activity against this domain.

Molecular biology has entered an era of systematic and automated experimentation. High-throughput techniques have moved biology from small-scale experiments focused on specific genes and proteins to genome and proteome-wide screens. One result of this endeavor is the compilation of complex networks of interacting proteins. Molecular biologists hope to understand life's complex molecular machines by studying these networks. This thesis addresses tree open problems centered upon their analysis and quality assessment. First, we introduce power graph analysis as a novel approach to the representation and visualization of biological networks. Power graphs are a graph theoretic approach to lossless and compact representation of complex networks. It groups edges into cliques and bicliques, and nodes into a neighborhood hierarchy. We demonstrate power graph analysis on five examples, and show its advantages over traditional network representations. Moreover, we evaluate the algorithm performance on a benchmark, test the robustness of the algorithm to noise, and measure its empirical time complexity at O (e1.71)- sub-quadratic in the number of edges e. Second, we tackle the difficult and controversial problem of data quality in protein interaction networks. We propose a novel measure for accuracy and completeness of genome-wide protein interaction networks based on network compressibility. We validate this new measure by i) verifying the detrimental effect of false positives and false negatives, ii) showing that gold standard networks are highly compressible, iii) showing that authors' choice of confidence thresholds is consistent with high network compressibility, iv) presenting evidence that compressibility is correlated with co-expression, co-localization and shared function, v) showing that complete and accurate networks of complex systems in other domains exhibit similar levels of compressibility than current high quality interactomes. Third, we apply power graph analysis to networks derived from text-mining as well to gene expression microarray data. In particular, we present i) the network-based analysis of genome-wide expression profiles of the neuroectodermal conversion of mesenchymal stem cells. ii) the analysis of regulatory modules in a rare mitochondrial cytopathy: emph{Mitochondrial Encephalomyopathy, Lactic acidosis, and Stroke-like episodes} (MELAS), and iii) we investigate the biochemical causes behind the enhanced biocompatibility of tantalum compared with titanium.