Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Microbial stress. From sensing to intracellular and population responses

We initially devised this Research Topic (RT) as a valuable initiative to collect high-quality scientific articles from the participants of the 4th European Federation of Biotechnology (EFB) Microbial Stress meeting held in Kinsale, Ireland, April 2018. The scope of the RT is based on the scientific content of that “Microbial Stress: from Systems to Molecules and back” meeting. Indeed, over 40% of the articles eventually accepted for publication were contributed by meeting participants, but notably the remaining 60% was contributed by authors that work in this field. The collection of 22 original research and 2 review articles, contributed by 163 authors collectively, deal with the many different aspects of the microbial responses to biotic and abiotic stresses, relevant to many fields: from host-pathogen interactions to biotechnology, from bioremediation to food processing, from molecular and single-cell to population studies. The RT showcases the rapid developments of the microbial stress research on a range of microorganisms and stress conditions, and confirms that understanding microbial physiology under stress can be a trigger for the development of new methodologies as well as helping to integrate the knowledge from many different microbiological fields of research. The retrospective analysis of the articles contributed to this RT allowed them to be assigned to one of four main sub-topics: (i) impact of weak organic acids and low pH on micro-organisms, from clinical to biotechnological contexts; (ii) adaptive responses in microbial pathogens to abiotic/environmental stress; (iii) oxidative and metal stress, from clinical to bioremediation contexts, and (iv) regulation of transcription and translation under stress, from epigenetic aspects to the role of second messengers and sRNA

Modélisation et commande robuste des systèmes biologiques : exemple de la production d’acide lactique en fermenteur industriel

This PhD thesis focuses on the optimization of the bioprocess of lactic acid production from wheat flour. Indeed, lactic acid has received much attention for the production of PLA (Poly Lactic Acid), a biopolymer, since different inexpensive raw material such as wheat flour are now used as carbon source for its production. This work was performed in three main steps. In the first step, an innovative wheat transformation process is proposed, whose main steps are the following: a liquefaction followed by a simultaneous saccharification, proteins hydrolysis (SSPH) and and a final simultaneous saccharification, proteins hydrolysis and fermentation (SSPHF). Secondly, the modeling of the SSPHF (limiting step) in a continuous bioreactor is considered. The determination and validation of model parameters is performed by means of experimental campaigns in a 5 L bioreactor.In the last step, the development of control strategies to maintain the process at its optimal operating point is considered. To do so, due to the absence of sensors for real-time measurement of the concentrations of key variables of the bioreactor, estimators of these concentrations and of the lactic acid production rate are first developed. Then, control strategies for regulating the lactic acid concentration at its optimal value are designed and compared in simulation. An adaptive control combining a state feedback linearizing control and an estimator of the lactic acid production rate is finally chosen to be experimentally validated on an instrumented reactor. This strategy showed good robustness features with respect to modeling mismatches and was able during experiments to increase twice the lactic acid productivity.Cette thèse de doctorat porte sur l’optimisation du bioprocédé de production d’acide lactique à partir de la farine de blé. L'acide lactique s’avère en effet de plus en plus attractif pour la production de PLA (acide poly lactique), un bio polymère, d’autant plus que différentes matières premières peu coûteuses comme la farine de blé sont désormais utilisées comme sources de carbone pour sa production. Cette thèse comprend trois parties principales. Une première partie propose pour l’optimisation du procédé de transformation du blé un schéma innovant composé de trois étapes successives : une liquéfaction, suivi d’une étape de saccharification et hydrolyse des protéines simultanées (SSPH) et une étape finale de saccharification, hydrolyse des protéines et fermentation simultanées (SSPHF). La deuxième partie s’intéresse à la modélisation de l’étape SSPHF (étape limitante) dans un bioréacteur continu. La détermination des paramètres du modèle ainsi que leur validation sont réalisées à l’aide de campagnes d’essais sur un bioréacteur de 5 L.Enfin, la dernière partie développe la mise en oeuvre de stratégies de commande permettant de maintenir le bioprocédé à son point optimal de fonctionnement. Pour ce faire, du fait de l’absence de capteurs pour la mesure en temps réel des concentrations des variables clé dans le bioréacteur, des estimateurs de ces concentrations ainsi que du taux de production en acide lactique sont tout d’abord élaborés. Des stratégies de commande régulant la concentration d’acide lactique à sa valeur optimale sont ensuite synthétisées et comparées en simulation. Une commande adaptative combinant une commande linéarisante par retour d’état et un estimateur du taux de production en acide lactique est finalement retenue et validée expérimentalement sur un réacteur instrumenté. Cette dernière s’est avérée robuste vis-à-vis des erreurs de modélisation et a permis lors des expériences de doubler la productivité de l’acide lactique

Novel bioinformatics tools for epitope-based peptide vaccine design

BACKGROUND T-cells are essential in the mediation of immune responses, helping clear bacteria, viruses and cancerous cells. T-cells recognise anomalies in the cellular proteome associated with infection and neoplasms through the T-cell receptor (TCR). The most common TCRs in humans, αβ TCRs, engage processed peptide epitopes presented on the major histocompatibility complex (pMHC). TCR-pMHC interaction is critical to vaccination. In this thesis I will discuss three pieces of software and outcomes derived from them that contribute to epitope-based vaccine design. RESULTS Three pieces of software were developed to help scientists study and understand T-cell responses. The first, STACEI allows users to interrogate the TCR-pMHC crystal structures. The time consuming, error-prone analysis that previously would have to be ran manually, is replaced by a single, flexible package. The second development is the introduction of general-purpose computing on the GPU (GP-GPU) in aiding the prediction of T-cell epitopes by scanning protein datasets using data derived from combinatorial peptide libraries (CPLs). Finally, I introduce RECIPIENT, a reverse vaccinology tool (RV) that combines pangenomic and population genetics methods to predict good vaccine targets across multiple pathogen samples. CONCLUSION Across this thesis, I introduce three different methods that aid the study of T-cells that will hopefully improve future vaccine design. These methods range across data types and methodologies, with methods focusing on mechanistic understanding of the TCR-pMHC binding event; the application of GP-GPU to CPLs and using microbial genomics to aid the study and understanding of antigen-specific T-cell responses. These three methods have a significant potential for further integration, especially the structural methods