Data based system design and network analysis tools for chemical and biological processes

Ph.DDOCTOR OF PHILOSOPH

Contributions of Graph Theory and Algorithms to Animal Behaviour and Neuroscience

Η θεωρία γραφημάτων και οι αλγόριθμοι προσφέρουν πολύτιμες εργαλειοθήκες για τη μοντε- λοποίηση καθώς και την ανάλυση πολυάριθμων φαινομένων στις φυσικές επιστήμες. Εδώ παρουσιάζεται μια ανασκόπηση της σύγχρονης βιβλιογραφίας, χωρισμένη σε τέσσερα κύρια κεφάλαια, δίνοντας κάποιες ενδείξεις για το πώς οι έννοιες αυτών των δύο κλάδων μπορούν να χρησιμοποιηθούν για τη μελέτη της συμπεριφοράς των ζώων και της νευροεπιστήμης. Κατ ’εξαίρεση, το πρώτο μέρος του πρώτου κεφαλαίου παρέχει μια σύντομη συζήτηση σχετικά με τις εφαρμογές της θεωρίας γραφημάτων στη μοριακή βιολογία. Η επιλογή αυτή έγινε προκειμένου να καταστεί η εργασία αυτή πληρέστερη και να δοθεί στους αναγνώστες με διαφορετικό υπόβαθρο, όσο το δυνατόν περισσότερο, συνολική άποψη για τη δυνητική χρησι- μότητα τέτοιων διεπιστημονικών προσεγγίσεων. Τα υπόλοιπα δύο τμήματα του πρώτου κεφα- λαίου εστιάζουν σε δίκτυα του εγκεφάλου και σε κεντρικές έννοιες της θεωρίας γραφημάτων, όπως η κεντρικότητα, στη μελέτη τους. Το δεύτερο κεφάλαιο εισάγει μερικές έννοιες της κοινωνικότητας των ζώων και αναφέρεται σε μελέτες της συνεργασίας στο ζωικό βασίλειο, εστιάζοντας στην εξελικτική θεωρία γραφημάτων και παιγνίων. Επιπλέον, στη τελευταία ενότητα αυτού του κεφαλαίου συζητείται η συλλογική κίνηση ομάδων ζώων, παρέχοντας εκτός των άλλων, εισαγωγή βασικών όρων για το επόμενο τρίτο κεφάλαιο. Η διεπιστημονική έρευνα, με στόχο την ενοποίηση μεθόδων από διαφορετικούς τομείς, λαμβάνει χώρα ευρέως για να απαντήσει βιολογικά ερωτήματα. Εντούτοις, όπως παρουσιάζεται παρακάτω, η έρευνα στους αλγορίθμους και στη βιολογία μπορούν να συμβάλλουν στην ανάπτυξη η μια της άλλης. Ως εκ τούτου, το τρίτο κεφάλαιο παρέχει πληροφορίες σχετικά με αλγόριθμους των οποίων ο σχεδιασμός έχει εμπνευστεί από τη (συλλογική) συμπεριφορά των ζώων στο φυσικό περιβάλλον. Τέλος, το τέταρτο κεφάλαιο αποκλίνει εκ νέου από το επίκεντρο των προηγούμε- νων κεφαλαίων και κάνει μια σύντομη εισαγωγή στο σημαντικό, αλλά και αμφιλεγόμενο, υπολογιστικό χαρακτήρα της νόησης και κατ’ επέκταση της συμπεριφοράς. Συνολικά, μπορεί κανείς να παρατηρήσει ότι η συνεργασία των προαναφερθέντων πεδίων είναι εκτεταμένη ενώ η πραγματοποιημένη έρευνα ανοίγει νέα ερωτήματα που μπορούν να μελετηθούν μόνο υπό το φως τέτοιων διεπιστημονικών συνεργασιών.Graph theory and algorithms offer precious toolboxes for the modelling as well as the analysis of numerous phenomena in natural sciences. Here a review of the modern bibliography is pre- sented, divided in four main chapters, giving some indications on how the concepts of these two disciplines can be used for the study of animal behaviour and neuroscience. As an exception the premier part of the first chapter provides a short discussion on the applications of graph theory on molecular biology. This choice made in order to make this work more complete and give to the readers from various backgrounds an, as much as possible, overall view of the future potential of such interdisciplinary approaches. The rest two sections of the first chapter deals with brain networks and central terms of graph theory, such as centrality, in their study. The second chapter introduces some concepts of animal sociality and refers to studies of animal cooperation, focusing on evolutionary graph and game theory. Moreover, in the last section of this chapter the collective motion of animal groups is discussed providing, into the bargain, an introduction of basic terms for the subsequent third chapter. Interdisciplinary research, aiming to unite methods from different fields, is vastly used in order to answer biological questions. Although, as it is presented below, both the fields of algorithms and biology can contribute to the elaboration of each other. Hence, the third chapter provides information about algorithms whose design has been inspired by the (collective) behaviour of animals in the nature. Finally, the fourth chapter deviates anew from the central focus of the previous chapters and makes a short introduction in the substantial controversial computational nature of cognition and by extension behaviour. Overall, one can observe that the cooperation of the above mentioned fields is extensive while the accomplished research opens new questions which can be studied only in the light of such collaborations

Computational Approaches to Address the Next-Generation Sequencing Era

In this thesis, I propose new algorithms and models to address biological problems. Computer science in fact plays a key role in proteomics and genetics research due to the advent of big datasets. In the context of protein study, I developed new methods for protein function prediction based on information retrieval principles. By using heterogeneous source of knowledge, like graph search and sequence similarity, I designed a tool called INGA that can be used to annotate entire genomes. It has been benchmarked during the Critical Assessment of Function Annotation challenge, and it proved to be one of the most effective approach for function inference. To better characterize proteins from the structural point of view, I proposed a protein conformers detection strategy based on residue interaction network (RIN) data. RIN graphs were extended to deal with the time-dependent protein coordinate fluctuations, and were generated by clustering algorithms. An implementation called RING MD highlighted effectively the key amino acids known to be functionally relevant in Ubiquitin. These amino acids in fact are very important to explain the protein three-dimensional dynamics. With the same rationale, RIN graphs were used also to predict the impact of mutations within a protein structure. By combining information about a mutant node in the network and its features, an artificial neural network was trained to estimate the free Gibbs energy change of a protein. Extreme changes in the internal energy might lead to the protein unfolding, and possibly to disease. The reduction of a protein flexibility may hamper its function as well. As an example, the extreme fluctuations observed in intrinsically disordered proteins (IDPs) are fundamental for their activities. To better understand IDPs, I contributed in the collection of the largest dataset of disordered regions. In the following analysis, it was shown what are the typical functions of these sequences and the biological processes where they are involved. Due to the importance of their detection, a comprehensive assessment of disorder predictors was performed to show what are the state-of-the-art methods and their limitations. In the context of genetics, I focused on phenotype prediction. During the Critical Assessment of Genome Interpretation (CAGI), I proposed new approaches for the analysis of exome data to prioritize the risk of Crohn's disease and abnormal cholesterol levels. These are often defined as complex disease, since the mechanism behind their insurgence is still unknown. In my study, human samples with an enrichment of mutations in critical genes were predicted to have an high genetic risk. In addition to disease associated genes, protein interaction networks were considered to better account for variants accumulation in biological pathways. Such strategy was shown to be among the best approaches by CAGI organizers. In the simpler case of Mendelian traits, with BOOGIE I designed a method for human blood groups prediction based on exome data. It uses a specialized version of nearest neighbor algorithm in order to match the gene variants in an unannotated exome with the ones available in a reference knowledge base. The most similar hit is used to transfer the blood group. With an accuracy above 90%, BOOGIE is a proof-of-concept that shows the potential applications of genetic prediction, and can be easily extended to any Mendelian trait. To summarize, this thesis is a partial answer to the exponential growth of sequences available that need further experiments. By integrating heterogeneous information and designing new predictive models based on machine learning, I developed novel tools for biological data analysis and classification. All implementations are freely available for the community and might be helpful during future investigations like in drug design and disease studies

A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

Holistic biomimicry: a biologically inspired approach to environmentally benign engineering

Humanity's activities increasingly threaten Earth's richness of life, of which mankind is a part. As part of the response, the environmentally conscious attempt to engineer products, processes and systems that interact harmoniously with the living world. Current environmental design guidance draws upon a wealth of experiences with the products of engineering that damaged humanity's environment. Efforts to create such guidelines inductively attempt to tease right action from examination of past mistakes. Unfortunately, avoidance of past errors cannot guarantee environmentally sustainable designs in the future. One needs to examine and understand an example of an environmentally sustainable, complex, multi-scale system to engineer designs with similar characteristics. This dissertation benchmarks and evaluates the efficacy of guidance from one such environmentally sustainable system resting at humanity's doorstep - the biosphere. Taking a holistic view of biomimicry, emulation of and inspiration by life, this work extracts overarching principles of life from academic life science literature using a sociological technique known as constant comparative method. It translates these principles into bio-inspired sustainable engineering guidelines. During this process, it identifies physically rooted measures and metrics that link guidelines to engineering applications. Qualitative validation for principles and guidelines takes the form of review by biology experts and comparison with existing environmentally benign design and manufacturing guidelines. Three select bio-inspired guidelines at three different organizational scales of engineering interest are quantitatively validated. Physical experiments with self-cleaning surfaces quantify the potential environmental benefits generated by applying the first, sub-product scale guideline. An interpretation of a metabolically rooted guideline applied at the product / organism organizational scale is shown to correlate with existing environmental metrics and predict a sustainability threshold. Finally, design of a carpet recycling network illustrates the quantitative environmental benefits one reaps by applying the third, multi-facility scale bio-inspired sustainability guideline. Taken as a whole, this work contributes (1) a set of biologically inspired sustainability principles for engineering, (2) a translation of these principles into measures applicable to design, (3) examples demonstrating a new, holistic form of biomimicry and (4) a deductive, novel approach to environmentally benign engineering. Life, the collection of processes that tamed and maintained themselves on planet Earth's once hostile surface, long ago confronted and solved the fundamental problems facing all organisms. Through this work, it is hoped that humanity has taken one small step toward self-mastery, thus drawing closer to a solution to the latest problem facing all organisms.Ph.D.Committee Chair: Bert Bras; Committee Member: David Rosen; Committee Member: Dayna Baumeister; Committee Member: Janet Allen; Committee Member: Jeannette Yen; Committee Member: Matthew Realf

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Dynamical Models of biological networks

In der Molekularbiologie sind mathematische Modelle von regulatorischen und metabolischen Netzwerken essentiell, um von einer Betrachtung isolierter Komponenten und Interaktionen zu einer systemischen Betrachtungsweise zu kommen. Genregulatorische Systeme eignen sich besonders gut zur Modellierung, da sie experimentell leicht zugänglich und manipulierbar sind. In dieser Arbeit werden verschiedene genregulatorische Netzwerke unter Zuhilfenahme von mathematischen Modellen analysiert. Weiteres wird ein Modell einer in silico Zelle vorgestellt und diskutiert. Zunächst werden zwei zyklische genregulatorische Netzwerke - der klassische Repressilator und ein Repressilator mit zusätzlicher Autoaktivierung – im Detail mit analytischen Methoden untersucht. Um den Einfluß zufällig schwankender Molekülzahlen auf die Dynamik der beiden Systeme zu untersuchen, werden stochastische Modelle erstellt und die beiden oszillierenden Systeme verglichen. Weiteres werden mögliche Auswirkungen von Genduplikationen auf ein einfaches genregulatorisches Netzwerk untersucht. Dazu wird zunächst ein kleines Netzwerk von GATA Transkriptionsfaktoren, das eine zentrale Rolle in der Regulation des Stickstoffmetabolismus in Hefe spielt, modelliert und das Modell mit experimentellen Daten verglichen, um Parameterregionen einschränken zu können. Außerdem werden potentielle Topologien genregulatorischer Netzwerke von GATA Transkriptionsfaktoren in verwandten Fungi mittels sequenzbasierender Methoden gesucht und verglichen. Im letzten Teil der Arbeit wird MiniCellSim vorgestellt, ein Modell einer selbständigen in silico Zelle. Es erlaubt ein dynamisches System, das eine Protozelle mit einem genregulatorischen Netzwerk, einem einfachen Metabolismus und einer Zellmembran beschreibt, aus einer Sequenz abzuleiten. Nachdem alle Parameter, die zur Berechnung des dynamischen Systems benötigt werden, ohne zusätzliche Eingabe nur aus der Sequenzinformation abgeleitet werden, kann das Modell für Studien zur Evolution von genregulatorischen Netzwerken verwendet werden.In this thesis different types of gene regulatory networks are analysed using mathematical models. Further a computational framework of a novel, self-contained in silico cell model is described and discussed. At first the behaviour of two cyclic gene regulatory systems - the classical repressilator and a repressilator with additional auto-activation - are inspected in detail using analytical bifurcation analysis. To examine the behaviour under random fluctuations, stochastic versions of the systems are created. Using the analytical results sustained oscillations in the stochastic versions are obtained, and the two oscillating systems compared. In the second part of the thesis possible implications of gene duplication on a simple gene regulatory system are inspected. A model of a small network formed by GATA-type transcription factors, central in nitrogen catabolite repression in yeast, is created and validated against experimental data to obtain approximate parameter values. Further, topologies of potential gene regulatory networks and modules consisting of GATA-type transcription factors in other fungi are derived using sequence-based approaches and compared. The last part describes MiniCellSim, a model of a self-contained in silico cell. In this framework a dynamical system describing a protocell with a gene regulatory network, a simple metabolism, and a cell membrane is derived from a string representing a genome. All the relevant parameters required to compute the time evolution of the dynamical system are calculated from within the model, allowing the system to be used in studies of evolution of gene regulatory and metabolic networks