Computational approaches to complex biological networks

The need of understanding and modeling the biological networks is one of the raisons d'\ueatre and of the driving forces behind the emergence of Systems Biology. Because of its holistic approach and because of the widely different level of complexity of the networks, different mathematical methods have been developed during the years. Some of these computational methods are used in this thesis in order to investigate various properties of different biological systems. The first part deals with the prediction of the perturbation of cellular metabolism induced by drugs. Using Flux Balance Analysis to describe the reconstructed genome-wide metabolic networks, we consider the problem of identifying the most selective drug synergisms for given therapeutic targets. The second part of this thesis considers gene regulatory and large social networks as signed graphs (activation/deactivation or friendship/hostility are rephrased as positive/negative coupling between spins). Using the analogy with an Ising spin glass an analysis of the energy landscape and of the content of \u201cdisorder\u201d 'is carried out. Finally, the last part concerns the study of the spatial heterogeneity of the signaling pathway of rod photoreceptors. The electrophysiological data produced by our collaborators in the Neurobiology laboratory have been analyzed with various dynamical systems giving an insight into the process of ageing of photoreceptors and into the role diffusion in the pathway

Applying Systems Pharmacology to the Treatment of Chronic Illness Using Novel Scoring and Translational Methods

Chronic illnesses are poorly understood diseases that are often highly resistant to treatment. The prevalence and severity of these illnesses necessitates new methods for treatment development that diverge from the paradigm of one drug, one illness. Instead, multidrug interventions that utilize repurposable, previously approved drugs could be far more advantageous. In order to support this, a novel scoring framework and accompanying set of tools, collectively termed DrugAble, have been developed. DrugAble scores proposed, model-based treatment target solutions by analyzing drug-target interaction data and addressing the network complexity of these solutions. Actionability scores that summarize the likelihood of a proposed target set constituting a pharmacologically accessible path to remission are generated. Additionally, DrugAble proposes combinations of repurposable drugs that can potentially be used in tandem to achieve remission. Here, DrugAble is demonstrated on molecular target solutions supporting an escape from Myalgic Encephalomyelitis / Chronic Fatigue Syndrome, a debilitating illness that affects up to 2.5 million Americans alone. DrugAble effectively discriminates between theoretical target sets and those that are clinically actionable using available drugs while simultaneously accounting for drug-target interactions and off-target effects of these drugs. This framework constitutes the necessary first steps to designing more effective treatments for chronic illnesses, with the ultimate goal of reducing the failure rate of clinical trials and the financial burden on both drug developers and patients. Most importantly, it opens new and more immediately accessible paths toward achieving remission and full recovery for those suffering from chronic illnesse

Probabilistic Argumentation for Patient Decision Making

Medical drug reviews are increasingly commonplace on the web and have become an important source of information for patients undergoing medical treatment. Patients will look to these reviews in order to understand the impact the drugs have had on others who have experienced them. In short these reviews can be interpreted as a body of arguments and counterarguments for/against the drug being reviewed. One of the challenges of reading these reviews is drawing out the arguments easily and forming a final opinion; this is due to the number of reviews and the variety of arguments presented. This thesis explores the use of computational models of argumentation in order to extract structured argumentation data from the reviews and present them to the user. In particular I propose a pipeline that performs argument extraction, argument graph extraction and visualisation

Robustness Evaluation for Phylogenetic Reconstruction Methods and Evolutionary Models Reconstruction of Tumor Progression

During evolutionary history, genomes evolve by DNA mutation, genome rearrangement, duplication and gene loss events. There has been endless effort to the phylogenetic and ancestral genome inference study. Due to the great development of various technology, the information about genomes is exponentially increasing, which make it possible figure the problem out. The problem has been shown so interesting that a great number of algorithms have been developed rigorously over the past decades in attempts to tackle these problems following different kind of principles. However, difficulties and limits in performance and capacity, and also low consistency largely prevent us from confidently statement that the problem is solved. To know the detailed evolutionary history, we need to infer the phylogeny of the evolutionary history (Big Phylogeny Problem) and also infer the internal nodes information (Small Phylogeny Problem). The work presented in this thesis focuses on assessing methods designed for attacking Small Phylogeny Problem and algorithms and models design for genome evolution history inference from FISH data for cancer data. During the recent decades, a number of evolutionary models and related algorithms have been designed to infer ancestral genome sequences or gene orders. Due to the difficulty of knowing the true scenario of the ancestral genomes, there must be some tools used to test the robustness of the adjacencies found by various methods. When it comes to methods for Big Phylogeny Problem, to test the confidence rate of the inferred branches, previous work has tested bootstrapping, jackknifing, and isolating and found them good resampling tools to corresponding phylogenetic inference methods. However, till now there is still no system work done to try and tackle this problem for small phylogeny. We tested the earlier resampling schemes and a new method inversion on different ancestral genome reconstruction methods and showed different resampling methods are appropriate for their corresponding methods. Cancer is famous for its heterogeneity, which is developed by an evolutionary process driven by mutations in tumor cells. Rapid, simultaneous linear and branching evolution has been observed and analyzed by earlier research. Such process can be modeled by a phylogenetic tree using different methods. Previous phylogenetic research used various kinds of dataset, such as FISH data, genome sequence, and gene order. FISH data is quite clean for the reason that it comes form single cells and shown to be enough to infer evolutionary process for cancer development. RSMT was shown to be a good model for phylogenetic analysis by using FISH cell count pattern data, but it need efficient heuristics because it is a NP-hard problem. To attack this problem, we proposed an iterative approach to approximate solutions to the steiner tree in the small phylogeny tree. It is shown to give better results comparing to earlier method on both real and simulation data. In this thesis, we continued the investigation on designing new method to better approximate evolutionary process of tumor and applying our method to other kinds of data such as information using high-throughput technology. Our thesis work can be divided into two parts. First, we designed new algorithms which can give the same parsimony tree as exact method in most situation and modified it to be a general phylogeny building tool. Second, we applied our methods to different kinds data such as copy number variation information inferred form next generation sequencing technology and predict key changes during evolution

Computational Toxinology

Venoms are complex mixtures of biological macromolecules and other compounds that are used for predatory and defensive purposes by hundreds of thousands of known species worldwide. Throughout human history, venoms and venom components have been used to treat a vast array of illnesses, causing them to be of great clinical, economic, and academic interest to the drug discovery and toxinology communities. In spite of major computational advances that facilitate data-driven drug discovery, most therapeutic venom effects are still discovered via tedious trial-and-error, or simply by accident. In this dissertation, I describe a body of work that aims to establish a new subdiscipline of translational bioinformatics, which I name “computational toxinology”. To accomplish this goal, I present three integrated components that span a wide range of informatics techniques: (1) VenomKB, (2) VenomSeq, and (3) VenomKB’s Semantic API. To provide a platform for structuring, representing, retrieving, and integrating venom data relevant to drug discovery, VenomKB provides a database-backed web application and knowledge base for computational toxinology. VenomKB is structured according to a fully-featured ontology of venoms, and provides data aggregated from many popular web re- sources. VenomSeq is a biotechnology workflow that is designed to generate new high-throughput sequencing data for incorporation into VenomKB. Specifically, we expose human cells to controlled doses of crude venoms, conduct RNA-Sequencing, and build profiles of differential gene expression, which we then compare to publicly-available differential expression data for known dis- eases and drugs with known effects, and use those comparisons to hypothesize ways that the venoms could act in a therapeutic manner, as well. These data are then integrated into VenomKB, where they can be effectively retrieved and evaluated using existing data and known therapeutic associations. VenomKB’s Semantic API further develops this functionality by providing an intelligent, powerful, and user-friendly interface for querying the complex underlying data in VenomKB in a way that reflects the intuitive, human-understandable mean- ing of those data. The Semantic API is designed to cater to the needs of advanced users as well as laypersons and bench scientists without previous expertise in computational biology and semantic data analysis. In each chapter of the dissertation, I describe how we evaluated these 3 components through various approaches. We demonstrate the utility of VenomKB and the Semantic API by testing a number of practical use-cases for each, designed to highlight their ability to rediscover existing knowledge as well as suggesting potential areas for future exploration. We use statistics and data science techniques to evaluate VenomSeq on 25 diverse species of venomous animals, and propose biologically feasible explanations for significant findings. In evaluating the Semantic API, I show how observations on VenomSeq data can be interpreted and placed into the context of past research by members of the larger toxinology community. Computational toxinology is a toolbox designed to be used by multiple stakeholders (toxinologists, computational biologists, and systems pharmacologists, among others) to improve the return rate of clinically-significant findings from manual experimentation. It aims to achieve this goal by enabling access to data, providing means for easy validation of results, and suggesting specific hypotheses that are preliminarily supported by rigorous inferential statistics. All components of the research I describe are open-access and publicly available, to improve reproducibility and encourage widespread adoptio

V Jornadas de Investigación de la Facultad de Ciencia y Tecnología. 2016

171 p.I. Abstracts. Ahozko komunikazioak / Comunicaciones orales: 1. Biozientziak: Alderdi Molekularrak / Biociencias: Aspectos moleculares. 2. Biozientziak: Ingurune Alderdiak / Biociencias: Aspectos Ambientales. 3. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 4. Geología / Geología. 5. Matematika / Matemáticas. 6. Kimika / Química. 7. Ingenieritza Kimikoa eta Kimika / Ingeniería Química y Química. II. Abstracts. Idatzizko Komunikazioak (Posterrak) / Comunicaciones escritas (Pósters): 1. Biozientziak / Biociencias. 2. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 3. Geologia / Geologia. 4. Matematika / Matemáticas. 5. Kimika / Química. 6. Ingenieritza Kimikoa / Ingeniería Química

Integrated information theory in complex neural systems

This thesis concerns Integrated Information Theory (IIT), a branch of information theory aimed at providing a fundamental theory of consciousness. At its core, lie two powerful intuitions: • That a system that is somehow more than the sum of its parts has non-zero integrated information, Φ; and • That a system with non-zero integrated information is conscious. The audacity of IIT’s claims about consciousness has (understandably) sparked vigorous criticism, and experimental evidence for IIT as a theory of consciousness remains scarce and indirect. Nevertheless, I argue that IIT still has merits as a theory of informational complexity within complexity science, leaving aside all claims about consciousness. In my work I follow this broad line of reasoning: showcasing applications where IIT yields rich analyses of complex systems, while critically examining its merits and limitations as a theory of consciousness. This thesis is divided in three parts. First, I describe three example applications of IIT to complex systems from the computational neuroscience literature (coupled oscillators, spiking neurons, and cellular automata), and develop novel Φ estimators to extend IIT’s range of applicability. Second, I show two important limitations of current IIT: that its axiomatic foundation is not specific enough to determine a unique measure of integrated information; and that available measures do not behave as predicted by the theory when applied to neurophysiological data. Finally, I present new theoretical developments aimed at alleviating some of IIT’s flaws. These are based on the concepts of partial information decomposition and lead to a unification of both theories, Integrated Information Decomposition, or ΦID. The thesis concludes with two experimental studies on M/EEG data, showing that a much simpler informational theory of consciousness – the entropic brain hypothesis – can yield valuable insight without the mathematical challenges brought by IIT.Open Acces

Quantitative and evolutionary global analysis of enzyme reaction mechanisms

The most widely used classification system describing enzyme-catalysed reactions is the Enzyme Commission (EC) number. Understanding enzyme function is important for both fundamental scientific and pharmaceutical reasons. The EC classification is essentially unrelated to the reaction mechanism. In this work we address two important questions related to enzyme function diversity. First, to investigate the relationship between the reaction mechanisms as described in the MACiE (Mechanism, Annotation, and Classification in Enzymes) database and the main top-level class of the EC classification. Second, how well these enzymes biocatalysis are adapted in nature. In this thesis, we have retrieved 335 enzyme reactions from the MACiE database. We consider two ways of encoding the reaction mechanism in descriptors, and three approaches that encode only the overall chemical reaction. To proceed through my work, we first develop a basic model to cluster the enzymatic reactions. Global study of enzyme reaction mechanism may provide important insights for better understanding of the diversity of chemical reactions of enzymes. Clustering analysis in such research is very common practice. Clustering algorithms suffer from various issues, such as requiring determination of the input parameters and stopping criteria, and very often a need to specify the number of clusters in advance. Using several well known metrics, we tried to optimize the clustering outputs for each of the algorithms, with equivocal results that suggested the existence of between two and over a hundred clusters. This motivated us to design and implement our algorithm, PFClust (Parameter-Free Clustering), where no prior information is required to determine the number of cluster. The analysis highlights the structure of the enzyme overall and mechanistic reaction. This suggests that mechanistic similarity can influence approaches for function prediction and automatic annotation of newly discovered protein and gene sequences. We then develop and evaluate the method for enzyme function prediction using machine learning methods. Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. The machine learning method needs only chemoinformatics descriptors as an input and is applicable for regression analysis. The last phase of this work is to test the evolution of chemical mechanisms mapped onto ancestral enzymes. This domain occurrence and abundance in modern proteins has showed that the / architecture is probably the oldest fold design. These observations have important implications for the origins of biochemistry and for exploring structure-function relationships. Over half of the known mechanisms are introduced before architectural diversification over the evolutionary time. The other halves of the mechanisms are invented gradually over the evolutionary timeline just after organismal diversification. Moreover, many common mechanisms includes fundamental building blocks of enzyme chemistry were found to be associated with the ancestral fold