Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength

Gene Regulatory Network Analysis and Web-based Application Development

Microarray data is a valuable source for gene regulatory network analysis. Using earthworm microarray data analysis as an example, this dissertation demonstrates that a bioinformatics-guided reverse engineering approach can be applied to analyze time-series data to uncover the underlying molecular mechanism. My network reconstruction results reinforce previous findings that certain neurotransmitter pathways are the target of two chemicals - carbaryl and RDX. This study also concludes that perturbations to these pathways by sublethal concentrations of these two chemicals were temporary, and earthworms were capable of fully recovering. Moreover, differential networks (DNs) analysis indicates that many pathways other than those related to synaptic and neuronal activities were altered during the exposure phase. A novel differential networks (DNs) approach is developed in this dissertation to connect pathway perturbation with toxicity threshold setting from Live Cell Array (LCA) data. Findings from this proof-of-concept study suggest that this DNs approach has a great potential to provide a novel and sensitive tool for threshold setting in chemical risk assessment. In addition, a web-based tool “Web-BLOM” was developed for the reconstruction of gene regulatory networks from time-series gene expression profiles including microarray and LCA data. This tool consists of several modular components: a database, the gene network reconstruction model and a user interface. The Bayesian Learning and Optimization Model (BLOM), originally implemented in MATLAB, was adopted by Web-BLOM to provide an online reconstruction of large-scale gene regulation networks. Compared to other network reconstruction models, BLOM can infer larger networks with compatible accuracy, identify hub genes and is much more computationally efficient

Blueprint: descrição da complexidade da regulação metabólica através da reconstrução de modelos metabólicos e regulatórios integrados

Tese de doutoramento em Biomedical EngineeringUm modelo metabólico consegue prever o fenótipo de um organismo. No entanto, estes modelos podem obter previsões incorretas, pois alguns processos metabólicos são controlados por mecanismos reguladores. Assim, várias metodologias foram desenvolvidas para melhorar os modelos metabólicos através da integração de redes regulatórias. Todavia, a reconstrução de modelos regulatórios e metabólicos à escala genómica para diversos organismos apresenta diversos desafios. Neste trabalho, propõe-se o desenvolvimento de diversas ferramentas para a reconstrução e análise de modelos metabólicos e regulatórios à escala genómica. Em primeiro lugar, descreve-se o Biological networks constraint-based In Silico Optimization (BioISO), uma nova ferramenta para auxiliar a curação manual de modelos metabólicos. O BioISO usa um algoritmo de relação recursiva para orientar as previsões de fenótipo. Assim, esta ferramenta pode reduzir o número de artefatos em modelos metabólicos, diminuindo a possibilidade de obter erros durante a fase de curação. Na segunda parte deste trabalho, desenvolveu-se um repositório de redes regulatórias para procariontes que permite suportar a sua integração em modelos metabólicos. O Prokaryotic Transcriptional Regulatory Network Database (ProTReND) inclui diversas ferramentas para extrair e processar informação regulatória de recursos externos. Esta ferramenta contém um sistema de integração de dados que converte dados dispersos de regulação em redes regulatórias integradas. Além disso, o ProTReND dispõe de uma aplicação que permite o acesso total aos dados regulatórios. Finalmente, desenvolveu-se uma ferramenta computacional no MEWpy para simular e analisar modelos regulatórios e metabólicos. Esta ferramenta permite ler um modelo metabólico e/ou rede regulatória, em diversos formatos. Esta estrutura consegue construir um modelo regulatório e metabólico integrado usando as interações regulatórias e as ligações entre genes e proteínas codificadas no modelo metabólico e na rede regulatória. Além disso, esta estrutura suporta vários métodos de previsão de fenótipo implementados especificamente para a análise de modelos regulatórios-metabólicos.Genome-Scale Metabolic (GEM) models can predict the phenotypic behavior of organisms. However, these models can lead to incorrect predictions, as certain metabolic processes are controlled by regulatory mechanisms. Accordingly, many methodologies have been developed to extend the reconstruction and analysis of GEM models via the integration of Transcriptional Regulatory Network (TRN)s. Nevertheless, the perspective of reconstructing integrated genome-scale regulatory and metabolic models for diverse prokaryotes is still an open challenge. In this work, we propose several tools to assist the reconstruction and analysis of regulatory and metabolic models. We start by describing BioISO, a novel tool to assist the manual curation of GEM models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype predictions. Hence, this tool can reduce the number of artifacts in GEM models, decreasing the burdens of model refinement and curation. A state-of-the-art repository of TRNs for prokaryotes was implemented to support the reconstruction and integration of TRNs into GEM models. The ProTReND repository comprehends several tools to extract and process regulatory information available in several resources. More importantly, this repository contains a data integration system to unify the regulatory data into standardized TRNs at the genome scale. In addition, ProTReND contains a web application with full access to the regulatory data. Finally, we have developed a new modeling framework to define, simulate and analyze GEnome-scale Regulatory and Metabolic (GERM) models in MEWpy. The GERM model framework can read a GEM model, as well as a TRN from different file formats. This framework assembles a GERM model using the regulatory interactions and Genes-Proteins-Reactions (GPR) rules encoded into the GEM model and TRN. In addition, this modeling framework supports several methods of phenotype prediction designed for regulatory-metabolic models.I would like to thank Fundação para a Ciência e Tecnologia for the Ph.D. studentship I was awarded with (SFRH/BD/139198/2018)

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships