Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public

Decoding Complexity in Metabolic Networks using Integrated Mechanistic and Machine Learning Approaches

How can we get living cells to do what we want? What do they actually ‘want’? What ‘rules’ do they observe? How can we better understand and manipulate them? Answers to fundamental research questions like these are critical to overcoming bottlenecks in metabolic engineering and optimizing heterologous pathways for synthetic biology applications. Unfortunately, biological systems are too complex to be completely described by physicochemical modeling alone. In this research, I developed and applied integrated mechanistic and data-driven frameworks to help uncover the mysteries of cellular regulation and control. These tools provide a computational framework for seeking answers to pertinent biological questions. Four major tasks were accomplished. First, I developed innovative tools for key areas in the genome-to-phenome mapping pipeline. An efficient gap filling algorithm (called BoostGAPFILL) that integrates mechanistic and machine learning techniques was developed for the refinement of genome-scale metabolic network reconstructions. Genome-scale metabolic network reconstructions are finding ever increasing applications in metabolic engineering for industrial, medical and environmental purposes. Second, I designed a thermodynamics-based framework (called REMEP) for mutant phenotype prediction (integrating metabolomics, fluxomics and thermodynamics data). These tools will go a long way in improving the fidelity of model predictions of microbial cell factories. Third, I designed a data-driven framework for characterizing and predicting the effectiveness of metabolic engineering strategies. This involved building a knowledgebase of historical microbial cell factory performance from published literature. Advanced machine learning concepts, such as ensemble learning and data augmentation, were employed in combination with standard mechanistic models to develop a predictive platform for important industrial biotechnology metrics such as yield, titer, and productivity. Fourth, my modeling tools and skills have been used for case studies on fungal lipid metabolism analyses, E. coli resource allocation balances, reconstruction of the genome-scale metabolic network for a non-model species, R. opacus, as well as the rapid prediction of bacterial heterotrophic fluxomics. In the long run, this integrated modeling approach will significantly shorten the “design-build-test-learn” cycle of metabolic engineering, as well as provide a platform for biological discovery

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Developing methods for the context-specific reconstruction of metabolic models of cancer cells

Dissertação de mestrado em BioinformáticaThe recent advances in genome sequencing technologies and other high-throughput methodologies allowed the identification and quantification of individual cell components. These efforts led to the development of genome-scale metabolic models (GSMMs), not only for humans but also for several other organisms. These models have been used to predict cellular metabolic phenotypes under a variety of physiological conditions and contexts, proving to be useful in tasks such as drug discovery, biomarker identification and interactions between hosts and pathogens. Therefore, these models provide a useful tool for targeting diseases such as cancer, Alzheimer or tuberculosis. However, the usefulness of GSSMs is highly dependent on their capabilities to predict phenotypes in the array of different cell types that compose the human body, making the development of tissue/context-specific models mandatory. To address this issue, several methods have been proposed to integrate omics data, such as transcriptomics or proteomics, to improve the phenotype prediction abilities of GSSMs. Despite these efforts, these methods still have some limitations. In most cases, their usage is locked behind commercially licensed software platforms, or not available in a user-friendly fashion, thus restricting their use to users with programming or command-line knowledge. In this work, an open-source tool was developed for the reconstruction of tissue/context-specific models based on a generic template GSMM and the integration of omics data. The Tissue-Specific Model Reconstruction (TSM-Rec) tool was developed under the Python programming language and features the FASTCORE algorithm for the reconstruction of tissue/context-specific metabolic models. Its functionalities include the loading of omics data from a variety of omics databases, a set of filtering and transformation methods to adjust the data for integration with a template metabolic model, and finally the reconstruction of tissue/context-specific metabolic models. To evaluate the functionality of the developed tool, a cancer related case-study was carried. Using omics data from 314 glioma patients, the TSM-Rec tool was used to reconstruct metabolic models of different grade gliomas. A total of three models were generated, corresponding to grade II, III and IV gliomas. These models were analysed regarding their differences and similarities in reactions and pathways. This comparison highlighted biological processes common to all glioma grades, and pathways that are more prominent in each glioma model. The results show that the tool developed during this work can be useful for the reconstruction of cancer metabolic models, in a search for insights into cancer metabolism and possible approaches towards drug-target discovery.Os avanços recentes nas tecnologias de sequenciação de genomas e noutras metodologias experimentais de alto rendimento permitiram a identificação e quantificação dos diversos componentes celulares. Estes esforços levaram ao desenvolvimento de Modelos Metabólicos à Escala Genómica (MMEG) não só de humanos, mas também de diversos organismos. Estes modelos têm sido utilizados para a previsão de fenótipos metabólicos sob uma variedade de contextos e condições fisiológicas, mostrando a sua utilidade em áreas como a descoberta de fármacos, a identificação de biomarcadores ou interações entre hóspede e patógeno. Desta forma, estes modelos revelam-se ferramentas úteis para o estudo de doenças como o cancro, Alzheimer ou a tuberculose. Contudo, a utilidade dos MMEG está altamente dependente das suas capacidades de previsão de fenótipos nos diversostipos celulares que compõem o corpo humano, tornando o desenvolvimento de modelos específicos de tecidos uma tarefa obrigatória. Para resolver este problema, vários métodos têm proposto a integração de dados ómicos como os de transcriptómica ou proteómica para melhorar as capacidades preditivas dos MMEG. Apesar disso, estes métodos ainda sofrem de algumas limitações. Na maioria dos casos o seu uso está confinado a plataformas ou softwares com licenças comerciais, ou não está disponível numa ferramenta de fácil uso, limitando a sua utilização a utilizadores com conhecimentos de programação ou de linha de comandos. Neste trabalho, foi desenvolvida uma ferramenta de acesso livre para a reconstrução de modelos metabólicos específicos para tecidos tendo por base um MMEG genérico e a integração de dados ómicos. A ferramenta TSM-Rec (Tissue-Specific Model Reconstruction), foi desenvolvida na linguagem de programação Python e recorre ao algoritmo FASTCORE para efetuar a reconstrução de modelos metabólicos específicos. As suas funcionalidades permitem a leitura de dados ómicos de diversas bases de dados ómicas, a filtragem e transformação dos mesmos para permitir a sua integração com um modelo metabólico genérico e por fim, a reconstrução de modelos metabólicos específicos. De forma a avaliar o funcionamento da ferramenta desenvolvida, esta foi aplicada num caso de estudo de cancro. Recorrendo a dados ómicos de 314 pacientes com glioma, usou-se a ferramenta TSM-Rec para a reconstrução de modelos metabólicos de gliomas de diferentes graus. No total, foram desenvolvidos três modelos correspondentes a gliomas de grau II, grau III e grau IV. Estes modelos foram analisados no sentido de perceber as diferenças e as similaridades entre as reações e as vias metabólicas envolvidas em cada um dos modelos. Esta comparação permitiu isolar processos biológicos comuns a todos os graus de glioma, assim como vias metabólicas que se destacam em cada um dos graus. Os resultados obtidos demonstram que a ferramenta desenvolvida pode ser útil para a reconstrução de modelos metabólicos de cancro, na procura de um melhor conhecimento do metabolismo do cancro e possíveis abordagens para a descoberta de fármacos