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

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)

Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.T32GM008764 - NIGMS NIH HHS; T32 GM008764 - NIGMS NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHS; DE-SC0012627 - Biological and Environmental Research; RGP0020/2016 - Human Frontier Science Program; NSFOCE-BSF 1635070 - National Science Foundation; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; R37DE016937 - NIDCR NIH HHS; R37 DE016937 - NIDCR NIH HHS; R01GM121950 - NIGMS NIH HHS; R01DE024468 - NIDCR NIH HHS; 1457695 - National Science FoundationPublished versio

PSAMM: A Portable System for the Analysis of Metabolic Models

The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies

The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities

Probabilistic metabolic modeling of microbial communities

Microbial communities (microbiomes) comprise a vast component of life on our planet. They are involved in many fundamental processes, ranging from balancing global biogeochemical cycles to influencing human health. Recently, advances in genome sequencing technologies have allowed us to explore the genetic diversity of microbiomes in high-throughput, cataloging hundreds of thousands of microbial species and millions of genes. As genomic data is accumulating, the challenge remains: to translate genome sequences into functional predictions of relevant phenotypes. A promising approach to address this challenge is the annotation of genomic data to a metabolic network (referred to as genome-scale metabolic model reconstruction), which can then be analyzed to simulate metabolic phenotypes. Although this approach has provided valuable insight into microbial phenotypes, there are many sources of uncertainty in both reconstruction and analysis of genome-scale metabolic networks that currently limit their application. The development of improved reconstruction and analysis methods, and additional sources of data, that further address this uncertainty would facilitate our understanding of microbial community function. The first section of this dissertation is a review that outlines the major uncertainties along a general pipeline for genome-scale metabolic model reconstruction and analysis, and highlights existing approaches for addressing them. An emphasis is placed on probabilistic and ensemble based methods that can be used to formally represent uncertainty and facilitate the crystallization of metabolic network knowledge. The second section of this dissertation introduces a new probabilistic genome-scale metabolic model analysis method, inspired by percolation theory, to quantify the biosynthetic capabilities of microbial organisms in uncertain environments. This method was applied to microbial organisms from the human oral microbiome, providing broad insight into the structure of this microbial community. The third section of this dissertation describes the development of an experimental device to facilitate the collection of data related to metabolic interactions between microbes. The data collected with this device was probabilistically integrated with a mechanistic metabolic model to gain quantitative insight into the syntrophic interaction between an engineered E. coli auxotroph pair. Together, the work described in this dissertation introduces several novel probabilistic methods for metabolic modeling of microbial communities, and sets the stage for future work that can further improve our understanding of these important biological systems.2021-09-28T00:00:00

Reframing gene essentiality in terms of adaptive flexibility

Abstract Background Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality. Results In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed. Conclusions Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events

Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the genus Sodalis

Insect-bacterial symbioses are ubiquitous, but there is still much to uncover about how these relationships establish, persist and evolve. The tsetse endosymbiont Sodalis glossinidius displays intriguing metabolic adaptations to its microenvironment, but the process by which this relationship evolved remains to be elucidated. The recent chance discovery of the free-living species of the genus Sodalis, Sodalis praecaptivus, provides a serendipitous starting point from which to investigate the evolution of this symbiosis. Here, we present a flux balance model for S. praecaptivus and empirically verify its predictions. Metabolic modelling is used in combination with a multi-objective evolutionary algorithm to explore the trajectories that S. glossinidius may have undertaken from this starting point after becoming internalized. The order in which key genes are lost is shown to influence the evolved populations, providing possible targets for future in vitro genetic manipulation. This method provides a detailed perspective on possible evolutionary trajectories for S. glossinidius in this fundamental process of evolutionary and ecological change