Metabolic modeling to understand and redesign microbial systems

The goals of this thesis are to increase the understanding of microbial metabolism and to functionally (re-)design microbial systems using Genome- Scale Metabolic models (GSMs). GSMs are species-specific knowledge repositories that can be used to predict metabolic activities for wildtype and genetically modified organisms. <strong>Chapter 1</strong> describes the assumptions associated with GSMs, the GSM generation process, common GSM analysis methods, and GSM-driven strain design methods. Thereby, <strong>chapter 1</strong> provides a background for all other chapters. In this work, there is a focus on the metabolically versatile bacterium <em>Pseudomonas putida</em> (<strong>chapters 2,3,4,5,6</strong>), but also other model microbes and biotechnologically or societally relevant microbes are considered (<strong>chapters 3,4,6,7,8</strong>). GSMs are reflections of the genome annotation of the corresponding organism. For <em>P. putida</em>, the genome annotation that GSMs have been built on is more than ten years old. In <strong>chapter 2</strong>, this genome annotation was updated both on a structural and functional level using state-of-the-art annotation tools. A crucial part of the functional annotation relied on the most comprehensive <em>P. putida </em>GSM to date. This GSM was used to identify knowledge gaps in <em>P. putida </em>metabolism by determining the inconsistencies between its growth predictions and experimental measurements. Inconsistencies were found for 120 compounds that could be degraded by <em>P. putida</em> in vitro but not in silico. These compounds formed the basis for a targeted manual annotation process. Ultimately, suitable degradation pathways were identified for 86/120 as part of the functional reannotation of the <em>P. putida</em> genome. For <em>P. putida</em> there are 3 independently generated GSMs, which is not uncommon for model organisms. These GSMs differ in generation procedure and represent different and complementary subsets of the knowledge on the metabolism of the organism. However, the differing generation procedures also makes it extremely cumbersome to compare their contents, let alone to combine them into a single consensus GSM. <strong>Chapter 3 </strong>addresses this issue through the introduction of a computational tool for COnsensus Metabolic Model GENeration (COMMGEN). COMMGEN automatically identifies inconsistencies between independently generated GSMs and semi-automatically resolves them. Thereby, it greatly facilitates a detailed comparison of independently generated GSMs as well as the construction of consensus GSMs that more comprehensively describe the knowledge on the modeled organism. GSMs can predict whether or not the corresponding organism and derived mutants can grow in a large variety of different growth conditions. In comparison, experimental data is extremely limited. For example, BIOLOG data describes growth phenotypes for one strain in a few hundred different media, and genome-wide gene essentially data is typically limited to a single growth medium. In <strong>chapter 4</strong> GSMs of multiple <em>Pseudomonas</em> species were used to predict growth phenotypes for all possible single-gene-deletion mutants in all possible minimal growth media to determine conditionally and unconditionally essential genes. This simulated data was integrated with genomic data on 432 sequenced Pseudomonas species, which revealed a clear link between the essentiality of a gene function and the persistence of the gene within the <em>Pseudomonas</em> genus. <strong>Chapters 5 </strong>and <strong>6</strong> describe the use of GSMs to (re-)design microbial systems. <em>P. putida</em> is, despite its acknowledged versatile metabolism, an obligate aerobe. As the oxygen-requirement limits the potential applications of <em>P. putida</em>, there have been several experimental attempts to enable it to grow anaerobically, which have so far not succeeded. <strong>Chapter 5</strong> describes an in silico effort to determine why <em>P. putida</em> cannot grow anaerobically using a combination of GSM analyses and comparative genomics. These analyses resulted in a shortlist of several essential and oxygen-dependent processes in <em>P. putida</em>. The identification of these processes has enabled the design of <em>P. putida </em>strains that can grow anaerobically based on the current understanding of P. putida metabolism as represented in GSMs. Efficient microbial CO2 fixation is a requirement for the biobased community, but the natural CO2 fixation pathways are rather inefficient, while the synthetic CO2 fixation pathways have been designed without considering the metabolic context of a target organism. <strong>Chapter 6</strong> introduces a computational tool, CO2FIX, that designs species-specific CO2 fixation pathways based on GSMs and biochemical reaction databases. The designed pathways are evaluated for their ATP efficiency, thermodynamic feasibility, and kinetic rates. CO2FIX is applied to eight different organisms, which has led to the identification of both species-specific and general CO2 fixation pathways that have promising features while requiring surprisingly few non-native reactions. Three of these pathways are described in detail. In all previous chapters GSMs of relatively well-understood microbes have been used to gain further insight into their metabolism and to functionally (re-)design them. For complex microbial systems, such as algae (<strong>chapter 7</strong>) and gut microbial communities (<strong>chapter 8</strong>), GSMs are similarly useful, but substantially more difficult to create and analyze. Algae are widely considered as potential centerpieces of a biobased economy. <strong>Chapter 7</strong> reviews the current challenges in algal genome annotation, modeling and synthetic biology. The gut microbiota is an incredibly complex microbial system that is crucial to our well-being. <strong>Chapter 8</strong> reviews the ongoing developments in the modeling of both single gut microbes and gut microbial communities, and discusses how these developments will enable the move from studying correlation to causation, and ultimately the rational steering of gut microbial activity. <strong>Chapter 9</strong> discusses how the previous chapters contribute to the research goals of this thesis. In addition, it provides an extensive discussion on current GSM practices, the issues associated therewith, and how these issues can be tackled. In particular, the discussion focuses on issues related to: (i) The inability to distinguish between biological difference and GSM generation artifacts when using multiple GSMs, (ii) The lack of continuous GSM updates, (iii) The mismatch between what GSM predictions and experimental data represent, (iv) The need for standardization in GSM evaluation, and (v) The lack of experimental validation of GSM-driven strain design for metabolic engineering.