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Engineering microbes to synthesize molecules of societal value has historically been a time consuming and artisanal process, with the synthesis of each new non-native molecule typically warranting its own separate publication. Because most microbial strain engineering efforts leverage a finite number of common metabolic engineering design tactics, we reasoned that automating these design steps would help create a pipeline that can quickly, cheaply, and reliably generate so-called microbial factories. In this work we describe the design and implementation of a computational system, an Automated Scientist we call Lila, which handles all metabolic engineering design and optimization through the design-build-test-learn (DBTL) paradigm. Lila generates metabolic routes, identifies relevant genetic elements for perturbation, and specifies the design and re-design of microbial strains in a matter of seconds to minutes. Strains specified by Lila are then built and subsequently phenotyped as part of a largely automated in-house pipeline. Humans remain in-the-loop to curate choices made by the system, helping for example to refine the metabolic model or suggest custom protein modifications. Lila attempted to build strains that could produce 454 biochemically diverse molecules with precursors located broadly throughout the metabolism of two microbial hosts,Saccharomyces cerevisiaeandEscherichia coli. Notably, we observed the highest published titers for the molecule naringenin, the metabolic precursor to flavonoids. In total we created hundreds of thousands of microbial strains capable of overproducing 242 molecules, of which 180 are not native toS. cerevisiaeorE. coli.

Genome-scale models (GEMs) of metabolism were constructed for 55 fully sequenced Escherichia coli and Shigella strains. The GEMs enable a systems approach to characterizing the pan and core metabolic capabilities of the E. coli species. The majority of pan metabolic content was found to consist of alternate catabolic pathways for unique nutrient sources. The GEMs were then used to systematically analyze growth capabilities in more than 650 different growth-supporting environments. The results show that unique strain-specific metabolic capabilities correspond to pathotypes and environmental niches. Twelve of the GEMs were used to predict growth on six differentiating nutrients, and the predictions were found to agree with 80% of experimental outcomes. Additionally, GEMs were used to predict strain-specific auxotrophies. Twelve of the strains modeled were predicted to be auxotrophic for vitamins niacin (vitamin B3), thiamin (vitamin B1), or folate (vitamin B9). Six of the strains modeled have lost biosynthetic pathways for essential amino acids methionine, tryptophan, or leucine. Genome-scale analysis of multiple strains of a species can thus be used to define the metabolic essence of a microbial species and delineate growth differences that shed light on the adaptation process to a particular microenvironment.

The in silico reconstruction of metabolic networks has become an effective and useful systems biology approach to predict and explain many different cellular phenotypes. When simulation outputs do not match experimental data, the source of the inconsistency can often be traced to incomplete biological information that is consequently not captured in the model. To address this problem, general approaches continue to be needed that can suggest experimentally testable hypotheses to reconcile inconsistencies between simulation and experimental data. Here, we present such an approach that focuses specifically on correcting cases in which experimental data show a particular gene to be essential but model simulations do not. We use metabolic models to predict efficient compensatory pathways, after which cloning and overexpression of these pathways are performed to investigate whether they restore growth and to help determine why these compensatory pathways are not active in mutant cells. We demonstrate this technique for a ppc knockout of Salmonella enterica serovar Typhimurium; the inability of cells to route flux through the glyoxylate shunt when ppc is removed was correctly identified by our approach as the cause of the discrepancy. These results demonstrate the feasibility of our approach to drive biological discovery while simultaneously refining metabolic network reconstructions.

There has been growing interest in disrupting bacterial virulence mechanisms as a form of infectious disease control through the use of ‘anti-infective' drugs. Pseudomonas aeruginosa is an opportunistic pathogen noted for its intrinsic antibiotic resistance that causes serious infections requiring new therapeutic options. In this study, an analysis of the P. aeruginosa PAO1 deduced proteome was performed to identify pathogen-associated proteins. A computational screening approach was then used to discover drug repurposing opportunities, i.e. identifying approved drugs that bind and potentially disrupt the pathogen-associated protein targets. The selective oestrogen receptor modulator raloxifene, a drug currently used in the prevention of osteoporosis and/or invasive breast cancer in post-menopausal women, was predicted from this screen to bind P. aeruginosa PhzB2. PhzB2 is involved in production of the blue pigment pyocyanin produced via the phenazine biosynthesis pathway. Pyocyanin is toxic to eukaryotic cells and has been shown to play a role in infection in a mouse model, making it an attractive target for anti-infective drug discovery. Raloxifene was found to strongly attenuate P. aeruginosa virulence in a Caenorhabditis elegans model of infection. Treatment of P. aeruginosa wild-type strains PAO1 and PA14 with raloxifene resulted in a dose-dependent reduction in pyocyanin production in vitro; pyocyanin production and virulence were also reduced for a phzB2 insertion mutant. These results suggest that raloxifene may be suitable for further development as a therapeutic for P. aeruginosa infection and that such already approved drugs may be computationally screened and potentially repurposed as novel anti-infective/anti-virulence agents.

Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r2 (p, Author Summary All heterotrophic organisms must balance the deployment of consumed carbon compounds between growth and the generation of energy. These two competing objectives have been shown, both computationally and experimentally, to exist as the principal dimensions of the function of metabolic networks. Each of these dimensions can also be thought of as the familiar metabolic functions of catabolism, anabolism, and generation of energy. Here we detail how two global transcription factors (TFs), ArcA and Fnr of Escherichia coli that sense redox ratios, act on a genome-wide basis to coordinately regulate these global metabolic functions through transcriptional control of enzyme and transporter levels in changing environments. A model results from the study that shows how global transcription factors regulate global dimensions of metabolism and form a regulatory hierarchy that reflects the structural hierarchy of the metabolic network.

Good Enough Can Be Good Enough To begin to understand why some enzymes are promiscuous and have many substrates, whereas others are highly specific, and why some have high activity, whereas others appear not to be optimized, Nam et al. (p. 1101 ) analyzed metabolic networks in bacteria. Specialist enzymes are essential for life, catalyze a high flux of enzymatic activity, and are more highly regulated. However, not all enzymes appear to be on a track of gradual improvement of specificity and efficiency. Generalist enzymes seem to well serve their own purposes, and their optimization may not justify the evolutionary cost.

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

The genotype-phenotype relationship is fundamental to biology. Finding general, underlying rules that govern the complex relationship between gene expression and cell growth, however, has proven a challenge. On page 1099 of this issue, Scott et al. ( 1 ) offer empirical “growth laws” that correlate the growth rates of bacteria with how they allocate resources to protein synthesis and metabolic functions.