Rewriting yeast central carbon metabolism for industrial isoprenoid production.

A bio-based economy has the potential to provide sustainable substitutes for petroleum-based products and new chemical building blocks for advanced materials. We previously engineered Saccharomyces cerevisiae for industrial production of the isoprenoid artemisinic acid for use in antimalarial treatments. Adapting these strains for biosynthesis of other isoprenoids such as ß-farnesene (C15H24), a plant sesquiterpene with versatile industrial applications, is straightforward. However, S. cerevisiae uses a chemically inefficient pathway for isoprenoid biosynthesis, resulting in yield and productivity limitations incompatible with commodity-scale production. Here we use four non-native metabolic reactions to rewire central carbon metabolism in S. cerevisiae, enabling biosynthesis of cytosolic acetyl coenzyme A (acetyl-CoA, the two-carbon isoprenoid precursor) with a reduced ATP requirement, reduced loss of carbon to CO2-emitting reactions, and improved pathway redox balance. We show that strains with rewired central metabolism can devote an identical quantity of sugar to farnesene production as control strains, yet produce 25% more farnesene with that sugar while requiring 75% less oxygen. These changes lower feedstock costs and dramatically increase productivity in industrial fermentations which are by necessity oxygen-constrained. Despite altering key regulatory nodes, engineered strains grow robustly under taxing industrial conditions, maintaining stable yield for two weeks in broth that reaches >15% farnesene by volume. This illustrates that rewiring yeast central metabolism is a viable strategy for cost-effective, large-scale production of acetyl-CoA-derived molecules.

(1)H nuclear magnetic resonance metabolomics analysis identifies novel urinary biomarkers for lung function.

Chronic obstructive pulmonary disease (COPD), characterized by chronic airflow limitation, is a serious public health concern. In this study, we used proton nuclear magnetic resonance ((1)H NMR) spectroscopy to identify and quantify metabolites associated with lung function in COPD. Plasma and urine were collected from 197 adults with COPD and from 195 without COPD. Samples were assayed using a 600 MHz NMR spectrometer, and the resulting spectra were analyzed against quantitative spirometric measures of lung function. After correcting for false discoveries and adjusting for covariates (sex, age, smoking) several spectral regions in urine were found to be significantly associated with baseline lung function. These regions correspond to the metabolites trigonelline, hippurate and formate. Concentrations of each metabolite, standardized to urinary creatinine, were associated with baseline lung function (minimum p-value = 0.0002 for trigonelline). No significant associations were found with plasma metabolites. Urinary hippurate and formate are often related to gut microflora. This could suggest that the microbiome varies between individuals with different lung function. Alternatively, the associated metabolites may reflect lifestyle differences affecting overall health. Our results will require replication and validation, but demonstrate the utility of NMR metabolomics as a screening tool for identifying novel biomarkers of pulmonary outcomes.

A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189.

With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

OptCircuit: an optimization based method for computational design of genetic circuits.

BACKGROUND: Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. It has been widely acknowledged that that task of building circuits to meet multiple inducer-specific requirements is a challenging one. This is because of the incomplete description of component interactions compounded by the fact that the number of ways in which one can chose and interconnect components, increases exponentially with the number of components. RESULTS: In this paper we introduce OptCircuit, an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. Multiple literature sources are used to compile a comprehensive compilation of kinetic descriptions of promoter-protein pairs. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ordinary differential equations but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. Computational results for a toggle switch example demonstrate the ability of the framework to generate the complete list of circuit designs of varying complexity that exhibit the desired response. Designs identified for a genetic decoder highlight the ability of OptCircuit to suggest circuit configurations that go beyond the ones compatible with digital logic-based design principles. Finally, the results obtained from the concentration band detector example demonstrate the ability of OptCircuit to design circuits whose responses are contingent on the level of external inducer as well as pinpoint parameters for modification to rectify an existing (non-functional) biological circuit and restore functionality. CONCLUSION: Our results demonstrate that OptCircuit framework can serve as a design platform to aid in the construction and finetuning of integrated biological circuits.

Metabolic flux elucidation for large-scale models using 13C labeled isotopes.

A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraint-based modeling framework with isotopic label tracing on a large scale. When cells are fed a growth substrate with certain carbon positions labeled with (13)C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the antimalarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-(13)C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.

Optimization based automated curation of metabolic reconstructions.

BACKGROUND: Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis) with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them. RESULTS: In this work, an optimization based procedure is proposed to identify and eliminate network gaps in these reconstructions. First we identify the metabolites in the metabolic network reconstruction which cannot be produced under any uptake conditions and subsequently we identify the reactions from a customized multi-organism database that restores the connectivity of these metabolites to the parent network using four mechanisms. This connectivity restoration is hypothesized to take place through four mechanisms: a) reversing the directionality of one or more reactions in the existing model, b) adding reaction from another organism to provide functionality absent in the existing model, c) adding external transport mechanisms to allow for importation of metabolites in the existing model and d) restore flow by adding intracellular transport reactions in multi-compartment models. We demonstrate this procedure for the genome- scale reconstruction of Escherichia coli and also Saccharomyces cerevisiae wherein compartmentalization of intra-cellular reactions results in a more complex topology of the metabolic network. We determine that about 10% of metabolites in E. coli and 30% of metabolites in S. cerevisiae cannot carry any flux. Interestingly, the dominant flow restoration mechanism is directionality reversals of existing reactions in the respective models. CONCLUSION: We have proposed systematic methods to identify and fill gaps in genome-scale metabolic reconstructions. The identified gaps can be filled both by making modifications in the existing model and by adding missing reactions by reconciling multi-organism databases of reactions with existing genome-scale models. Computational results provide a list of hypotheses to be queried further and tested experimentally.

A computational framework for the topological analysis and targeted disruption of signal transduction networks.

In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-scale reconstruction of a signaling network composed of nine signaling pathways implicated in prostate cancer. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exist two distinct types of outputs in the signaling network that either can be elicited by many different input combinations or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades. By comparing the obtained disruption targets with lists of drug molecules we find that many of these targets can be acted upon by existing drug compounds, whereas the remaining ones point at so-far unexplored targets. Overall the proposed computational frameworks can help elucidate input/output relationships of signaling networks and help to guide the systematic design of interference strategies.

DEMSIM: a discrete event based mechanistic simulation platform for gene expression and regulation dynamics.

In this paper, a discrete event based mechanistic simulation platform DEMSIM is developed for testing and validating putative regulatory interactions. The proposed framework models the main processes in gene expression, which are transcription, translation and decay processes, as stand-alone modules while superimposing the regulatory circuitry to obtain an accurate time evolution of the system. The stochasticity inherent to gene expression and regulation processes is captured using Monte Carlo based sampling. The proposed framework is applied to the extensively studied lac operon system, the SOS response system and the araBAD operon system of Escherichia coli. The results for the lac gene system demonstrate the simulation framework's ability to capture the dynamics of gene regulation, whereas the results for the SOS response system indicate that the framework is able to make accurate predictions about system behavior in response to perturbations. Finally, simulation studies for the araBAD system suggest that the developed framework is able to distinguish between different plausible regulatory mechanisms postulated to explain observed gene expression profiles. Overall, the obtained results highlight the effectiveness of DEMSIM at describing the underlying biological processes involved in gene regulation for querying alternative regulatory hypotheses.