A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with highthroughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability resulting in improved understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes, which is an important limitation in many modeling applications. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present a new algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation logic, the ability to handle very large enzyme complex rules that may incorporate multiple isoforms, and depending on the model constraints, either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available, and binaries are provided for Linux x86-64 systems. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.Comment: 30 pages, 12 figures, 4 table

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

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

Multi-Target Analysis and Design of Mitochondrial Metabolism.

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.PL has received funding from (FP7-Health-F5-2012) under grant agreement no. 305280 (MIMOmics). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLoS via http://dx.doi.org/10.1371/journal.pone.013382

How evolutionary objectives and the intracellular environment shape metabolic fluxes

Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. In the first chapter of this dissertation, we introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories. For over a hundred years, enzymes, or the proteins that catalyze metabolic reactions, have been characterized in vitro, even though the aqueous solution of a test tube little resembles the crowded intracellular milieu. Since few metabolites show unique fluorescent signatures, metabolism is all but invisible, greatly complicating efforts to describe fluxes in vivo. In the second chapter of this dissertation, we introduce a new technique called EIFFL (Estimation of Intracellular Flux through Fluorescence Loss) for visualizing the flux through a reaction inside single E. coli cells, using a substrate that undergoes an enzyme-catalyzed loss of fluorescence. EIFFL would not only further our quantitative understanding of metabolism, but enable us to promptly detect enzymes that confer clinically meaningful states, such as antibiotic resistance. We present a particular instance of EIFFL that couples nfsA, the major nitroreductase of E. coli responsible for its antibiotic sensitivity to nitrofurantoin, to 2-NBDG, a glucose derivative that loses fluorescence upon being reduced by nfsA with NADPH. We correlate the flux through the reaction with the concentration of a fluorescently tagged nfsA and measure the “flux noise” across a population of E. coli cells. Given that nfsA abolishes 2-NBDG fluorescence by the same molecular mechanism that it activates nitrofurantoin, EIFFL could serve as a means to rapidly infer the antibiotic resistance of single pathogenic E. coli cells directly from clinical samples.2020-02-20T00:00:00

Understanding global resource allocation in fission yeast through data analysis and coarse-grained mathematical modelling

Unicellular organisms can grow in a large variety of environments. Even in those supporting robust growth, cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate and the cell size. I have approached this topic from two angles, namely a comprehensive analysis of a gene expression data set and the construction of coarse-grained resource allocation models (C-GRAMs). First, I studied a combined data set of protein and transcript abundances during growth of the fission yeast Schizosaccharomyces pombe on various abundant nitrogen sources. Approximately half of gene expression was significantly correlated with the growth rate, and this came alongside wide-spread nutrient-specific expression. Genes positively correlated with the growth rate participated in protein production, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, the expression of metabolic enzymes was mainly condition specific. Second, C-GRAMs are simple models of single cells, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models can then be easily explored. A minimal C-GRAM with nitrogen and carbon pathways converging on biomass production described the effects of the uptake of sugars, ammonium, and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximised the growth rate. Prompted by new observations that the relation between cell volume and the growth rate was identical for both carbon and nitrogen perturbations, but that the surface-to-volume ratio was elevated in low-nitrogen conditions, I extended this to a C-GRAM that additionally accounted for the cell cycle, cell division, cell wall biosynthesis, and the effect of molecular crowding on the ribosomal efficiency.Open Acces