Vassily Hatzimanikatis

A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

An updated genome‐scale reconstruction of the metabolic network in Escherichia coli K‐12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome‐scale metabolic model to predict high‐throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

Exploring the diversity of complex metabolic networks

MOTIVATION Metabolism, the network of chemical reactions that make life possible, is one of the most complex processes in nature. We describe here the development of a computational approach for the identification of every possible biochemical reaction from a given set of enzyme reaction rules that allows the de novo synthesis of metabolic pathways composed of these reactions, and the evaluation of these novel pathways with respect to their thermodynamic properties. RESULTS We applied this framework to the analysis of the aromatic amino acid pathways and discovered almost 75,000 novel biochemical routes from chorismate to phenylalanine, more than 350,000 from chorismate to tyrosine, but only 13 from chorismate to tryptophan. Thermodynamic analysis of these pathways suggests that the native pathways are thermodynamically more favorable than the alternative possible pathways. The pathways generated involve compounds that exist in biological databases, as well as compounds that exist in chemical databases and novel compounds, suggesting novel biochemical routes for these compounds and the existence of biochemical compounds that remain to be discovered or synthesized through enzyme and pathway engineering. AVAILABILITY Framework will be available via web interface at http://systemsbiology.northwestern.edu/BNICE (site under construction). CONTACT vassily@northwestern.edu or broadbelt@northwestern.edu SUPPLEMENTARY INFORMATION http://systemsbiology.northwestern.edu/BNICE/publications.

Metabolic flux analysis of hybridoma cells in different culture media using mass balances

The estimation of the intracellular fluxes of mammalian cells using only the mass balances of the relevant metabolites is not possible because the set of linear equations defined by these mass balances is underdetermined. Either additional experimental flux data or additional theoretical constraints are required to find one unique flux distribution out of the solution space that is bound by the mass balances. Here, a method is developed using the latter approach. The uptake and production rates of amino acids, glucose, lactate, O2, CO2, NH4, MAB, and the intracellular amino acid pools have been determined for two different steady‐states. The cellular composition {total protein and protein composition, total lipids and fatty acid distribution, total carbohydrates, DNA and RNA} has been measured to calculate the requirements for biosynthesis. It is shown to be essential to determine the uptake/production rates of ammonia and either carbon dioxide or oxygen. In mammalian cells these are cometabolites of cyclic metabolic pathways. The flux distribution that is found using the Euclidean minimum norm as the additional theoretical constraint and taking either the CO2 or the NAD(P)H mass balance into account is shown to be in agreement with the measured O2 and CO2 metabolic rates.

Inverse metabolic engineering: A strategy for directed genetic engineering of useful phenotypes

The classical method of metabolic engineering, identifying a rate-determining step in a pathway and alleviating the bottleneck by enzyme overexpression, has motivated much research but has enjoyed only limited practical success. Intervention of other limiting steps, of counter-balancing regulation, and of unknown coupled pathways often confounds this direct approach. Here the concept of inverse metabolic engineering is codified and its application is illustrated with several examples. Inverse metabolic engineering means the elucidation of a metabolic engineering strategy by: first, identifying, constructing, or calculating a desired phenotype; second, determining the genetic or the particular environmental factors conferring that phenotype; and third, endowing that phenotype on another strain or organism by directed genetic or environmental manipulation. This paradigm has been successfully applied in several contexts, including elimination of growth factor requirements in mammalian cell culture and increasing the energetic efficiency of microaerobic bacterial respiration.

Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3‐hydroxypropanoate

Sustainable microbial production of high‐value organic compounds such as 3‐hydroxypropanoate (3HP) is becoming an increasingly attractive alternative to organic syntheses that utilize petrochemical feedstocks. We applied the Biochemical Network Integrated Computational Explorer (BNICE) framework to the automated design and evaluation of novel biosynthetic routes for the production of 3HP from pyruvate. Among the pathways generated by the BNICE framework were all of the known pathways for the production of 3HP as well as numerous novel pathways. The pathways generated by BNICE were ranked based on four criteria: pathway length, thermodynamic feasibility, maximum achievable yield to 3HP from glucose, and maximum achievable activity at which 3HP can be produced. Four pathways emerged from this ranking as the most promising for the biosynthesis of 3HP, and three of these pathways, including the shortest pathways discovered, were novel. We also discovered novel routes for the biosynthesis of 28 commercially available compounds that are currently produced exclusively through organic synthesis. Examination of the optimal pathways for the biosynthesis of these 28 compounds in E. coli revealed pyruvate and succinate to be ideal intermediates for achieving high product yields from glucose. Biotechnol. Bioeng. 2010; 106: 462–473.

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Proteomics: Theoretical and Experimental Considerations

Cellular engineering relies on the ability to decipher the genetic basis of various phenotypes. Emerging technologies for analyzing the biological function of the information encoded in the genome of particular organisms and/or tissues focus on the monitoring of transcription (mRNA) and translation (protein) processes. Elementary theoretical considerations presented in this article strongly suggest that a combination of mRNA and protein expression patterns should be simultaneously considered to fully develop a conceptual understanding of the functional architecture of genomes and gene networks. We propose a framework of experimental and mathematical methods for acquiring and analyzing quantitative proteomic information and discuss recent developments in proteome analytical technology.

Manipulating redox and ATP balancing for improved production of succinate in E. coli

Redox and energy balance plays a key role in determining microbial fitness. Efforts to redirect bacterial metabolism often involve overexpression and deletion of genes surrounding key central metabolites, such as pyruvate and acetyl-coA. In the case of metabolic engineering of Escherichia coli for succinate production, efforts have mainly focused on the manipulation of key pyruvate metabolizing enzymes. E. coli AFP111 strain lacking ldhA, pflB and ptsG encoded activities accumulates acetate and ethanol as well as shows poor anaerobic growth on rich and minimal media. To address these issues, we first deleted genes (adhE, ackA-pta) involved in byproduct formation downstream of acetyl-CoA followed by the deletion of iclR and pdhR to activate the glyoxylate pathway. Based on data from these studies, we hypothesized that the succinate productivity was limited by the insufficient ATP generation. Genome-scale thermodynamics-based flux balance analysis indicated that overexpression of ATP-forming PEPCK from Actinobacillus succinogenes in an ldhA, pflB and ptsG triple mutant strain could result in an increase in biomass and succinate flux. Testing of this prediction confirmed that PEPCK overexpression resulted in a 60% increase in biomass and succinate formation in the ldhA, pflB, ptsG mutant strain.

Effect of Vitreoscilla hemoglobin dosage on microaerobic Escherichia coli carbon and energy metabolism

The amount of Vitreoscilla hemoglobin (VHb) expression was modulated over a broad range with an isopropyl‐β‐D‐thiogalactopyranoside‐ (IPTG‐) inducible plasmid, and the consequences on microaerobic Escherichia coli physiology were examined in glucose fed‐batch cultivations. The effect of IPTG induction on growth under oxygen‐limited conditions was most visible during late fed‐batch phase where the final cell density increased initially linearly with increasing VHb concentrations, ultimately saturating at a 2.7‐fold increase over the VHb‐negative (Vhb−) control. During the same growth phase, the specific excretions of fermentation by‐products, acetate, ethanol, formate, lactate, and succinate from the culture expressing the highest amount of VHb were reduced by 25%, 49%, 68%, 72%, and 50%, respectively, relative to the VHb− control. During the exponential growth phase, VHb exerted a positive but smaller control on growth rate, growth yield, and respiration. Varying the amount of VHb from 0 to 3.8 μmol/g dry cell weight (DCW) increased the specific growth rate, the growth yield, and the oxygen consumption rate by 33%, 35%, and 60%, respectively. Increasing VHb concentration to 3.8 μmol/g DCW suppressed the rate of carbon dioxide evolution in the exponential phase by 30%. A metabolic flux distribution analysis incorporating data from these cultivations discloses that VHb+ cells direct a larger fraction of glucose toward the pentose phosphate pathway and a smaller fraction of carbon through the tricarboxylic acid cycle from acetyl coenzyme A. The overall nicotinamide adenine dinucleotide [NAD(P)H] flux balance indicates that VHb‐expressing cells generate a net NADH flux by the NADH/NADPH transhydrogenase while the VHb− cells yield a net NADPH flux under the same growth conditions. Flux distribution analysis also reveals that VHb+ cells have a smaller adenosine triphosphate (ATP) synthesis rate from substrate‐level phosphorylation but a larger overall ATP production rate under microaerobic conditions. The thermodynamic efficiency of growth, based on reducing equivalents generated per unit of biomass produced, is greater for VHb+ cells.

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints.

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.