Design and Multiobjective Dynamic Optimization of Superheaters for Load-Following Operation in Pulverized Coal Power Plants.

Pulverized coal power plants are increasingly participating in aggressive load-following markets, therefore necessitating the design and optimization of primary superheaters for flexible operations. These superheaters play a critical role in maintaining the final steam temperature of the steam turbine, but their high operating temperatures and pressures make them prone to failure. This study focuses on the optimal design of future-generation primary superheaters for a fast load-following operation. To achieve this, a detailed first-principles model of a primary superheater is developed along with submodels for stress and fatigue damage. Two single-objective optimization problems are solved: one for minimizing metal mass as a measure of capital cost and another for minimizing pressure drop on the steam side as a measure of operating cost. Since these objective functions conflict, a multiobjective optimization problem is executed using a weighted metric methodology. Results from these optimization studies show that the base case design can violate stress constraints during the aggressive load-following operation. However, by optimizing the design variables, it is possible to not only satisfy tight stress constraints but also achieve the desired number of allowable cycles and adhere to the steam outlet temperature constraint. In addition, the optimized design reduces either the metal mass or the steam-side pressure drop compared to that of the base case design. Importantly, this approach is not limited to primary superheaters alone but can also be applied to similar high-temperature heat exchangers in other applications.

Development of a commercial scale process for production of 1,4-butanediol from sugar.

A sustainable bioprocess for the production of 1,4-butanediol (BDO) from carbohydrate feedstocks was developed. BDO is a chemical intermediate that goes into a variety of products including automotive parts, electronics, and apparel, and is currently manufactured commercially through energy-intensive petrochemical processes using fossil raw materials. This review highlights the development of an Escherichia coli strain and an overall process that successfully performed at commercial scale for direct production of bio-BDO from dextrose. Achieving such high level performance required an integrated technology platform enabling detailed engineering of enzyme, pathway, metabolic network, and organism, as well as development of effective fermentation and downstream recovery processes.

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models.

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

An integrated biotechnology platform for developing sustainable chemical processes.

Genomatica has established an integrated computational/experimental metabolic engineering platform to design, create, and optimize novel high performance organisms and bioprocesses. Here we present our platform and its use to develop E. coli strains for production of the industrial chemical 1,4-butanediol (BDO) from sugars. A series of examples are given to demonstrate how a rational approach to strain engineering, including carefully designed diagnostic experiments, provided critical insights about pathway bottlenecks, byproducts, expression balancing, and commercial robustness, leading to a superior BDO production strain and process.

Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol.

1,4-Butanediol (BDO) is an important commodity chemical used to manufacture over 2.5 million tons annually of valuable polymers, and it is currently produced exclusively through feedstocks derived from oil and natural gas. Herein we report what are to our knowledge the first direct biocatalytic routes to BDO from renewable carbohydrate feedstocks, leading to a strain of Escherichia coli capable of producing 18 g l(-1) of this highly reduced, non-natural chemical. A pathway-identification algorithm elucidated multiple pathways for the biosynthesis of BDO from common metabolic intermediates. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic operation of the oxidative tricarboxylic acid cycle, thereby generating reducing power to drive the BDO pathway. The organism produced BDO from glucose, xylose, sucrose and biomass-derived mixed sugar streams. This work demonstrates a systems-based metabolic engineering approach to strain design and development that can enable new bioprocesses for commodity chemicals that are not naturally produced by living cells.

Geobacter sulfurreducens strain engineered for increased rates of respiration.

Geobacter species are among the most effective microorganisms known for the bioremediation of radioactive and toxic metals in contaminated subsurface environments and for converting organic compounds to electricity in microbial fuel cells. However, faster rates of electron transfer could aid in optimizing these processes. Therefore, the Optknock strain design methodology was applied in an iterative manner to the constraint-based, in silico model of Geobacter sulfurreducens to identify gene deletions predicted to increase respiration rates. The common factor in the Optknock predictions was that each resulted in a predicted increase in the cellular ATP demand, either by creating ATP-consuming futile cycles or decreasing the availability of reducing equivalents and inorganic phosphate for ATP biosynthesis. The in silico model predicted that increasing the ATP demand would result in higher fluxes of acetate through the TCA cycle and higher rates of NADPH oxidation coupled with decreases in flux in reactions that funnel acetate toward biosynthetic pathways. A strain of G. sulfurreducens was constructed in which the hydrolytic, F(1) portion of the membrane-bound F(0)F(1) (H(+))-ATP synthase complex was expressed when IPTG was added to the medium. Induction of the ATP drain decreased the ATP content of the cell by more than half. The cells with the ATP drain had higher rates of respiration, slower growth rates, and a lower cell yield. Genome-wide analysis of gene transcript levels indicated that when the higher rate of respiration was induced transcript levels were higher for genes involved in energy metabolism, especially in those encoding TCA cycle enzymes, subunits of the NADH dehydrogenase, and proteins involved in electron acceptor reduction. This was accompanied by lower transcript levels for genes encoding proteins involved in amino acid biosynthesis, cell growth, and motility. Several changes in gene expression that involve processes not included in the in silico model were also detected, including increased expression of a number of redox-active proteins, such as c-type cytochromes and a putative multicopper outer-surface protein. The results demonstrate that it is possible to genetically engineer increased respiration rates in G. sulfurreducens in accordance with predictions from in silico metabolic modeling. To our knowledge, this is the first report of metabolic engineering to increase the respiratory rate of a microorganism.

Predicting biological system objectives de novo from internal state measurements.

BACKGROUND: Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth) and the subsequent application of linear programming (LP) to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning. RESULTS: We present a novel method called Biological Objective Solution Search (BOSS) for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, BOSS identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant in silico flux distribution and available experimental (e.g., isotopomer) flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of Saccharomyces cerevisiae. CONCLUSION: We illustrate how BOSS offers insight into the functional organization of biochemical networks, facilitating the interrogation of cellular design principles and development of cellular engineering applications. Furthermore, we describe how growth is the best-fit objective function for the yeast metabolic network given experimentally-measured fluxes.

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.

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.

In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

The development and validation of new methods to help direct rational strain design for metabolite overproduction remains an important problem in metabolic engineering. Here we show that computationally predicted E. coli strain designs, calculated from a genome-scale metabolic model, can lead to successful production strains and that adaptive evolution of the engineered strains can lead to improved production capabilities. Three strain designs for lactate production were implemented yielding a total of 11 evolved production strains that were used to demonstrate the utility of this integrated approach. Strains grown on 2 g/L glucose at 37 degrees C showed lactate titers ranging from 0.87 to 1.75 g/L and secretion rates that were directly coupled to growth rates.

Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions.

In this article, we introduce metabolite concentration coupling analysis (MCCA) to study conservation relationships for metabolite concentrations in genome-scale metabolic networks. The analysis allows the global identification of subsets of metabolites whose concentrations are always coupled within common conserved pools. Also, the minimal conserved pool identification (MCPI) procedure is developed for elucidating conserved pools for targeted metabolites without computing the entire basis conservation relationships. The approaches are demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. Despite significant differences in the size and complexity of the examined organism's models, we find that the concentrations of nearly all metabolites are coupled within a relatively small number of subsets. These correspond to the overall exchange of carbon molecules into and out of the networks, interconversion of energy and redox cofactors, and the transfer of nitrogen, sulfur, phosphate, coenzyme A, and acyl carrier protein moieties among metabolites. The presence of large conserved pools can be viewed as global biophysical barriers protecting cellular systems from stresses, maintaining coordinated interconversions between key metabolites, and providing an additional mode of global metabolic regulation. The developed approaches thus provide novel and versatile tools for elucidating coupling relationships between metabolite concentrations with implications in biotechnological and medical applications.

OptStrain: a computational framework for redesign of microbial production systems.

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.

Flux coupling analysis of genome-scale metabolic network reconstructions.

In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v(1) and v(2), are (1) directionally coupled, if a non-zero flux for v(1) implies a non-zero flux for v(2) but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v(1) implies a non-zero, though variable, flux for v(2) and vice versa; or (3) fully coupled, if a non-zero flux for v(1) implies not only a non-zero but also a fixed flux for v(2) and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations.

Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization.

The advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. In this work, the computational OptKnock framework is introduced for suggesting gene deletion strategies leading to the overproduction of chemicals or biochemicals in E. coli. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) must be accompanied, due to stoichiometry, by the production of a desired product. Computational results for gene deletions for succinate, lactate, and 1,3-propanediol (PDO) production are in good agreement with mutant strains published in the literature. While some of the suggested deletion strategies are straightforward and involve eliminating competing reaction pathways, many others suggest complex and nonintuitive mechanisms of compensating for the removed functionalities. Finally, the OptKnock procedure, by coupling biomass formation with chemical production, hints at a growth selection/adaptation system for indirectly evolving overproducing mutants.

Optimization-based framework for inferring and testing hypothesized metabolic objective functions.

An optimization-based framework is introduced for testing whether experimental flux data are consistent with different hypothesized objective functions. Specifically, we examine whether the maximization of a weighted combination of fluxes can explain a set of observed experimental data. Coefficients of importance (CoIs) are identified that quantify the fraction of the additive contribution of a given flux to a fitness (objective) function with an optimization that can explain the experimental flux data. A high CoI value implies that the experimental flux data are consistent with the hypothesis that the corresponding flux is maximized by the network, whereas a low value implies the converse. This framework (i.e., ObjFind) is applied to both an aerobic and anaerobic set of Escherichia coli flux data derived from isotopomer analysis. Results reveal that the CoIs for both growth conditions are strikingly similar, even though the flux distributions for the two cases are quite different, which is consistent with the presence of a single metabolic objective driving the flux distributions in both cases. Interestingly, the CoI associated with a biomass production flux, complete with energy and reducing power requirements, assumes a value 9 and 15 times higher than the next largest coefficient for the aerobic and anaerobic cases, respectively.

Exploring the overproduction of amino acids using the bilevel optimization framework OptKnock.

In this study, we modify and extend the bilevel optimization framework OptKnock for identifying gene knockout strategies in the Escherichia coli metabolic network, leading to the overproduction of representative amino acids and key precursors for all five families. These strategies span not only the central metabolic network genes but also the amino acid biosynthetic and degradation pathways. In addition to gene deletions, the transport rates of carbon dioxide, ammonia, and oxygen, as well as the secretion pathways for key metabolites, are introduced as optimization variables in the framework. Computational results demonstrate the importance of manipulating energy-producing/consuming pathways, controlling the uptake of nitrogen and oxygen, and blocking the secretion pathways of key competing metabolites. The identified pathway modifications include not only straightforward elimination of competing reactions but also a number of nonintuitive knockouts quite distant from the amino acid-producing pathways. Specifically, OptKnock suggests three reactions (i.e., pyruvate kinase, phosphotransacetylase, and ATPase) for deletion, in addition to the straightforward elimination of 2-ketoglutarate dehydrogenase, to generate a glutamate-overproducing mutant. Similarly, phosphofructokinase and ATPase are identified as promising knockout targets to complement the removal of pyruvate formate lyase and pyruvate dehydrogenase for enhancing the yield of alanine. Although OptKnock in its present form does not consider regulatory constraints, it does provide useful suggestions largely in agreement with existing practices and, more importantly, introduces a framework for incorporating additional modeling refinements as they become available.