Modeling and simulation: tools for metabolic engineering

Translating biochemical network models between different kinetic formats

Mechanistic biochemical network models describe the dynamics of intracellular metabolite pools in terms of substance concentrations, stoichiometry and reaction kinetics. Data from stimulus response experiments are currently the most informative source for in-vivo parameter estimation in such models. However, only a part of the parameters of classical enzyme kinetic models can usually be estimated from typical stimulus response data. For this reason, several alternative kinetic formats using different "languages" (e.g. linear, power laws, linlog, generic and convenience) have been proposed to reduce the model complexity. The present contribution takes a rigorous "multi-lingual" approach to data evaluation by translating biochemical network models from one kinetic format into another. For this purpose, a new high-performance algorithm has been developed and tested. Starting with a given model, it replaces as many kinetic terms as possible by alternative expressions while still reproducing the experimental data. Application of the algorithm to a published model for Escherichia coli's sugar metabolism demonstrates the power of the new method. It is shown that model translation is a powerful tool to investigate the information content of stimulus response data and the predictive power of models. Moreover, the local and global approximation capabilities of the models are elucidated and some pitfalls of traditional single model approaches to data evaluation are revealed.

Fueling industrial biotechnology growth with bioethanol

Industrial biotechnology is the conversion of biomass via biocatalysis, microbial fermentation, or cell culture to produce chemicals, materials, and/or energy. Industrial biotechnology processes aim to be cost-competitive, environmentally favorable, and self-sustaining compared to their petrochemical equivalents. Common to all processes for the production of energy, commodity, added value, or fine chemicals is that raw materials comprise the most significant cost fraction, particularly as operating efficiencies increase through practice and improving technologies. Today, crude petroleum represents the dominant raw material for the energy and chemical sectors worldwide. Within the last 5 years petroleum prices, stability, and supply have increased, decreased, and been threatened, respectively, driving a renewed interest across academic, government, and corporate centers to utilize biomass as an alternative raw material. Specifically, bio-based ethanol as an alternative biofuel has emerged as the single largest biotechnology commodity, with close to 46 billion L produced worldwide in 2005. Bioethanol is a leading example of how systems biology tools have significantly enhanced metabolic engineering, inverse metabolic engineering, and protein and enzyme engineering strategies. This enhancement stems from method development for measurement, analysis, and data integration of functional genomics, including the transcriptome, proteome, metabolome, and fluxome. This review will show that future industrial biotechnology process development will benefit tremendously from the precedent set by bioethanol - that enabling technologies (e.g., systems biology tools) coupled with favorable economic and socio-political driving forces do yield profitable, sustainable, and environmentally responsible processes. Biofuel will continue to be the keystone of any industrial biotechnology-based economy whereby biorefineries leverage common raw materials and unit operations to integrate diverse processes to produce demand-driven product portfolios.

Metabolic reconstruction and analysis for parasite genomes

With the completion of sequencing projects for several parasite genomes, efforts are ongoing to make sense of this mass of information in terms of the gene products encoded and their interactions in the growth, development and survival of parasites. The emerging science of systems biology aims to explain the complex relationship between genotype and phenotype by using network models. One area in which this approach has been particularly successful is in the modeling of metabolism. With an accurate picture of the set of metabolic reactions encoded in a genome, it is now possible to identify enzymes or transporters that might be viable targets for new drugs. Because these predictions greatly depend on the quality and completeness of the genome annotation, there are substantial efforts in the scientific community to increase the numbers of metabolic enzymes identified. In this review, we discuss the opportunities for using metabolic reconstruction and analysis tools in parasitology research, and their applications to protozoan parasites.

Something from nothing: bridging the gap between constraint-based and kinetic modelling

Two divergent modelling methodologies have been adopted to increase our understanding of metabolism and its regulation. Constraint-based modelling highlights the optimal path through a stoichiometric network within certain physicochemical constraints. Such an approach requires minimal biological data to make quantitative inferences about network behaviour; however, constraint-based modelling is unable to give an insight into cellular substrate concentrations. In contrast, kinetic modelling aims to characterize fully the mechanics of each enzymatic reaction. This approach suffers because parameterizing mechanistic models is both costly and time-consuming. In this paper, we outline a method for developing a kinetic model for a metabolic network, based solely on the knowledge of reaction stoichiometries. Fluxes through the system, estimated by flux balance analysis, are allowed to vary dynamically according to linlog kinetics. Elasticities are estimated from stoichiometric considerations. When compared to a popular branched model of yeast glycolysis, we observe an excellent agreement between the real and approximate models, despite the absence of (and indeed the requirement for) experimental data for kinetic constants. Moreover, using this particular methodology affords us analytical forms for steady state determination, stability analyses and studies of dynamical behaviour.

Metabolomics: current state and evolving methodologies and tools

In recent years, metabolomics developed to an accepted and valuable tool in life sciences. Substantial improvements of analytical hardware allow metabolomics to run routinely now. Data are successfully used to investigate genotype-phenotype relations of strains and mutants. Metabolomics facilitates metabolic engineering to optimise mircoorganisms for white biotechnology and spreads to the investigation of biotransformations and cell culture. Metabolomics serves not only as a source of qualitative but also quantitative data of intra-cellular metabolites essential for the model-based description of the metabolic network operating under in vivo conditions. To collect reliable metabolome data sets, culture and sampling conditions, as well as the cells' metabolic state, are crucial. Hence, application of biochemical engineering principles and method standardisation efforts become important. Together with the other more established omics technologies, metabolomics will strengthen its claim to contribute to the detailed understanding of the in vivo function of gene products, biochemical and regulatory networks and, even more ambitious, the mathematical description and simulation of the whole cell in the systems biology approach. This knowledge will allow the construction of designer organisms for process application using biotransformation and fermentative approaches making effective use of single enzymes, whole microbial and even higher cells.

Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering

Background

Nowadays, the focus in metabolic engineering research is shifting from massive overexpression and inactivation of genes towards the model-based fine tuning of gene expression. In this context, the construction of a library of synthetic promoters of Escherichia coli as a useful tool for fine tuning gene expression is discussed here.

Results

A degenerated oligonucleotide sequence that encodes consensus sequences for E. coli promoters separated by spacers of random sequences has been designed and synthesized. This 57 bp long sequence contains 24 conserved, 13 semi-conserved (W, R and D) and 20 random nucleotides. This mixture of DNA fragments was cloned into a promoter probing vector (pVIK165). The ligation mixtures were transformed into competent E. coli MA8 and the resulting clones were screened for GFP activity by measuring the relative fluorescence units; some clones produced high fluorescence intensity, others weak fluorescence intensity. The clones cover a range of promoter activities from 21.79 RFU/OD₆₀₀ ml to 7606.83 RFU/OD₆₀₀ ml. 57 promoters were sequenced and used for promoter analysis. The present results conclusively show that the postulates, which link promoter strength to anomalies in the -10 box and/or -35 box, and to the length of the spacer, are not generally valid. However, by applying Partial Least Squares regression, a model describing the promoter strength was built and validated.

Conclusion

For Escherichia coli, the promoter strength can not been linked to anomalies in the -10 box and/or -35 box, and to the length of the spacer. Also a probabilistic approach to relate the promoter sequence to its strength has some drawbacks. However, by applying Partial Least Squares regression, a good correlation was found between promoter sequence and promoter strength. This PLS model can be a useful tool to rationally design a suitable promoter in order to fine tune gene expression.

Systems biology for industrial strains and fermentation processes—Example: Amino acids

Systems biology is attracting significant interest finding applications not only in pharmaceutical development but also for basic studies on microbial systems. The latter often concentrate on the quantitative understanding of global regulation phenomena. So far, these activities are dominated by academic groups basically mirroring the necessity to prepare the sound scientific understanding first, before industrial applications can be derived later. However, this short-term view may not be sufficient because systems biology already offers numerous benefits for industrial applications, provided that special constraints are considered. This contribution indicates some of the constraints worth noticing when industrial systems biology projects are carried out. Consequently, differences in project structure and goals between purely academic and industrial systems biology projects are outlined.

Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography-mass spectrometry

A highly selective and sensitive method for identification and quantification of intracellular metabolites involved in central carbon metabolism (including glycolysis, pentose phosphate pathway and tricarboxylic acid cycle) by means of liquid chromatography-tandem quadrupole mass spectrometry (LC-MS/MS) was developed. The volatile ion pair modifier tributylammonium acetate (TBAA) was employed in the mobile phase for simultaneously separation of 29 negatively charged compounds including sugar phosphates, nucleotides, and carboxylic acids on a common C18 reversed-phase column. Method validation results displayed that limits of detection (LODs) calculated according to DIN (German Institute for Standardization) 32645 are mostly below 60 nM, only with the exception of pyruvate and malate. The calibration curves showed excellent linearity mainly over three orders of magnitude with correlation coefficients R(2)>0.9982. This LC-MS/MS method was successfully applied to determine these metabolites in cell extracts of Escherichia coli. Most of the intracellular metabolites were found within the detection range and the relative standard deviations of the measurements were smaller than 5.65% (n=5) for a cell extract sample.

Understanding the roadmap of metabolism by pathway analysis

The theoretical investigation of the structure of metabolic systems has recently attracted increasing interest. In this chapter, the basic concepts of metabolic pathway analysis are described and various applications are outlined. In particular, the concepts of nullspace and elementary flux modes are explained. The presentation is illustrated by a simple example from tyrosine metabolism and a system describing lysine production in Corynebacterium glutamicum. The latter system gives rise to 37 elementary modes, 36 of which produce lysine with different molar yields. The examples illustrate that metabolic pathway analysis is a useful tool for better understanding the complex architecture of intracellular metabolism, for determining the pathways on which the molar conversion yield of a substrate-product pair under study is maximal, and for assigning functions to orphan genes (functional genomics). Moreover, problems emerging in the modeling of large networks are discussed. An outlook on current trends in the field concludes the chapter.

Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived C-labelling data

This study focuses on unravelling the carbon and redox metabolism of a previously developed glycerol-overproducing Saccharomyces cerevisiae strain with deletions in the structural genes encoding triosephosphate isomerase (TPI1), the external mitochondrial NADH dehydrogenases (NDE1 and NDE2) and the respiratory chain-linked glycerol-3-phosphate dehydrogenase (GUT2). Two methods were used for analysis of metabolic fluxes: metabolite balancing and (13)C-labelling-based metabolic flux analysis. The isotopic enrichment of intracellular primary metabolites was measured both directly (liquid chromatography-MS) and indirectly through proteinogenic amino acids (nuclear magnetic resonance and gas chromatography-MS). Because flux sensitivity around several important metabolic nodes proved to be dependent on the applied technique, the combination of the three (13)C quantification techniques generated the most accurate overall flux pattern. When combined, the measured conversion rates and (13)C-labelling data provided evidence that a combination of assimilatory metabolism and pentose phosphate pathway activity diverted some of the carbon away from glycerol formation. Metabolite balancing indicated that this results in excess cytosolic NADH, suggesting the presence of a cytosolic NADH sink in addition to those that were deleted. The exchange flux of four-carbon dicarboxylic acids across the mitochondrial membrane, as measured by the (13)C-labelling data, supports a possible role of a malate/aspartate or malate/oxaloacetate redox shuttle in the transfer of these redox equivalents from the cytosol to the mitochondrial matrix.

Effects of the presence of ColE1 plasmid DNA in Escherichia coli on the host cell metabolism

Background

Although understanding of physiological interactions between plasmid DNA and its host is important for vector design and host optimization in many biotechnological applications, to our knowledge, global studies on plasmid-host interactions have not been performed to date even for well-characterized plasmids.

Results

Escherichia coli cells, either devoid of plasmid DNA or bearing plasmid pOri1 (with a single ColE1 replication origin) or plasmid pOri2 (with double ColE1 replication origins), were cultured in a chemostat. We used a combination of metabolic flux analysis, DNA microarray and enzyme activity analysis methods to explore differences in the metabolism between these strains. We found that the presence of plasmids significantly influenced various metabolic pathways in the host cells, e.g. glycolysis, the tricarboxylic acid (TCA) cycle and the pentose phosphate (PP) pathway. Expression of rpiA, a gene coding for ribose-5-phosphate isomerase A, was considerably decreased in E. coli carrying a high copy number plasmid relative to E. coli carrying a low copy number plasmid and plasmid-free E. coli. The rpiA gene was cloned into an expression vector to construct plasmid pETrpiA. Following induction of pETrpiA-bearing E. coli, which harbored either pOri1 or pOri2, with isopropyl-β-D-thiogalactopyranoside (IPTG), the copy number of pOri1 and pOri2 was sigificantly higher than that measured in a host devoid of pETrpiA.

Conclusion

The presence of plasmids can significantly influence some metabolic pathways in the host cell. We believe that the results of detailed metabolic analysis may be useful in optimizing host strains, vectors and cultivation conditions for various biotechnological purposes.

Short-term metabolome dynamics and carbon, electron, and ATP balances in chemostat-grown Saccharomyces cerevisiae CEN.PK 113-7D following a glucose pulse

The in vivo kinetics in Saccharomyces cerevisiae CEN.PK 113-7D was evaluated during a 300-second transient period after applying a glucose pulse to an aerobic, carbon-limited chemostat culture. We quantified the responses of extracellular metabolites, intracellular intermediates in primary metabolism, intracellular free amino acids, and in vivo rates of O(2) uptake and CO(2) evolution. With these measurements, dynamic carbon, electron, and ATP balances were set up to identify major carbon, electron, and energy sinks during the postpulse period. There were three distinct metabolic phases during this time. In phase I (0 to 50 seconds after the pulse), the carbon/electron balances closed up to 85%. The accumulation of glycolytic and storage compounds accounted for 60% of the consumed glucose, caused an energy depletion, and may have led to a temporary decrease in the anabolic flux. In phase II (50 to 150 seconds), the fermentative metabolism gradually became the most important carbon/electron sink. In phase III (150 to 300 seconds), 29% of the carbon uptake was not identified in the measurements, and the ATP balance had a large surplus. These results indicate an increase in the anabolic flux, which is consistent with macroscopic balances of extracellular fluxes and the observed increase in CO(2) evolution associated with nonfermentative metabolism. The identified metabolic processes involving major carbon, electron, and energy sinks must be taken into account in in vivo kinetic models based on short-term dynamic metabolome responses.

Emerging Corynebacterium glutamicum systems biology

Corynebacterium glutamicum is widely used for the biotechnological production of amino acids. Amino acid producing strains have been improved classically by mutagenesis and screening as well as in a rational manner using recombinant DNA technology. Metabolic flux analysis may be viewed as the first systems approach to C. glutamicum physiology since it combines isotope labeling data with metabolic network models of the biosynthetic and central metabolic pathways. However, only the complete genome sequence of C. glutamicum and post-genomics methods such as transcriptomics and proteomics have allowed characterizing metabolic and regulatory properties of this bacterium on a truly global level. Besides transcriptomics and proteomics, metabolomics and modeling approaches have now been established. Systems biology, which uses systematic genomic, proteomic and metabolomic technologies with the final aim of constructing comprehensive and predictive models of complex biological systems, is emerging for C. glutamicum. We will present current developments that advanced our insight into fundamental biology of C. glutamicum and that in the future will enable novel biotechnological applications for the improvement of amino acid production.

Development of a kinetic metabolic model: application to Catharanthus roseus hairy root

A kinetic metabolic model describing Catharanthus roseus hairy root growth and nutrition was developed. The metabolic network includes glycolysis, pentose-phosphate pathway, TCA cycle and the catabolic reactions leading to cell building blocks such as amino acids, organic acids, organic phosphates, lipids and structural hexoses. The central primary metabolic network was taken at pseudo-steady state and metabolic flux analysis technique allowed reducing from 31 metabolic fluxes to 20 independent pathways. Hairy root specific growth rate was described as a function of intracellular concentration in cell building blocks. Intracellular transport and accumulation kinetics for major nutrients were included. The model uses intracellular nutrients as well as energy shuttles to describe metabolic regulation. Model calibration was performed using experimental data obtained from batch and medium exchange liquid cultures of C. roseus hairy root using a minimal medium in Petri dish. The model is efficient in estimating the growth rate.

Comparison of DNA and RNA quantification methods suitable for parameter estimation in metabolic modeling of microorganisms

Recent developments in cellular and molecular biology require the accurate quantification of DNA and RNA in large numbers of samples at a sensitivity that enables determination on small quantities. In this study, five current methods for nucleic acid quantification were compared: (i) UV absorbance spectroscopy at 260 nm, (ii) colorimetric reaction with orcinol reagent, (iii) colorimetric reaction based on diphenylamine, (iv) fluorescence detection with Hoechst 33258 reagent, and (v) fluorescence detection with thiazole orange reagent. Genomic DNA of three different microbial species (with widely different G+C content) was used, as were two different types of yeast RNA and a mixture of equal quantities of DNA and RNA. We can conclude that for nucleic acid quantification, a standard curve with DNA of the microbial strain under study is the best reference. Fluorescence detection with Hoechst 33258 reagent is a sensitive and precise method for DNA quantification if the G+C content is less than 50%. In addition, this method allows quantification of very low levels of DNA (nanogram scale). Moreover, the samples can be crude cell extracts. Also, UV absorbance at 260 nm and fluorescence detection with thiazole orange reagent are sensitive methods for nucleic acid detection, but only if purified nucleic acids need to be measured.

Metabolic response to exogenous ethanol in yeast: An in vivo NMR and mathematical modelling approach

The understanding of the metabolic behaviour of complex systems such as eukaryotic cells needs the development of new approaches that are able to deal with the complexity due to a large number of interactions within the system. In this paper, we applied an approach based on the combined use of in vivo NMR experiments and mathematical modelling in order to analyze the metabolic response to ethanol stress in a wild-strain of Saccharomyces cerevisiae. Considering the cellular metabolic processes resulting from activation, inhibition, and feed-back activities, we developed a model able to describe the modulation of the whole system induced by an external stress due to increasing concentrations of exogenous ethanol. This approach was able to interpret the experimental results in terms of metabolic response to exogenous ethanol in the yeast. The robustness and flexibility of the model enables it to work correctly at different initial exogenous ethanol concentrations.

Modelling strategies for the industrial exploitation of lactic acid bacteria

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.

Metabonomic modeling of drug toxicity

Global metabolic profiling (metabonomics/metabolomics) has shown particular promise in the area of toxicology and drug development. In both preclinical screening and mechanistic exploration, metabolic profiling can offer rapid, noninvasive toxicological information that is robust and reproducible, with little or no added technical resources to existing studies in drug metabolism and toxicity. In this review, the study design and analytical technology required for metabonomics are discussed, along with key examples of how fundamental questions in drug development can be addressed. Strategies for metabonomic data analysis in toxicity assessment are detailed in both principle and practice, together with a description of toxicologically relevant metabolic biomarkers. Extended into the assessment of efficacy and toxicity in the clinic, metabonomics may prove crucial in making personalized therapy and pharmacogenomics a reality.

Systems biotechnology for strain improvement

Various high-throughput experimental techniques are routinely used for generating large amounts of omics data. In parallel, in silico modelling and simulation approaches are being developed for quantitatively analyzing cellular metabolism at the systems level. Thus informative high-throughput analysis and predictive computational modelling or simulation can be combined to generate new knowledge through iterative modification of an in silico model and experimental design. On the basis of such global cellular information we can design cells that have improved metabolic properties for industrial applications. This article highlights the recent developments in these systems approaches, which we call systems biotechnology, and discusses future prospects.

Application of metabolome data in functional genomics: A conceptual strategy

A gene with yet unknown physiological function can be studied by changing its expression level followed by analysis of the resulting phenotype. This type of functional genomics study can be complicated by the occurrence of 'silent mutations', the phenotypes of which are not easily observable in terms of metabolic fluxes (e.g., the growth rate). Nevertheless, genetic alteration may give rise to significant yet complicated changes in the metabolome. We propose here a conceptual functional genomics strategy based on microbial metabolome data, which identifies changes in in vivo enzyme activities in the mutants. These predicted changes are used to formulate hypotheses to infer unknown gene functions. The required metabolome data can be obtained solely from high-throughput mass spectrometry analysis, which provides the following in vivo information: (1) the metabolite concentrations in the reference and the mutant strain; (2) the metabolic fluxes in both strains and (3) the enzyme kinetic parameters of the reference strain. We demonstrate in silico that changes in enzyme activities can be accurately predicted by this approach, even in 'silent mutants'.

A kinetic study of sugarcane sucrose synthase

The kinetic data on sugarcane (Saccharum spp. hybrids) sucrose synthase (SuSy, UDP-glucose: D-fructose 2-alpha-D-glucosyltransferase, EC 2.4.1.13) are limited. We characterized kinetically a SuSy activity partially purified from sugarcane variety N19 leaf roll tissue. Primary plot analysis and product inhibition studies showed that a compulsory order ternary complex mechanism is followed, with UDP binding first and UDP-glucose dissociating last from the enzyme. Product inhibition studies showed that UDP-glucose is a competitive inhibitor with respect to UDP and a mixed inhibitor with respect to sucrose. Fructose is a mixed inhibitor with regard to both sucrose and UDP. Kinetic constants are as follows: Km values (mm, +/- SE) were, for sucrose, 35.9 +/- 2.3; for UDP, 0.00191 +/- 0.00019; for UDP-glucose, 0.234 +/- 0.025 and for fructose, 6.49 +/- 0.61. values were, for sucrose, 227 mm; for UDP, 0.086 mm; for UDP-glucose, 0.104; and for fructose, 2.23 mm. Replacing estimated kinetic parameters of SuSy in a kinetic model of sucrose accumulation with experimentally determined parameters of the partially purified isoform had significant effects on model outputs, with a 41% increase in sucrose concentration and 7.5-fold reduction in fructose the most notable. Of the metabolites included in the model, fructose concentration was most affected by changes in SuSy activity: doubling and halving of SuSy activity reduced and increased the steady-state fructose concentration by about 42 and 140%, respectively. It is concluded that different isoforms of SuSy could have significant differential effects on metabolite concentrations in vivo, therefore impacting on metabolic regulation.

Investigating the dynamic behavior of biochemical networks using model families

MOTIVATION

Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support.

SUMMARY

To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects.

AVAILABILITY

http://www.simtec.mb.uni-siegen.de/

CONTACT

wiechert@simtec.mb.uni-siegen.de.

Analysis of dynamic labeling data

Comprehensive assessments of the organization and regulation of metabolic pathways cannot be limited to steady-state measurements alone but require dynamic time series data. One experimental means of generating such data consists of radioactively labeling precursors and measuring their fate over time. While labeling experiments belong to the standard repertoire of biological laboratory techniques, corresponding mathematical tools for analyzing the non-linear dynamics of tracers are scarce. The article addresses this issue, using Biochemical Systems Theory as the modeling framework. The description of the dynamics of labeled metabolites alone is difficult, but it is demonstrated that these difficulties are easily overcome by setting up dynamic models in two or three blocks, one for the kinetics of the total pools, the second just for the labeled portions, and the third, optional, block for the remaining unlabeled components. Since the dynamic model is not limited in complexity and can account for linear pathways, converging and diverging branches, cycles, and the various observed modes of regulation, the proposed method of non-linear tracer analysis is rather general and permits simulations of most standard labeling experiments, both at steady state and during transients.

The Systems Biology of Glycosylation

Glycosylation can have a profound influence on the function of a variety of eukaryotic cells. In particular, it can affect signal transduction and cell-cell communication properties and thus shape critical cell decisions, including the regulation of differentiation and apoptosis. Regulation of glycosylation has multiple layers of complexity, both structural and functional, which make its experimental and theoretical analysis difficult to perform and interpret. Novel research methodologies provided by systems biology can help to address many outstanding issues and integrate glycosylation with other metabolic and cell regulation processes. Here we review the toolbox available for biochemical systems analysis of glycosylation.

Use of genome-scale microbial models for metabolic engineering

Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction.

Toward large-scale modeling of the microbial cell for computer simulation

In the post-genomic era, the large-scale, systematic, and functional analysis of all cellular components using transcriptomics, proteomics, and metabolomics, together with bioinformatics for the analysis of the massive amount of data generated by these "omics" methods are the focus of intensive research activities. As a consequence of these developments, systems biology, whose goal is to comprehend the organism as a complex system arising from interactions between its multiple elements, becomes a more tangible objective. Mathematical modeling of microorganisms and subsequent computer simulations are effective tools for systems biology, which will lead to a better understanding of the microbial cell and will have immense ramifications for biological, medical, environmental sciences, and the pharmaceutical industry. In this review, we describe various types of mathematical models (structured, unstructured, static, dynamic, etc.), of microorganisms that have been in use for a while, and others that are emerging. Several biochemical/cellular simulation platforms to manipulate such models are summarized and the E-Cell system developed in our laboratory is introduced. Finally, our strategy for building a "whole cell metabolism model", including the experimental approach, is presented.

A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics

The control properties of biochemical pathways can be described by control coefficients and elasticities, as defined in the framework of metabolic control analysis. The determination of these parameters using the traditional metabolic control analysis relationships is, however, limited by experimental difficulties (e.g. realizing and measuring small changes in biological systems) and lack of appropriate mathematical procedures (e.g. when the more practical large changes are made). In this paper, the recently developed lin-log approach is proposed to avoid the above-mentioned problems and is applied to estimate control parameters from measurements obtained in steady state experiments. The lin-log approach employs approximative linear-logarithmic kinetics parameterized by elasticities and provides analytical solutions for fluxes and metabolite concentrations when large changes are made. Published flux and metabolite concentration data are used, obtained from a reconstructed section of glycolysis converting 3-phosphoglycerate to pyruvate [Giersch, C. (1995) Eur. J. Biochem. 227, 194-201]. With the lin-log approach, all data from different experiments can be combined to give realistic elasticity and flux control coefficient estimates by linear regression. Despite the large changes, a good agreement of fluxes and metabolite concentrations is obtained between the measured and calculated values according to the lin-log model. Furthermore, it is shown that the lin-log approach allows a rigorous statistical evaluation to identify the optimal reference state and the optimal model structure assumption. In conclusion, the lin-log approach addresses practical problems encountered in the traditional metabolic control analysis-based methods by introducing suitable nonlinear kinetics, thus providing a novel framework with improved procedures for the estimation of elasticities and control parameters from large perturbation experiments.

Metabolic engineering: advances in modeling and intervention in health and disease

The field of metabolic engineering encompasses a powerful set of tools that can be divided into (a) methods to model complex metabolic pathways and (b) techniques to manipulate these pathways for a desired metabolic outcome. These tools have recently seen increased utility in the medical arena, and this paper aims to review significant accomplishments made using these approaches. The modeling of metabolic pathways has been applied to better understand disease-state physiology in a variety of cellar, subcellular, and organ systems, including the liver, heart, mitochondria, and cancerous cells. Metabolic pathway engineering has been used to generate cells with novel biochemical functions for therapeutic use, and specific examples are provided in the areas of glycosylation engineering and dopamine-replacement therapy. In order to document the potential of applying both metabolic modeling and pathway manipulation, we describe pertinent advances in the field of diabetes research. Undoubtedly, as the field of metabolic engineering matures and is applied to a wider array of problems, new advances and therapeutic strategies will follow.

Oscillatory metabolism of Saccharomyces cerevisiae: an overview of mechanisms and models

The budding yeast Saccharomyces cerevisiae displays steady oscillations in continuous cultures under certain conditions. Oscillatory responses are important both metabolically and in process applications. Although much information has become available, a definitive theory to explain and model these oscillations is yet to be formulated. Models of oscillatory cultivation have focussed primarily either on intracellular reactions or on transport processes coupled to substantially lumped intracellular kinetics. This review discusses the development of the models and the directions they provide for a comprehensive model of oscillatory metabolism.

Metabolic isotopomer labeling systems. Part II: structural flux identifiability analysis

Metabolic flux analysis using carbon labeling experiments (CLEs) is an important tool in metabolic engineering where the intracellular fluxes have to be computed from the measured extracellular fluxes and the partially measured distribution of 13C labeling within the intracellular metabolite pools. The relation between unknown fluxes and measurements is described by an isotopomer labeling system (ILS) (see Part I [Math. Biosci. 169 (2001) 173]). Part II deals with the structural flux identifiability of measured ILSs in the steady state. The central question is whether the measured data contains sufficient information to determine the unknown intracellular fluxes. This question has to be decided a priori, i.e. before the CLE is carried out. In structural identifiability analysis the measurements are assumed to be noise-free. A general theory of structural flux identifiability for measured ILSs is presented and several algorithms are developed to solve the identifiability problem. In the particular case of maximal measurement information, a symbolical algorithm is presented that decides the identifiability question by means of linear methods. Several upper bounds of the number of identifiable fluxes are derived, and the influence of the chosen inputs is evaluated. By introducing integer arithmetic this algorithm can even be applied to large networks. For the general case of arbitrary measurement information, identifiability is decided by a local criterion. A new algorithm based on integer arithmetic enables an a priori local identifiability analysis to be performed for networks of arbitrary size. All algorithms have been implemented and flux identifiability is investigated for the network of the central metabolic pathways of a microorganism. Moreover, several small examples are worked out to illustrate the influence of input metabolite labeling and the paradox of information loss due to network simplification.

Metabolomics in systems biology

The primary aim of "omic" technologies is the nontargeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample. By their nature, these technologies reveal unexpected properties of biological systems. A second and more challenging aspect of omic technologies is the refined analysis of quantitative dynamics in biological systems. For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with high sample numbers in reliable measurement times with respect to both technical accuracy and the identification and quantitation of small-molecular-weight metabolites. This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology. The aim of this review is to (a) provide an in-depth overview about metabolomic technology, (b) explore how metabolomic networks can be connected to the underlying reaction pathway structure, and (c) discuss the need to investigate integrative biochemical networks.

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.

Carbohydrate cycling in micro-organisms: what can (13)C-NMR tell us?

The extension of (13)C-nuclear magnetic resonance (NMR) techniques to study cellular metabolism over recent years has provided valuable data supporting the occurrence, diversity and extent of carbon cycling in the carbohydrate metabolism of micro-organisms. The occurrence of such cycles, resulting from the simultaneous operation of different and sometimes opposite individual steps, is inherently related to the network organisation of cellular metabolism. These cycles are tentatively classified here as 'reversibility', 'metabolic' and 'substrate' cycles on the basis of their balance in carbon and cofactors. Current hypotheses concerning the physiological relevance of carbohydrate cycles are discussed in light of the (13)C-NMR data. They most likely represent system-level mechanisms for coherent and timely partitioning of carbon resources to fit with the various biosynthetic, energetic or redox needs of cells and/or additional strategies in the adaptive capacity of micro-organisms to face variation in environmental conditions.

Bio- and chemo-informatics beyond data management: crucial challenges and future opportunities

Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go beyond data- and information-management. Here, we review exemplary areas, such as target identification and validation, virtual screening, and prediction of downstream characteristics of leads, where further research will play a key role in progressing the field.

Engineering secondary metabolite production in plants

Recent achievements have been made in the metabolic engineering of plant secondary metabolism. Various pathways have been altered using genes encoding biosynthetic enzymes or genes encoding regulatory proteins. In addition, antisense genes have been used to block competitive pathways, thereby increasing the flux towards the desired secondary metabolites.