Genomics to fluxomics and physiomics - pathway engineering

Recent advances in high-throughput 13C-fluxomics

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT ¹³C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT ¹³C-fluxomics workflows.

Emerging applications of metabolomics in drug discovery and precision medicine

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

White biotechnology: State of the art strategies for the development of biocatalysts for biorefining

White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.

How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells. It encompasses records for more than 500 flux distributions among 36 organisms and includes information regarding the genotype, culture medium, growth conditions and other specific information gathered from hundreds of journal articles. In addition to its comprehensive literature-derived data, the CeCaFDB supports a common text search function among the data and interactive visualization of the curated flux distributions with compartmentation information based on the Cytoscape Web API, which facilitates data interpretation. The CeCaFDB offers four modules to calculate a similarity score or to perform an alignment between the flux distributions. One of the modules was built using an inter programming algorithm for flux distribution alignment that was specifically designed for this study. Based on these modules, the CeCaFDB also supports an extensive flux distribution comparison function among the curated data. The CeCaFDB is strenuously designed to address the broad demands of biochemists, metabolic engineers, systems biologists and members of the -omics community.

A novel platform for automated high-throughput fluxome profiling of metabolic variants

Advances in metabolic engineering are enabling the creation of a large number of cell factories. However, high-throughput platforms do not yet exist for rapidly analyzing the metabolic network of the engineered cells. To fill the gap, we developed an integrated solution for fluxome profiling of large sets of biological systems and conditions. This platform combines a robotic system for (13)C-labelling experiments and sampling of labelled material with NMR-based isotopic fingerprinting and automated data interpretation. As a proof-of-concept, this workflow was applied to discriminate between Escherichia coli mutants with gradual expression of the glucose-6-phosphate dehydrogenase. Metabolic variants were clearly discriminated while pathways that support metabolic flexibility towards modulation of a single enzyme were elucidating. By directly connecting the data flow between cell cultivation and flux quantification, considerable advances in throughput, robustness, release of resources and screening capacity were achieved. This will undoubtedly facilitate the development of efficient cell factories.

Carbon metabolic pathways in phototrophic bacteria and their broader evolutionary implications

Photosynthesis is the biological process that converts solar energy to biomass, bio-products, and biofuel. It is the only major natural solar energy storage mechanism on Earth. To satisfy the increased demand for sustainable energy sources and identify the mechanism of photosynthetic carbon assimilation, which is one of the bottlenecks in photosynthesis, it is essential to understand the process of solar energy storage and associated carbon metabolism in photosynthetic organisms. Researchers have employed physiological studies, microbiological chemistry, enzyme assays, genome sequencing, transcriptomics, and (13)C-based metabolomics/fluxomics to investigate central carbon metabolism and enzymes that operate in phototrophs. In this report, we review diverse CO(2) assimilation pathways, acetate assimilation, carbohydrate catabolism, the tricarboxylic acid cycle and some key, and/or unconventional enzymes in central carbon metabolism of phototrophic microorganisms. We also discuss the reducing equivalent flow during photoautotrophic and photoheterotrophic growth, evolutionary links in the central carbon metabolic network, and correlations between photosynthetic and non-photosynthetic organisms. Considering the metabolic versatility in these fascinating and diverse photosynthetic bacteria, many essential questions in their central carbon metabolism still remain to be addressed.

Dynamic flux responses in riboflavin overproducing Bacillus subtilis to increasing glucose limitation in fed-batch culture

How do intracellular fluxes respond to dynamically increasing glucose limitation when the physiology changes from strong overflow metabolism near to exclusively maintenance metabolism? Here we investigate this question in a typical industrial, glucose-limited fed-batch cultivation with a riboflavin overproducing Bacillus subtilis strain. To resolve dynamic flux changes, a novel approach to (13)C flux analysis was developed that is based on recording (13)C labeling patterns in free intracellular amino acids. Fluxes are then estimated with stationary flux ratio and iterative isotopomer balancing methods, for which a decomposition of the process into quasi-steady states and estimation of isotopic steady state (13)C labeling patterns was necessary. By this approach, we achieve a temporal resolution of 30-60 min that allows us to resolve the slow metabolic transients that typically occur in such cultivations. In the late process phase we found, most prominently, almost exclusive respiratory metabolism, significantly increased pentose phosphate pathway contribution and a strongly decreased futile cycle through the PEP carboxykinase. As a consequence, higher catabolic NADPH formation occurred than was necessary to satisfy the anabolic demands, suggesting a transhydrogenase-like mechanism to close the balance of reducing equivalents.

Systems biology of industrial microorganisms

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data

We present FiD (Fragment iDentificator), a software tool for the structural identification of product ions produced with tandem mass spectrometric measurement of low molecular weight organic compounds. Tandem mass spectrometry (MS/MS) has proven to be an indispensable tool in modern, cell-wide metabolomics and fluxomics studies. In such studies, the structural information of the MS(n) product ions is usually needed in the downstream analysis of the measurement data. The manual identification of the structures of MS(n) product ions is, however, a nontrivial task requiring expertise, and calls for computer assistance. Commercial software tools, such as Mass Frontier and ACD/MS Fragmenter, rely on fragmentation rule databases for the identification of MS(n) product ions. FiD, on the other hand, conducts a combinatorial search over all possible fragmentation paths and outputs a ranked list of alternative structures. This gives the user an advantage in situations where the MS/MS data of compounds with less well-known fragmentation mechanisms are processed. FiD software implements two fragmentation models, the single-step model that ignores intermediate fragmentation states and the multi-step model, which allows for complex fragmentation pathways. The software works for MS/MS data produced both in positive- and negative-ion modes. The software has an easy-to-use graphical interface with built-in visualization capabilities for structures of product ions and fragmentation pathways. In our experiments involving amino acids and sugar-phosphates, often found, e.g., in the central carbon metabolism of yeasts, FiD software correctly predicted the structures of product ions on average in 85% of the cases. The FiD software is free for academic use and is available for download from www.cs.helsinki.fi/group/sysfys/software/fragid.

Comprehensive analysis of the metabolome of Pseudomonas putida S12 grown on different carbon sources

Metabolomics is an emerging, powerful, functional genomics technology that involves the comparative non-targeted analysis of the complete set of metabolites in an organism. We have set-up a robust quantitative metabolomics platform that allows the analysis of 'snapshot' metabolomes. In this study, we have applied this platform for the comprehensive analysis of the metabolite composition of Pseudomonas putida S12 grown on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. This paper focuses on the microbial aspects of analyzing comprehensive metabolomes, and demonstrates that metabolomes can be analyzed reliably. The technical (i.e. sample work-up and analytical) reproducibility was on average 10%, while the biological reproducibility was approximately 40%. Moreover, the energy charge values of the microbial samples generated were determined, and indicated that no biotic or abiotic changes had occurred during sample work-up and analysis. In general, the metabolites present and their concentrations were very similar after growth on the different carbon sources. However, specific metabolites showed large differences in concentration, especially the intermediates involved in the degradation of the carbon sources studied. Principal component discriminant analysis was applied to identify metabolites that are specific for, i.e. not necessarily the metabolites that show those largest differences in concentration, cells grown on either of these four carbon sources. For selected enzymatic reactions, i.e. the glucose-6-phosphate isomerase, triosephosphate isomerase and phosphoglyceromutase reactions, the apparent equilibrium constants (K(app)) were calculated. In several instances a carbon source-dependent deviation between the apparent equilibrium constant (K(app)) and the thermodynamic equilibrium constant (K(eq)) was observed, hinting towards a potential point of metabolic regulation or towards bottlenecks in biosynthesis routes. For glucose-6-phosphate isomerase and phosphoglyceromutase, the K(app) was larger than K(eq), and the results suggested that the specific enzymatic activities of these two enzymes were too low to reach the thermodynamic equilibrium in growing cells. In contrast, with triosephosphate isomerase the K(app) was smaller than K(eq), and the results suggested that this enzyme is kinetically controlled.

Predicting gene essentiality using genome-scale in silico models

Genome-scale metabolic models of organisms can be reconstructed using annotated genome sequence information, well-curated databases, and primary research literature. The metabolic reaction stoichiometry and other physicochemical factors are incorporated into the model, thus imposing constraints that represent restrictions on phenotypic behavior. Based on this premise, the theoretical capabilities of the metabolic network can be assessed by using a mathematical technique known as flux balance analysis (FBA). This modeling framework, also known as the constraint-based reconstruction and analysis approach, differs from other modeling strategies because it does not attempt to predict exact network behavior. Instead, this approach uses known constraints to separate the states that a system can achieve from those that it cannot. In recent years, this strategy has been employed to probe the metabolic capabilities of a number of organisms, to generate and test experimental hypotheses, and to predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the constraint-based modeling approach and focuses on its application to computationally predicting gene essentiality.

Gaining insight into microbial physiology in the large intestine: a special role for stable isotopes

The importance of the human large intestine for nutrition, health, and disease, is becoming increasingly realized. There are numerous indications of a distinct role for the gut in such important issues as immune disorders and obesity-linked diseases. Research on this long-neglected organ, which is colonized by a myriad of bacteria, is a rapidly growing field that is currently providing fascinating new insights into the processes going on in the colon, and their relevance for the human host. This review aims to give an overview of studies dealing with the physiology of the intestinal microbiota as it functions within and in interaction with the host, with a special focus on approaches involving stable isotopes. We have included general aspects of gut microbial life as well as aspects specifically relating to genomic, proteomic, and metabolomic studies. A special emphasis is further laid on reviewing relevant methods and applications of stable isotope-aided metabolic flux analysis (MFA). We argue that linking MFA with the '-omics' technologies using innovative modeling approaches is the way to go to establish a truly integrative and interdisciplinary approach. Systems biology thus actualized will provide key insights into the metabolic regulations involved in microbe-host mutualism and their relevance for health and disease.

Identification of flux regulation coefficients from elementary flux modes: A systems biology tool for analysis of metabolic networks

Within a metabolic network, the elementary flux modes enables a unique description of different operations of the network. Thus, the metabolic fluxes can be specified as convex combinations of the elementary flux modes. Here, we describe an approach to identify the set of elementary flux modes that operates in a given metabolic network through the use of measurements of macroscopic fluxes, that is, fluxes in and out of the cell. Besides enabling estimation of the metabolic fluxes, the parameters of the linear combinations of the elementary flux modes provide valuable physiological information; we call these parameters flux regulation coefficients (FRCs). These coefficients indicate which enzyme subsets are important at different growth conditions. We demonstrate how FRCs can be used to map the operation of the metabolic network of the yeast Saccharomyces sp. under different growth conditions.

Towards dynamic metabolic network measurements by multi-dimensional NMR-based fluxomics

Novel technologies for measuring biological systems and methods for visualizing data have led to a revolution in the life sciences. Nuclear magnetic resonance (NMR) techniques can provide information on metabolite structure and metabolic dynamics at the atomic level. We have been developing a new method for measuring the dynamic metabolic network of crude extracts that combines [(13)C(6)]glucose stable isotope labeling of Arabidopsis thaliana and multi-dimensional heteronuclear NMR analysis, whereas most conventional metabolic flux analyses examine proteinogenic amino acids that are specifically labeled with partially labeled substrates such as [2-(13)C(1)]glucose or 10% [(13)C(6)]glucose. To show the validity of our method, we investigated how to obtain information about biochemical reactions, C-C bond formation, and the cleavage of the main metabolites, such as free amino acids, in crude extracts based on the analysis of the (13)C-(13)C coupling pattern in 2D-NMR spectra. For example, the combination of different extraction solvents allows one to distinguish complicated (13)C-(13)C fine couplings at the C2 position of amino acids. As another approach, f1-f3 projection of the HCACO spectrum also helps in the analysis of (13)C-(13)C connectivities. Using these new methods, we present an example that involves monitoring the incorporation profile of [(13)C(6)]glucose into A. thaliana and its metabolic dynamics, which change in a time-dependent manner with atmospheric (12)CO(2) assimilation.

Introduction and Perspectives

Classically, metabolism was investigated by studying molecular characteristics of enzymes and their regulators in isolation. This reductionistic approach successfully established mechanistic relationships with the immediate interacting neighbors and allowed reconstruction of network structures. Severely underdeveloped was the ability to make precise predictions about the integrated operation of pathways and networks that emerged from the typically nonlinear and complex interactions of proteins and metabolites. The burden of metabolic engineering is a consequence of this fact-one cannot yet predict with any certainty precisely what needs to be engineered to produce more complex phenotypes. What was and still is missing are concepts, methods, and algorithms to integrate data and information into a quantitatively coherent whole, as well as theoretical concepts to reliably predict the consequence of environmental stimuli or genetic interventions. This introduction and perspective to Domain 3, Metabolism and Metabolic Fluxes, starts with a brief overview of the panoply of global measurement technologies that herald the dawning of systems biology and whose impact on metabolic research is apparent throughout the Domain 3. In the middle section, applications to Escherichia coli are used to illustrate general concepts and successes of computational methods that approach metabolism as a network of interacting elements, and thus have potential to fill the gap in quantitative data and information integration. The final section highlights prospective focus areas for future metabolic research, including functional genomics, eludication of evolutionary principles, and the integration of metabolism with regulatory networks.

Metabolic networks in motion: 13C-based flux analysis

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

Monitoring the health to disease continuum with global metabolic profiling and systems biology

Global metabolic profiling, which includes both metabolomics and metabonomics studies, is the latest ‘omics’ research platform that is being applied to understand the health and disease continuum. Metabolic profiling analyses have been demonstrated for the investigation of inborn errors of metabolism, organ transplant rejection, drug toxicity, disease diagnosis and prognosis, drug efficacy and nutritional status. Combining information generated from a metabolic profiling platform with that obtained based on genetics, transcriptomics and proteomics research paradigms will pave the way for a better understanding of the mechanisms of disease and toxicity. Metabolomics and nutrition will lay the groundwork for the application of personalized medicine in the 21st century.

Characterizing phenotype with tracer based metabolomics

In the post-genomic era, a pressing challenge to biological scientists is to understand the organization of gene functions, the interaction between gene and nutrient environment, and the genesis of phenotypes. Metabolomics, the quantitation of low molecular weight compounds, has been used to provide a phenotypic description of a cell or tissue by a set of metabolites. Gene function is hypothesized from its correlation with the corresponding set of macromolecules by transcriptomics or proteomics. Another approach to genotype-phenotype correlation is by the reconstruction of genome-scale metabolic maps. The utilization of specific pathways as predicted by reaction network analysis provides the phenotypic characterization of a cell, which can be plotted on a phenotypic phase plane. Tracer based metabolomics is the experimental approach to reaction network analysis using stable isotope tracers. The redistribution of the isotope tracer among metabolic intermediates is used to identify a finite number of pathways, the utilization of which is characteristic of the phenotypic behavior of cells. In this paper, we review tracer based metabolomic methods for the construction of phenotypic phase plane plots, and discuss the functional implications of phenotypic phase plane analysis. Examples of phenotypic changes in response to differentiation, inhibition of signaling pathways and perturbation in nutrient environment are provided.

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.

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets

Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on 'educated guesses' and 'gut feeling'. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues.

Compiling a molecular inventory for Mycobacterium bovis BCG at two growth rates: evidence for growth rate-mediated regulation of ribosome biosynthesis and lipid metabolism

An experimental system of Mycobacterium tuberculosis growth in a carbon-limited chemostat has been established by the use of Mycobacterium bovis BCG as a model organism. For this model, carbon-limited chemostats with low concentrations of glycerol were used to simulate possible growth rates during different stages of tuberculosis. A doubling time of 23 h (D = 0.03 h(-1)) was adopted to represent cells during the acute phase of infection, whereas a lower dilution rate equivalent to a doubling time of 69 h (D = 0.01 h(-1)) was used to model mycobacterial persistence. This chemostat model allowed the specific response of the mycobacterial cell to carbon limitation at different growth rates to be elucidated. The macromolecular (RNA, DNA, carbohydrate, and lipid) and elemental (C, H, and N) compositions of the biomass were determined for steady-state cultures, revealing that carbohydrates and lipids comprised more than half of the dry mass of the BCG cell, with only a quarter of the dry weight consisting of protein and RNA. Consistent with studies of other bacteria, the specific growth rate impacts on the macromolecular content of BCG and the proportions of lipid, RNA, and protein increased significantly with the growth rate. The correlation of RNA content with the growth rate indicates that ribosome production in carbon-limited M. bovis BCG cells is subject to growth rate-dependent control. The results also clearly show that the proportion of lipids in the mycobacterial cell is very sensitive to changes in the growth rate, probably reflecting changes in the amounts of storage lipids. Finally, this study demonstrates the utility of the chemostat model of mycobacterial growth for functional genomic, physiology, and systems biology studies.

Aluminum triggers decreased aconitase activity via Fe-S cluster disruption and the overexpression of isocitrate dehydrogenase and isocitrate lyase: a metabolic network mediating cellular survival

Although aluminum is known to be toxic to most organisms, its precise biochemical interactions are not fully understood. In the present study, we demonstrate that aluminum promotes the inhibition of aconitase (Acn) activity via the perturbation of the Fe-S cluster in Pseudomonas fluorescens. Despite the significant decrease in citrate isomerization activity, cellular survival is assured by the overexpression of isocitrate lyase and isocitrate dehydrogenase (IDH)-NADP+. 13C NMR spectroscopic studies, Blue Native PAGE, and Western blot analyses indicated that although the decrease in Acn activity is concomitant with the increase of aluminum in the culture, the amount of Acn expressed is not sensitive to the concentration of the trivalent metal. A 6-fold decrease in Acn activity and no discernable change in protein content in aluminum-stressed cultures were observed. The addition of Fe(NH4)2(SO4)2 in a reducing environment led to a significant recovery in Acn activity. This enzymatic activity reverted to normal levels when aluminum-stressed cells were transferred to either a control or an iron-supplemented medium. The overexpression of the two isocitrate-metabolizing enzymes isocitrate lyase and IDH-NADP+ appears to mitigate the deficit in Acn activity. The levels of these enzymes are dependent on the aluminum content of the culture and appear to be under transcriptional control. Hence, the regulation of the enzymes involved in the homeostasis of isocitrate constitutes a pivotal component of the global metabolic strategy that ensures the survival of this organism in an aluminum citrate environment.

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.

Reconstruction of microbial transcriptional regulatory networks

Although metabolic networks can be readily reconstructed through comparative genomics, the reconstruction of regulatory networks has been hindered by the relatively low level of evolutionary conservation of their molecular components. Recent developments in experimental techniques have allowed the generation of vast amounts of data related to regulatory networks. This data together with literature-derived knowledge has opened the way for genome-scale reconstruction of transcriptional regulatory networks. Large-scale regulatory network reconstructions can be converted to in silico models that allow systematic analysis of network behavior in response to changes in environmental conditions. These models can further be combined with genome-scale metabolic models to build integrated models of cellular function including both metabolism and its regulation.

Impact of ‘ome’ analyses on inverse metabolic engineering

Genome-wide or large-scale methodologies employed in functional genomics such as DNA sequencing, transcription profiling, proteomics, and metabolite profiling have become important tools in many metabolic engineering strategies. These techniques allow the identification of genetic differences and insight into their cellular effects. In the field of inverse metabolic engineering mapping of differences between strains with different degree of a certain desired phenotype and subsequent identification of factors conferring that phenotype are an essential part. Therefore, the tools of functional genomics in particular have the potential to promote and expand inverse metabolic engineering. Here, we review the use of functional genomics methods in inverse metabolic engineering, examples are presented, and we discuss the identification of targets for metabolic engineering with low fold changes using these techniques.

Engineering metabolic highways in Lactococci and other lactic acid bacteria

Lactic acid bacteria (LAB) are widely used in industrial food fermentations and are receiving increased attention for use as cell factories for the production of food and pharmaceutical products. Glycolytic conversion of sugars into lactic acid is the main metabolic highway in these Gram-positive bacteria and Lactococcus lactis has become the model organism because of its small genome, genetic accessibility and simple metabolism. Here we discuss the metabolic engineering of L. lactis and the value of metabolic models compared with other LAB, with a particular focus on the food-grade production of metabolites involved in flavour, texture and health.

Biochemical engineering: cues from cells

Engineering principles are used in the exploitation of biocatalysts derived from cells. The purity of reagents, catalysts and maintenance of operation variables are extremely important for bioengineering systems. Any change in the purity of reagents or in operation variables usually leads to a dramatic decrease in productivity. Cellular systems, however, are able to work with relatively high impure conditions and increase their productivity in response to external signals. Thus the seemingly disordered 'bag of juice' or cytoplasm has more order and much higher order of integration than first appears. Learning the semantics of this paradoxical ability of order and integration would help bioengineers to understand and enhance productivity even using impure reagents.

Metabolic networks of microbial systems

In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors.

Advancing drug discovery through systems biology

Pharmaceutical companies are facing an urgent need to both increase their lead compound and clinical candidate portfolios and satisfy market demands for continued innovation and revenue growth. Here, we outline an emerging approach that attempts to facilitate and alleviate many of the current drug discovery issues and problems. This is, in part, achieved through the systematic integration of technologies, which results in a superior output of data and information, thereby enhancing our understanding of biological function, chemico-biological interactions and, ultimately, drug discovery. Systems biology is one new discipline that is positioned to significantly impact this process.