From genomes to in silico cells via metabolic networks

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

Metabolic engineering and transcriptomic analysis of Saccharomyces cerevisiae producing p-coumaric acid from xylose

BACKGROUND

Aromatic amino acids and their derivatives are valuable chemicals and are precursors for different industrially compounds. p-Coumaric acid is the main building block for complex secondary metabolites in commercial demand, such as flavonoids and polyphenols. Industrial scale production of this compound from yeast however remains challenging.

RESULTS

Using metabolic engineering and a systems biology approach, we developed a Saccharomyces cerevisiae platform strain able to produce 242 mg/L of p-coumaric acid from xylose. The same strain produced only 5.35 mg/L when cultivated with glucose as carbon source. To characterise this platform strain further, transcriptomic analysis was performed, comparing this strain's growth on xylose and glucose, revealing a strong up-regulation of the glyoxylate pathway alongside increased cell wall biosynthesis and unexpectedly a decrease in aromatic amino acid gene expression when xylose was used as carbon source.

CONCLUSIONS

The resulting S. cerevisiae strain represents a promising platform host for future production of p-coumaric using xylose as a carbon source.

Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

Wine microbiome: A dynamic world of microbial interactions

Most fermented products are generated by a mixture of microbes. These microbial consortia perform various biological activities responsible for the nutritional, hygienic, and aromatic qualities of the product. Wine is no exception. Substantial yeast and bacterial biodiversity is observed on grapes, and in both must and wine. The diverse microorganisms present interact throughout the winemaking process. The interactions modulate the hygienic and sensorial properties of the wine. Many studies have been conducted to elucidate the nature of these interactions, with the aim of establishing better control of the two fermentations occurring during wine processing. However, wine is a very complex medium making such studies difficult. In this review, we present the current state of research on microbial interactions in wines. We consider the different kinds of interactions between different microorganisms together with the consequences of these interactions. We underline the major challenges to obtaining a better understanding of how microbes interact. Finally, strategies and methodologies that may help unravel microbe interactions in wine are suggested.

Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit

Modelling of metabolic networks is a powerful tool to analyse the behaviour of developing plant organs, including fruits. Guided by our current understanding of heterotrophic metabolism of plant cells, a medium-scale stoichiometric model, including the balance of co-factors and energy, was constructed in order to describe metabolic shifts that occur through the nine sequential stages of Solanum lycopersicum (tomato) fruit development. The measured concentrations of the main biomass components and the accumulated metabolites in the pericarp, determined at each stage, were fitted in order to calculate, by derivation, the corresponding external fluxes. They were used as constraints to solve the model by minimizing the internal fluxes. The distribution of the calculated fluxes of central metabolism were then analysed and compared with known metabolic behaviours. For instance, the partition of the main metabolic pathways (glycolysis, pentose phosphate pathway, etc.) was relevant throughout fruit development. We also predicted a valid import of carbon and nitrogen by the fruit, as well as a consistent CO2 release. Interestingly, the energetic balance indicates that excess ATP is dissipated just before the onset of ripening, supporting the concept of the climacteric crisis. Finally, the apparent contradiction between calculated fluxes with low values compared with measured enzyme capacities suggest a complex reprogramming of the metabolic machinery during fruit development. With a powerful set of experimental data and an accurate definition of the metabolic system, this work provides important insight into the metabolic and physiological requirements of the developing tomato fruits.

Advances in metabolic engineering of yeast Saccharomyces cerevisiae for production of chemicals

Yeast Saccharomyces cerevisiae is an important industrial host for production of enzymes, pharmaceutical and nutraceutical ingredients and recently also commodity chemicals and biofuels. Here, we review the advances in modeling and synthetic biology tools and how these tools can speed up the development of yeast cell factories. We also present an overview of metabolic engineering strategies for developing yeast strains for production of polymer monomers: lactic, succinic, and cis,cis-muconic acids. S. cerevisiae has already firmly established itself as a cell factory in industrial biotechnology and the advances in yeast strain engineering will stimulate development of novel yeast-based processes for chemicals production.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

Construction of a genome-scale kinetic model of mycobacterium tuberculosis using generic rate equations

The study of biological systems at the genome scale helps us understand fundamental biological processes that govern the activity of living organisms and regulate their interactions with the environment. Genome-scale metabolic models are usually analysed using constraint-based methods, since detailed rate equations and kinetic parameters are often missing. However, constraint-based analysis is limited in capturing the dynamics of cellular processes. In this paper, we present an approach to build a genome-scale kinetic model of Mycobacterium tuberculosis metabolism using generic rate equations. M. tuberculosis causes tuberculosis which remains one of the largest killer infectious diseases. Using a genetic algorithm, we estimated kinetic parameters for a genome-scale metabolic model of M. tuberculosis based on flux distributions derived from Flux Balance Analysis. Our results show that an excellent agreement with flux values is obtained under several growth conditions, although kinetic parameters may vary in different conditions. Parameter variability analysis indicates that a high degree of redundancy remains present in model parameters, which suggests that the integration of other types of high-throughput datasets will enable the development of better constrained models accounting for a variety of in vivo phenotypes.

Mass spectrometry-based microbial metabolomics

Through the characterization of metabolic pathways, metabolomics is able to illuminate the activities of a cell at the functional level. However, the metabolome, which is comprised of hundreds of chemically diverse metabolites, is rather difficult to monitor. Mass spectrometry (MS)-based profiling methods are increasingly being utilized for this purpose. To this end, MS is often coupled to the separation techniques gas chromatography (GC), liquid chromatography (LC), and capillary electrophoresis (CE). The mass-based selectivity that the MS provides, together with the chromatographic or electrophoretic separation of analytes, creates hyphenated techniques that are ideally suited to the measurement of large numbers of metabolites from microbial extracts. In this chapter, we describe GC-MS, LC-MS, and CE-MS methods that are applicable to microbial metabolomics experiments.

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

Metabolic systems analysis to advance algal biotechnology

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Network analysis can direct microbial development efforts towards successful strategies and enable quantitative fine-tuning of the network for optimal product yields while maintaining the robustness of the production microbe. Metabolic modeling yields insights into microbial function, guides experiments by generating testable hypotheses, and enables the refinement of knowledge on the specific organism. While the application of such analytical approaches to algal systems is limited to date, metabolic network analysis can improve understanding of algal metabolic systems and play an important role in expediting the adoption of new biofuel technologies.

A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions

Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by (13)C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.

Systems biology of lipid metabolism: from yeast to human

Lipid metabolism is highly relevant as it plays a central role in a number of human diseases. Due to the highly interactive structure of lipid metabolism and its regulation, it is necessary to apply a holistic approach, and systems biology is therefore well suited for integrated analysis of lipid metabolism. In this paper it is demonstrated that the yeast Saccharomyces cerevisiae serves as an excellent model organism for studying the regulation of lipid metabolism in eukaryotes as most of the regulatory structures in this part of the metabolism are conserved between yeast and mammals. Hereby yeast systems biology can assist to improve our understanding of how lipid metabolism is regulated.

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.

Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns

Elementary modes represent a valuable concept in the analysis of metabolic reaction networks. However, they can only be computed in medium-size systems, preventing application to genome-scale metabolic models. In consequence, the analysis is usually constrained to a specific part of the known metabolism, and the remaining system is modeled using abstractions like exchange fluxes and external species. As we show by the analysis of a model of the central metabolism of Escherichia coli that has been previously analyzed using elementary modes, the choice of these abstractions heavily impacts the pathways that are detected, and the results are biased by the knowledge of the metabolic capabilities of the network by the user. In order to circumvent these problems, we introduce the concept of elementary flux patterns, which explicitly takes into account possible steady-state fluxes through a genome-scale metabolic network when analyzing pathways through a subsystem. By being similar to elementary mode analysis, our concept now allows for the application of many elementary-mode-based tools to genome-scale metabolic networks. We present an algorithm to compute elementary flux patterns and analyze a model of the tricarboxylic acid cycle and adjacent reactions in E. coli. Thus, we detect several pathways that can be used as alternative routes to some central metabolic pathways. Finally, we give an outlook on further applications like the computation of minimal media, the development of knockout strategies, and the analysis of combined genome-scale networks.

Reconstruction of biochemical networks in microorganisms

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Metabolomics and fluxomics approaches

Systems biology is based on the understanding that the behaviour of the whole is greater than would be expected from the sum of its parts. Thus the ultimate goal of systems biology is to predict the behaviour of the whole system on the basis of the list of components involved. Recent advances in ‘-omics’ technologies and the development of new computational techniques and algorithms have greatly contributed to progress in this field of biology. Among the main ‘-omics’ technologies, metabolomics is expected to play a significant role in bridging the phenotype–genotype gap, since it amplifies changes in the proteome and provides a better representation of the phenotype of an organism than other methods. However, knowledge of the complete set of metabolites is not enough to predict the phenotype, especially for higher cells in which the distinct metabolic processes involved in their production and degradation are finely regulated and interconnected. In these cases, quantitative knowledge of intracellular fluxes is required for a comprehensive characterization of metabolic networks and their functional operation. These intracellular fluxes cannot be detected directly, but can be estimated through interpretation of stable isotope patterns in metabolites. Moreover, analysis of these fluxes by means of metabolic control theories offers a potentially unifying, holistic paradigm to explain the regulation of cell metabolism. In this chapter, we provide an overview of metabolomics and fluxomics, highlighting stable isotope strategies for fluxome characterization. We also discuss some of the tools used to quantitatively analyse the control exerted by components of the network over both the metabolome and the fluxome. Finally, we outline the role and future of metabolomics and fluxomics in drug discovery.

Proteome-wide characterization of sugarbeet seed vigor and its tissue specific expression

Proteomic analysis of mature sugarbeet seeds led to the identification of 759 proteins and their specific tissue expression in root, cotyledons, and perisperm. In particular, the proteome of the perispermic storage tissue found in many seeds of the Caryophyllales is described here. The data allowed us to reconstruct in detail the metabolism of the seeds toward recapitulating facets of seed development and provided insights into complex behaviors such as germination. The seed appears to be well prepared to mobilize the major classes of reserves (the proteins, triglycerides, phytate, and starch) during germination, indicating that the preparation of the seed for germination is mainly achieved during its maturation on the mother plant. Furthermore, the data revealed several pathways that can contribute to seed vigor, an important agronomic trait defined as the potential to produce vigorous seedlings, such as glycine betaine accumulation in seeds. This study also identified several proteins that, to our knowledge, have not previously been described in seeds. For example, the data revealed that the sugarbeet seed can initiate translation either through the traditional cap-dependent mechanism or by a cap-independent process. The study of the tissue specificity of the seed proteome demonstrated a compartmentalization of metabolic activity between the roots, cotyledons, and perisperm, indicating a division of metabolic tasks between the various tissues. Furthermore, the perisperm, although it is known as a dead tissue, appears to be very active biochemically, playing multiple roles in distributing sugars and various metabolites to other tissues of the embryo.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

Background

Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae.

Results

Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources.

Conclusion

A much enhanced annotation of the A. oryzae genome was performed and a genome-scale metabolic model of A. oryzae was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of A. oryzae physiology.

Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major

Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.

Getting to grips with the plant metabolic network

Research into plant metabolism has a long history, and analytical approaches of ever-increasing breadth and sophistication have been brought to bear. We now have access to vast repositories of data concerning enzymology and regulatory features of enzymes, as well as large-scale datasets containing profiling information of transcripts, protein and metabolite levels. Nevertheless, despite this wealth of data, we remain some way off from being able to rationally engineer plant metabolism or even to predict metabolic responses. Within the past 18 months, rapid progress has been made, with several highly informative plant network interrogations being discussed in the literature. In the present review we will appraise the current state of the art regarding plant metabolic network analysis and attempt to outline what the necessary steps are in order to further our understanding of network regulation.

Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

Background

In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.

Results

To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.

Conclusion

The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.

Enabling technologies: fermentation and downstream processing

Efficient parallel tools for bioprocess design, consequent application of the concepts for metabolic process analysis as well as innovative downstream processing techniques are enabling technologies for new industrial bioprocesses from an engineering point of view. Basic principles, state-of-the-art techniques and cutting-edge technologies are briefly reviewed. Emphasis is on parallel bioreactors for bioprocess design, biochemical systems characterization and metabolic control analysis, as well as on preparative chromatography, affinity filtration and protein crystallization on a process scale.

Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra- and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.

Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans

BACKGROUND

Aspergillus nidulans (the asexual form of Emericella nidulans) is a model organism for aspergilli, which are an important group of filamentous fungi that encompasses human and plant pathogens as well as industrial cell factories. Aspergilli have a highly diversified metabolism and, because of their medical, agricultural and biotechnological importance, it would be valuable to have an understanding of how their metabolism is regulated. We therefore conducted a genome-wide transcription analysis of A. nidulans grown on three different carbon sources (glucose, glycerol, and ethanol) with the objective of identifying global regulatory structures. Furthermore, we reconstructed the complete metabolic network of this organism, which resulted in linking 666 genes to metabolic functions, as well as assigning metabolic roles to 472 genes that were previously uncharacterized.

RESULTS

Through combination of the reconstructed metabolic network and the transcription data, we identified subnetwork structures that pointed to coordinated regulation of genes that are involved in many different parts of the metabolism. Thus, for a shift from glucose to ethanol, we identified coordinated regulation of the complete pathway for oxidation of ethanol, as well as upregulation of gluconeogenesis and downregulation of glycolysis and the pentose phosphate pathway. Furthermore, on change in carbon source from glucose to ethanol, the cells shift from using the pentose phosphate pathway as the major source of NADPH (nicotinamide adenine dinucleotide phosphatase, reduced form) for biosynthesis to use of the malic enzyme.

CONCLUSION

Our analysis indicates that some of the genes are regulated by common transcription factors, making it possible to establish new putative links between known transcription factors and genes through clustering.

Inverse drug screens: a rapid and inexpensive method for implicating molecular targets

Identification of gene products that function in some specific process of interest is a common goal in developmental biology. Although use of drug compounds to probe biological systems has a very long history in teratology and toxicology, systematic hierarchical drug screening has not been capitalized upon by the developmental biology community. This "chemical genetics" approach can greatly benefit the study of embryonic and regenerative systems, and we have formalized a strategy for using known pharmacological compounds to implicate specific molecular candidates in any chosen biological phenomenon. Taking advantage of a hierarchical structure that can be imposed on drug reagents in a number of fields such as ion transport, neurotransmitter function, metabolism, and cytoskeleton, any assay can be carried out as a binary search algorithm. This inverse drug screen methodology is much more efficient than exhaustive testing of large numbers of drugs, and reveals the identity of a manageable number of specific molecular candidates that can then be validated and targeted using more expensive and specific molecular reagents. Here, we describe the process of this loss-of-function screen and illustrate its use in uncovering novel bioelectrical and serotonergic mechanisms in embryonic patterning. This technique is an inexpensive and rapid complement to existing molecular screening strategies. Moreover, it is applicable to maternal proteins, and model species in which traditional genetic screens are not feasible, significantly extending the opportunities to identify key endogenous players in biological processes.

Chaperones as parts of cellular networks

The most important interactions between cellular molecules have a high affinity, are unique and specific, and require a network approach for a detailed description. Molecular chaperones usually have many first and second neighbors in protein-protein interaction networks and they play a prominent role in signaling and transcriptional regulatory networks of the cell. Chaperones may uncouple protein, signaling, membranous, organellar and transcriptional networks during stress, which gives an additional protection for the cell at the network-level. Recent advances uncovered that chaperones act as genetic buffers stabilizing the phenotype of various cells and organisms. This chaperone effect on the emergent properties of cellular networks may be generalized to proteins having a specific, central position and low affinity, weak links in protein networks. Cellular networks are preferentially remodeled in various diseases and aging, which may help us to design novel therapeutic and anti-aging strategies.

Elucidating the mode-of-action of compounds from metabolite profiling studies

Metabolite profiling has been carried out for decades and is as such not a new research area. However, the field has attracted increasing attention in the last couple of years, and the term metabolome is now often used to describe the complete pool of metabolites associated with an organism at any given time. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the best candidates for comprehensive analysis of the metabolome and the application of these technologies is presented in this chapter. In this relation, the importance of efficient metabolite screening for discovery of novel drugs is discussed. Related to metabolite profiling, the principals underlying the application of labeled substrates to quantify in vivo metabolic fluxes are introduced, and the chapter is concluded by discussing the perspectives of metabolite measurements in systems biology.

Aging cellular networks: Chaperones as major participants

We increasingly rely on the network approach to understand the complexity of cellular functions. Chaperones (heat shock proteins) are key "networkers", which sequester and repair damaged proteins. In order to link the network approach and chaperones with the aging process, we first summarize the properties of aging networks suggesting a "weak link theory of aging". This theory suggests that age-related random damage primarily affects the overwhelming majority of the low affinity, transient interactions (weak links) in cellular networks leading to increased noise, destabilization and diversity. These processes may be further amplified by age-specific network remodelling and by the sequestration of weakly linked cellular proteins to protein aggregates of aging cells. Chaperones are weakly linked hubs (i.e., network elements with a large number of connections) and inter-modular bridge elements of protein-protein interaction, signalling and mitochondrial networks. As aging proceeds, the increased overload of damaged proteins is an especially important element contributing to cellular disintegration and destabilization. Additionally, chaperone overload may contribute to the increase of "noise" in aging cells, which leads to an increased stochastic resonance resulting in a deficient discrimination between signals and noise. Chaperone- and other multi-target therapies, which restore the missing weak links in aging cellular networks, may emerge as important anti-aging interventions.

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.

Systems interface biology

The field of systems biology has attracted the attention of biologists, engineers, mathematicians, physicists, chemists and others in an endeavour to create systems-level understanding of complex biological networks. In particular, systems engineering methods are finding unique opportunities in characterizing the rich behaviour exhibited by biological systems. In the same manner, these new classes of biological problems are motivating novel developments in theoretical systems approaches. Hence, the interface between systems and biology is of mutual benefit to both disciplines.

Integration of metabolome data with metabolic networks reveals reporter reactions

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.

Systems biology of SNPs

Genome-scale networks can now be reconstructed based on high-throughput data sets. Mathematical analyses of these networks are used to compute their candidate functional or phenotypic states. Analysis of functional states of networks shows that the activity of biochemical reactions can be highly correlated in physiological states, forming so-called co-sets representing functional modules of the network. Thus, detrimental sequence defects in any one of the genes encoding members of a co-set can result in similar phenotypic consequences. Here we show that causal single nucleotide polymorphisms in genes encoding mitochondrial components can be classified and correlated using co-sets.

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.

Cellular networks and the aging process

The most important interactions between cellular molecules have a high affinity, are unique and specific, and require a network approach for a detailed description. After a brief introduction to cellular networks (protein--protein interaction networks, metabolic networks, gene regulatory networks, signalling networks and membrane--organelle networks) an overview is given on the network aspects of the theories on aging. The most important part of the review summarizes our knowledge on the aging of networks. The effects of aging on the general network models are described, as well as the initial findings on the effects of aging on the cellular networks. Finally we suggest a 'weak link theory of aging' linking the random damage of the network constituents to the overwhelming majority of the low affinity, transient interactions (weak links) in the cellular networks. We show that random damage of weak links may lead to an increase of noise and an increased vulnerability of cellular networks, and make a comparison between these predictions and the observed behaviour of the emergent properties of cellular networks in aged organisms.