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Microbial growth can be characterized by a limited set of macroscopic parameters such as growth rate, biomass yield and substrate affinity. Different culturing protocols for laboratory evolution have been developed to select mutant strains that have one specific macroscopic growth parameter improved. Some of those mutant strains display tradeoffs between growth parameters and changed metabolic strategies, for example, a shift from respiration to fermentation. Here we discuss recent studies suggesting that metabolic strategies and growth parameter tradeoffs originate from a common set of physicochemical and cellular constraints, associated with the allocation of intracellular resources over biosynthetic processes, mostly protein synthesis. This knowledge will give insight in ecological and biological concepts and can be used for metabolic and evolutionary engineering strategies.

Fluorescent proteins (FPs) are widely used in many organisms, but are commonly characterised in vitro. However, the in vitro properties may poorly reflect in vivo performance. Therefore, we characterised 27 FPs in vivo using Saccharomyces cerevisiae as model organism. We linked the FPs via a T2A peptide to a control FP, producing equimolar expression of the 2 FPs from 1 plasmid. Using this strategy, we characterised the FPs for brightness, photostability, photochromicity and pH-sensitivity, achieving a comprehensive in vivo characterisation. Many FPs showed different in vivo properties compared to existing in vitro data. Additionally, various FPs were photochromic, which affects readouts due to complex bleaching kinetics. Finally, we codon optimized the best performing FPs for optimal expression in yeast, and found that codon-optimization alters FP characteristics. These FPs improve experimental signal readout, opening new experimental possibilities. Our results may guide future studies in yeast that employ fluorescent proteins.

Since the 1970s, galactose metabolism in Lactococcus lactis has been in debate. Different studies led to diverse outcomes making it difficult to conclude whether galactose uptake was PEP- or ATP- dependent and decide what the exact connection was between galactose and lactose uptake and metabolism. It was shown that some Lactococcus strains possess two galactose-specific systems - a permease and a PTS, even if they lack the lactose utilization plasmid, proving that a lactose-independent PTSGal exists. However, the PTSGal transporter was never identified. Here, with the help of transcriptome analyses and genetic knock-out mutants, we reveal the identities of two low-affinity galactose PTSs. A novel plant-niche-related PTS component Llmg_0963 forming a hybrid transporter Llmg_0963PtcBA and a glucose/mannose-specific PTS are shown to be involved in galactose transport in L. lactis MG1363.

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection.Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

[EN] A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment., This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R.

Quantifying the rate of consumption and release of metabolites (i.e., flux profiling) has become integral to the study of cancer. The fluxes as well as the growth of the cells may be affected by metabolite depletion during cultivation.

Many organisms have several similar transporters with different affinities for the same substrate. Typically, high-affinity transporters are expressed when substrate is scarce and low-affinity ones when it is abundant. The benefit of using low instead of high-affinity transporters remains unclear, especially when additional nutrient sensors are present. Here, we investigate two hypotheses. It was previously hypothesized that there is a trade-off between the affinity and the catalytic efficiency of transporters, and we find some but no definitive support for it. Additionally, we propose that for uptake by facilitated diffusion, at saturating substrate concentrations, lowering the affinity enhances the net uptake rate by reducing substrate efflux. As a consequence, there exists an optimal, external-substrate-concentration dependent transporter affinity. A computational model of Saccharomyces cerevisiae glycolysis shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis with yeast strains expressing a single glucose transporter modified to have either a high or a low affinity. However, due to the intimate link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive. Still, our theoretical results provide a novel reason for the presence of low-affinity transport systems.

Protein investment costs are considered a major driver for the choice of alternative metabolic strategies. We tested this premise in Lactococcus lactis, a bacterium that exhibits a distinct, anaerobic version of the bacterial Crabtree/Warburg effect; with increasing growth rates it shifts from a high yield metabolic mode [mixed-acid fermentation; 3 adenosine triphosphate (ATP) per glucose] to a low yield metabolic mode (homolactic fermentation; 2 ATP per glucose). We studied growth rate-dependent relative transcription and protein ratios, enzyme activities, and fluxes of L. lactis in glucose-limited chemostats, providing a high-quality and comprehensive data set. A three- to fourfold higher growth rate rerouted metabolism from acetate to lactate as the main fermentation product. However, we observed hardly any changes in transcription, protein levels and enzyme activities. Even levels of ribosomal proteins, constituting a major investment in cellular machinery, changed only slightly. Thus, contrary to the original hypothesis, central metabolism in this organism appears to be hardly regulated at the level of gene expression, but rather at the metabolic level. We conclude that L. lactis is either poorly adapted to growth at low and constant glucose concentrations, or that protein costs play a less important role in fitness than hitherto assumed.

Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. Several advantages of using binding proteins in such uptake systems have been proposed. However, a systematic, rigorous and quantitative analysis of the function of binding proteins is lacking. By combining knowledge of selection pressure and physiochemical constraints, we derive kinetic, thermodynamic, and stoichiometric properties of binding-protein dependent transport systems that enable a maximal import activity per amount of transporter. Under the hypothesis that this maximal specific activity of the transport complex is the selection objective, binding protein concentrations should exceed the concentration of both the scarce nutrient and the transporter. This increases the encounter rate of transporter with loaded binding protein at low substrate concentrations, thereby enhancing the affinity and specific uptake rate. These predictions are experimentally testable, and a number of observations confirm them. Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. We combine knowledge of selection pressure and physiochemical constraints with mathematical modeling to elucidate the benefit these binding proteins confer. We show that, when the binding protein concentration exceeds both the nutrient and the transporter concentrations, they increase the substrate-transporter encounter rate and thereby the nutrient uptake rate.

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l -glutamine and l -glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters. Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

Microbial starter cultures are essential for consistent product quality and functional properties such as flavor, texture, pH or the alcohol content of various fermented foods. Strain improvement programs to achieve desired properties in starter cultures are diverse, but developments in next-generation sequencing lead to an increased interest in evolutionary engineering of desired phenotypes. We here discuss recent developments of strain selection protocols and how computational approaches can assist such experimental design. Furthermore the analysis of evolved phenotypes and possibilities with complex consortia are highlighted. Studies carried out with mainly yeast and lactic acid bacteria demonstrate the power of evolutionary engineering to deliver strains with novel phenotypes as well as insight into underlying mechanisms.

Maximization of growth rate is an important fitness strategy for bacteria. Bacteria can achieve this by expressing proteins at optimal concentrations, such that resources are not wasted. This is exemplified for Escherichia coli by the increase of its ribosomal protein-fraction with growth rate, which precisely matches the increased protein synthesis demand. These findings and others have led to the hypothesis that E. coli aims to maximize its growth rate in environments that support growth. However, what kind of regulatory strategy is required for a robust, optimal adjustment of the ribosome concentration to the prevailing condition is still an open question. In the present study, we analyze the ppGpp-controlled mechanism of ribosome expression used by E. coli and show that this mechanism maintains the ribosomes saturated with its substrates. In this manner, overexpression of the highly abundant ribosomal proteins is prevented, and limited resources can be redirected to the synthesis of other growth-promoting enzymes. It turns out that the kinetic conditions for robust, optimal protein-partitioning, which are required for growth rate maximization across conditions, can be achieved with basic biochemical interactions. We show that inactive ribosomes are the most suitable 'signal' for tracking the intracellular nutritional state and for adjusting gene expression accordingly, as small deviations from optimal ribosome concentration cause a huge fractional change in ribosome inactivity. We expect to find this control logic implemented across fast-growing microbial species because growth rate maximization is a common selective pressure, ribosomes are typically highly abundant and thus costly, and the required control can be implemented by a small, simple network.

Cells require membrane-located transporter proteins to import nutrients from the environment. Many organisms have several similar transporters for the same nutrient, which differ in their affinity. Typically, high affinity transporters are expressed when substrate is scarce and low affinity ones when substrate is more abundant. The benefit of using low affinity transporters when high affinity ones are available has so far remained unclear. Here, we investigate two hypotheses. First, it was previously hypothesized that a trade-off between the affinity and the maximal catalytic rate explains this phenomenon. We find some theoretical and experimental support for this hypothesis, but no conclusive evidence. Secondly, we propose a new hypothesis: for uptake by facilitated diffusion, at saturating extracellular substrate concentrations, lowering the affinity enhances the net uptake rate by reducing the substrate efflux rate. As a consequence, there exists an optimal, external substrate concentration dependent transporter affinity. Anin silicoanalysis of glycolysis inSaccharomyces cerevisiaeshows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis using yeast strains expressing a single glucose transporter that was modified to have either a high or a low affinity. Due to the intimate and reciprocal link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive in our hands. Still, our theoretical results provide a novel reason for the presence of low affinity transport systems which might have more general implications for enzyme catalyzed conversions.

Whooping cough is a highly contagious respiratory disease caused by Bordetella pertussis. Despite widespread vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm, and experimental data to probe the full metabolic potential of this pathogen, using B. pertussis strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine, using inorganic sulfur sources, such as thiosulfate, and it can grow on organic acids, such as citrate or lactate, as sole carbon sources, providing in vivo demonstration that its tricarboxylic acid (TCA) cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three strains were shown to grow on substrate combinations requiring a functional TCA cycle, but only one strain could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with > 2-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology and highlights the potential, but also the limitations, of models based solely on metabolic gene content., SCOPUS: ar.j, info:eu-repo/semantics/published

In microorganisms such as bacteria or yeasts, metabolic rates are tightly coupled to growth rate, and therefore to fitness. Although the topology of central pathways are largely conserved across organisms, the enzyme kinetics and their parameters generally vary. This prevents us to understand and predict (changes in) metabolic dynamics. The analytical treatment of metabolic pathways is generally restricted to small models, containing maybe two to four equations. Since such small core models involve much coarse graining, their biological interpretation is often hampered. In this paper we aim to bridge the gap between analytical, more in-depth treatment of small core models and biologically more realistic and detailed models by developing new methods. We illustrate these methods for a model of glycolysis in Saccharo-myces cerevisiae yeast, arguably the best characterised metabolic pathway in the literature. The model is more involved than in previous studies, and involves both ATP/ADP and NADH/NAD householding.A detailed analysis of the steady state equations sheds new light on two recently studied biological phenomena in yeast glycolysis: whether it is to be expected that fructose-1,6-biphosphate (FBP) parameterises all steady states, and the occurrence of bistability between a regular steady state and imbalanced steady state in which glycolytic intermediates keep accumulating.This work shows that the special structure of metabolic pathways does allow for more in-depth bifurcation analyses than is currently the norm. We especially emphasise which of the techniques developed here scale to larger pathways, and which do not.

A central theme in biology is to understand the molecular basis of fitness: which strategies succeed under which conditions; how are they mechanistically implemented; and which constraints shape trade-offs between alternative strategies. We approached these questions with parallel bacterial evolution experiments in chemostats. Chemostats provide a constant environment with a defined resource limitation (glucose), in which the growth rate can be controlled. UsingLactococcus lactis, we found a single mutation in a global regulator of carbon metabolism, CcpA, to confer predictable fitness improvements across multiple growth rates.In silicoprotein structural analysis complemented with biochemical and phenotypic assays, show that the mutation reprograms the CcpA regulon, specifically targeting transporters. This supports that membrane occupancy, rather than biosynthetic capacity, is the dominant constraint for the observed fitness enhancement. It also demonstrates that cells can modulate a pleiotropic regulator to work around limiting constraints.

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi-layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the coherence of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free-energy carriers, and precursors for biosynthesis and stress-related compounds. Here we will review the regulation of multi-tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components.

Next-generation sequencing experiments have shown that microRNAs (miRNAs) are expressed in many different isoforms (isomiRs), whose biological relevance is often unclear. We found that mature miR-21, the most widely researched miRNA because of its importance in human disease, is produced in two prevalent isomiR forms that differ by 1 nt at their 3' end, and moreover that the 3' end of miR-21 is posttranscriptionally adenylated by the noncanonical poly(A) polymerase PAPD5. PAPD5 knockdown caused an increase in the miR-21 expression level, suggesting that PAPD5-mediated adenylation of miR-21 leads to its degradation. Exoribonuclease knockdown experiments followed by small-RNA sequencing suggested that PARN degrades miR-21 in the 3'-to-5' direction. In accordance with this model, microarray expression profiling demonstrated that PAPD5 knockdown results in a down-regulation of miR-21 target mRNAs. We found that disruption of the miR-21 adenylation and degradation pathway is a general feature in tumors across a wide range of tissues, as evidenced by data from The Cancer Genome Atlas, as well as in the noncancerous proliferative disease psoriasis. We conclude that PAPD5 and PARN mediate degradation of oncogenic miRNA miR-21 through a tailing and trimming process, and that this pathway is disrupted in cancer and other proliferative diseases.

Glycolysis is the central pathway in energy metabolism in the majority of organisms. In a recent paper, van Heerden et al. [1] showed experimentally and computationally that glycolysis can exist in two states, a global steady state and a so-called imbalanced state. In the imbalanced state, intermediary metabolites accumulate at low levels of ATP and inorganic phosphate. It was shown that Baker's yeast uses a peculiar regulatory mechanism-via trehalose metabolism-to ensure that most yeast cells reach the steady state and not the imbalanced state. Results: Here we explore the apparent bistable behaviour in a core model of glycolysis that is based on a well-established detailed model, and study in great detail the bifurcation behaviour of solutions, without using any numerical information on parameter values. Conclusion: We uncover a rich suite of solutions, including so-called imbalanced states, bistability, and oscillatory behaviour. The techniques employed are generic, directly suitable for a wide class of biochemical pathways, and could lead to better analytical treatments of more detailed models. © 2014 Elsevier Inc.

Evolutionary adaptations in metabolic networks are fundamental to evolution of microbial growth. Studies on unneeded-protein synthesis indicate reductions in fitness upon nonfunctional protein synthesis, showing that cell growth is limited by constraints acting on cellular protein content. Here, we present a theory for optimal metabolic enzyme activity when cells are selected for maximal growth rate given such growth-limiting biochemical constraints. We show how optimal enzyme levels can be understood to result from an enzyme benefit minus cost optimization. The constraints we consider originate from different biochemical aspects of microbial growth, such as competition for limiting amounts of ribosomes or RNA polymerases, or limitations in available energy. Enzyme benefit is related to its kinetics and its importance for fitness, while enzyme cost expresses to what extent resource consumption reduces fitness through constraint-induced reductions of other enzyme levels. A metabolic fitness landscape is introduced to define the fitness potential of an enzyme. This concept is related to the selection coefficient of the enzyme and can be expressed in terms of its fitness benefit and cost.

We review the uses and limitations of modelling approaches that are in use in the field of Lactic Acid Bacteria (LAB). We describe recent developments in model construction and computational methods, starting from application of such models to monocultures. However, since most applications in food biotechnology involve complex nutrient environments and mixed cultures, we extend the scope to discuss developments in modelling such complex systems. With metagenomics and meta-functional genomics data becoming available, the developments in genome-scale community models are discussed. We conclude that exploratory tools are available and useful, but truly predictive mechanistic models will remain a major challenge in the field. © 2012 Elsevier Ltd.

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development., npj Systems Biology and Applications, 2, ISSN:2056-7189

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets.

Protein expression is shaped by evolutionary processes that tune microbial fitness. The limited biosynthetic capacity of a cell constrains protein expression and forces the cell to carefully manage its protein economy. In a chemostat, the physiology of the cell feeds back on the growth conditions, hindering intuitive understanding of how changes in protein concentration affect fitness. Here, we aim to provide a theoretical framework that addresses the selective pressures and optimal evolutionary-strategies in the chemostat. We show that the optimal enzyme levels are the result of a trade-off between the cost of their production and the benefit of their catalytic function. We also show that deviations from optimal enzyme levels are directly related to selection coefficients. The maximal fitness strategy for an organism in the chemostat is to express a well-defined metabolic subsystem known as an elementary flux mode. Using a coarse-grained, kinetic model of Saccharomyces cerevisiae’s metabolism and growth, we illustrate that the dynamics and outcome of evolution in a chemostat can be very counter-intuitive: Strictly-respiring and strictly-fermenting strains can evolve from a common ancestor. This work provides a theoretical framework that relates a kinetic, mechanistic view on metabolism with cellular physiology and evolutionary dynamics in the chemostat.

Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has been made towards mapping and modelling species-level metabolism, elucidating the metabolic exchanges between microorganisms and steering the community dynamics remain an enormous scientific challenge. In view of the complexity, computational models of microbial communities are essential to obtain systems-level understanding of ecosystem functioning. This review discusses the applications and limitations of constraint-based stoichiometric modelling tools, and in particular flux balance analysis (FBA). We explain this approach from first principles and identify the challenges one faces when extending it to communities, and discuss the approaches used in the field in view of these challenges. We distinguish between steady-state and dynamic FBA approaches extended to communities. We conclude that much progress has been made, but many of the challenges are still open.

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

Lactic acid-producing bacteria survive in distinct environments, but show common metabolic characteristics. Here we studied the dynamic interactions of the central metabolism in Lactococcus lactis, extensively used as a starter culture in the dairy industry, and Streptococcus pyogenes, a human pathogen. Glucose-pulse experiments and enzymatic measurements were performed to parameterize kinetic models of glycolysis. Significant improvements were made to existing kinetic models for L. lactis, which subsequently accelerated the development of the first kinetic model of S. pyogenes glycolysis. The models revealed an important role for extracellular phosphate in the regulation of central metabolism and the efficient use of glucose. Thus, phosphate, which is rarely taken into account as an independent species in models of central metabolism, should be considered more thoroughly in the analysis of metabolic systems in the future. Insufficient phosphate supply can lead to a strong inhibition of glycolysis at high glucose concentrations in both species, but this was more severe in S. pyogenes. S. pyogenes is more efficient at converting glucose to ATP, showing a higher tendency towards heterofermentative energy metabolism than L. lactis. Our comparative systems biology approach revealed that the glycolysis of L. lactis and S. pyogenes have similar characteristics, but are adapted to their individual natural habitats with respect to phosphate regulation. Database The mathematical models described here have been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/Levering/index.html free of charge.

The Lactococcus lactis laboratory strain MG1363 has been described to be unable to utilize lactose. However, in a rich medium supplemented with lactose as the sole carbon source, it starts to grow after prolonged incubation periods. Transcriptome analyses showed that L. lactis MG1363 Lac + cells expressed celB , encoding a putative cellobiose-specific phosphotransferase system (PTS) IIC component, which is normally silent in MG1363 Lac − cells. Nucleotide sequence analysis of the cel cluster of a Lac + isolate revealed a change from one of the guanines to adenine in the promoter region. We showed here that one particular mutation, taking place at increased frequency, accounts for the lactose-utilizing phenotype occurring in MG1363 cultures. The G-to-A transition creates a −10 element at an optimal distance from the −35 element. Thus, a fully active promoter is created, allowing transcription of the otherwise cryptic cluster. Nuclear magnetic resonance (NMR) spectroscopy results show that MG1363 Lac + uses a novel pathway of lactose utilization.

Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms. With the use of a small in silico hypothetical network, based on only the topology and directionality of a known pathway, several regulation scenarios can be formulated. Confronting these scenarios with experimental data results in a short list of possible pathway regulation mechanisms associated with the reversal of correlations between conditions. This procedure allows for the formulation of regulation scenarios without detailed prior knowledge of kinetics and for the inference of reaction rate changes without rate information. The method was applied to experimental time-resolved metabolomics data from multiple short-term perturbation-response experiments in S. cerevisiae across aerobic and anaerobic conditions. The method's output was validated against a detailed kinetic model of glycolysis in S. cerevisiae, which showed that the method can indeed infer the correct regulation scenario. © 2012 The Royal Society of Chemistry.

Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these ‘lessons from glycolysis’, we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.

Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such networkbased rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident. © 2010 John Wiley & Sons, Inc.

Aim: A medium with minimal requirements for the growth of Lactobacillus plantarum WCFS was developed. The composition of the minimal medium was compared to a genome-scale metabolic model of L. plantarum. Methods and Results: By repetitive single omission experiments, two minimal media were developed: PMM5 (true minimal medium) and PMM7 [a pseudominimal medium, supporting proper biomass formation of 350 mg l−1 dry weight (DW)]. The specific growth rate of L. plantarum on PMM7 was found to be 50% and 63% lower when compared to growth on established growth media (chemically defined medium and MRS, respectively). Using a genome-scale metabolic model of L. plantarum, it was predicted that PMM5 and PMM7 would not support the growth of L. plantarum. This is because the biosynthesis of para-aminobenzoic acid (pABA) was predicted to be essential for growth. The discrepancy in simulated growth and experimental growth on PMM7 was further investigated for pABA; a molecule which plays an important role in folate production. The growth performance and folate production were determined on PMM7 in the presence and absence of pABA. It was found that a 12 000-fold reduction in folate pools exerted no influence on formation of biomass or growth rate of L. plantarum cultures when grown in the absence of pABA. Conclusion: Largely reduced folate production pools do not have an effect on the growth of L. plantarum, showing that L. plantarum makes folate in a large excess. Significance and Impact of the study: These experiments illustrate the importance of combining genome-scale metabolic models with growth experiments on minimal media.

With the availability of genome sequences of many organisms and information about gene-protein-reaction (GPR) associations with respect to these organisms genome-scale metabolic networks can be reconstructed. In cellular systems biology these networks are used to model the behavior of metabolism in context of cell growth in terms of fluxes (reaction rates) through reactions in the network. Because the flux through each reaction can generally vary within a range, many flux distributions of the entire network are possible. However, since reactions are connected by common metabolites, reactions that are functionally coherent, are expected to highly correlate in terms of their flux value over different flux distributions. In this paper the genome-scale network of a lactic acid bacterium, named Lactococcus lactis MG1363, is used to generate flux distributions for multiple in silico environmental conditions, mimicking laboratory growth conditions. The flux distributions per condition are used to calculate a correlation matrix for each condition. Subsequently the correlations between the reactions are analyzed in a multivariate approach across the in silico environmental conditions in order to identify correlations that are invariant (i.e. independent of the environment) and correlations that are variant across conditions (i.e. dependent of the environment). The applied multivariate methods are Parallel Factor Analysis (PARAFAC) and Principal Component Analysis (PCA). The discussion of the results of both methods leads to the question whether latent variable models are suitable analyzing this type of data. Copyright © 2009 John Wiley & Sons, Ltd.

There has been considerable recent interest in deciphering the adaptive properties underlying the structure and function of metabolic networks. Various features of metabolic networks such as the global topology, distribution of fluxes, and mutational robustness, have been proposed to have adaptive significance and hence reflect design principles. However, whether evolutionary processes alternative to direct selection on the trait under investigation also play a role is often ignored and the selection pressures maintaining a given metabolic trait often remain speculative. Some systems-level traits might simply arise as by-products of selection on other traits or even through random genetic drift. Here, we ask which systems-level aspects of metabolism are likely to have adaptive utility and which could be better explained as by-products of other evolutionary forces. We conclude that the global topological characteristics of metabolic networks and their mutational robustness are unlikely to be directly shaped by natural selection. Conversely, models of optimal design revealed that various aspects of individual pathways and the behavior of the whole network show signs of adaptations, even though the exact selective forces often remain elusive. Comparative and experimental approaches, which so far have been relatively rarely employed, could help to distinguish between alternative adaptive scenarios. © HFSP Publishing.

Most fermented dairy products are manufactured using mixed starter cultures. These cultures are usually a well-balanced mixture of several different lactic acid bacteria. For the development of novel or improved dairy products, flavour characteristics are generally a primary target. In recent years, a number of high-throughput screening tools have emerged allowing the targeted selection of flavour-forming cultures and these will be illustrated with relevant examples. This includes methods directly targeting flavour production such as GC–MS analysis of flavour profiles on the head space of fermentation fluids. High-throughput tools and DNA microarrays are valuable tools for elucidating the regulatory responses to different substrates and processing conditions thus allowing rational intervention in fermentations to improve flavour production. Finally, genome-scale metabolic models are used to predict the production of relevant (flavour) components and to expand our knowledge about flavour-forming pathways. The integration of these approaches is anticipated to strengthen culture development programs enabling the production of well-tasting, fermented dairy products.

Obese humans are often insulin- and leptin resistant. Since leptin can affect glucose metabolism, it is conceivable that a lack of leptin signal transduction contributes to insulin resistance. It remains unclear whether leptin affects glucose metabolism via peripheral and/or central mechanistic routes. In the present study, we aimed: (i) to determine the relative contributions of lack of leptin signal transduction and adiposity to insulin resistance and (ii) to establish the impact of central leptin action on glucose metabolism. To address the first point, ob/ob mice were subjected to severe calorie restriction, so that their body weight became similar to that of wild-type mice. Insulin sensitivity was measured in obese ob/ob, lean (food restricted) ob/ob and lean, weight-matched wild-type mice. To address the second point, leptin (or vehicle) was i.c.v. infused to the lateral cerebral ventricle of ob/ob mice and insulin sensitivity was determined. Hyperinsulinaemic euglyceamic clamps were used to quantify insulin sensitivity. Food restriction barely affected body composition, although it profoundly curtailed body weight. Insulin suppressed hepatic glucose production (HGP) to a greater extent in lean ob/ob than in obese ob/ob mice, but its impact remained considerably less than in wild-type mice (% suppression: 11.8 +/- 8.9 versus 1.3 +/- 1.1 versus 56.6 +/- 13.0%/nmol, for lean, obese ob/ob and wild-type mice, respectively; P < 0.05). The insulin-mediated glucose disposal (GD) of lean ob/ob mice was also in between that of obese ob/ob and wild-type mice (37.5 +/- 21.4 versus 25.1 +/- 14.6 versus 59.6 +/- 17.3 mumol/min/kg/nmol of insulin, respectively; P < 0.05 wild-type versus obese ob/ob mice). Leptin infusion acutely enhanced both hepatic insulin sensitivity (insulin-induced inhibition of HGP) and insulin-mediated GD (9.1 +/- 2.4 versus 5.0 +/- 2.7%/nmol of insulin, and 25.6 +/- 5.6 versus 13.6 +/- 4.8 mumol/min/kg/nmol of insulin, respectively; P < 0.05 for both comparisons) in ob/ob mice. Both a lack of leptin signals and adiposity may contribute to insulin resistance in obese individuals. Diminution of central leptin signalling can critically affect glucose metabolism in these individuals.

High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism., Author Summary Organisms depend on huge networks of molecular reactions for environmental sensing, information integration, gene expression, and metabolism. The discovery of general principles of network behavior is a major ambition of systems biology and of great interest to biotechnology and medicine. We present a computational tool that calculates all optimal states of metabolism in terms of pathways, which is arguably the most intuitive and preferred approach to characterize whole-cell metabolism. We show how the space of all feasible flux distributions can be compactly described in terms of a unique set of minimal and feasible pathways, given realistic stoichiometric, thermodynamic, and optimization-objective constraints. This description clarifies the interplay between flux constraints and optimization objectives. We explain why some fluxes are variable and cross-correlate within the solution space while others do not and how multi-objective optimization shrinks the solution space. We illustrate our findings with a toy metabolic model to explain the main insights and apply it to a genome-scale stoichiometric model of Escherichia coli metabolism.

Protein investment costs are considered a major driver for the choice of alternative metabolic strategies. We tested this premise in Lactococcus lactis, a bacterium that exhibits a distinct, anaerobic version of the bacterial Crabtree/Warburg effect; with increasing growth rates it shifts from a high yield metabolic mode [mixed-acid fermentation; 3 adenosine triphosphate (ATP) per glucose] to a low yield metabolic mode (homolactic fermentation; 2 ATP per glucose). We studied growth rate-dependent relative transcription and protein ratios, enzyme activities, and fluxes of L. lactis in glucose-limited chemostats, providing a high-quality and comprehensive data set. A three- to fourfold higher growth rate rerouted metabolism from acetate to lactate as the main fermentation product. However, we observed hardly any changes in transcription, protein levels and enzyme activities. Even levels of ribosomal proteins, constituting a major investment in cellular machinery, changed only slightly. Thus, contrary to the original hypothesis, central metabolism in this organism appears to be hardly regulated at the level of gene expression, but rather at the metabolic level. We conclude that L. lactis is either poorly adapted to growth at low and constant glucose concentrations, or that protein costs play a less important role in fitness than hitherto assumed.

Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

Background The lactic acid bacterium Lactobacillus rhamnosus GG is the most studied probiotic bacterium with proven health benefits upon oral intake, including the alleviation of diarrhea. The mission of the Yoba for Life foundation is to provide impoverished communities in Africa increased access to Lactobacillus rhamnosus GG under the name Lactobacillus rhamnosus yoba 2012, world’s first generic probiotic strain. We have been able to overcome the strain’s limitations to grow in food matrices like milk, by formulating a dried starter consortium with Streptococcus thermophilus that enables the propagation of both strains in milk and other food matrices. The affordable seed culture is used by people in resource-poor communities. Results We used S. thermophilus C106 as an adjuvant culture for the propagation of L. rhamnosus yoba 2012 in a variety of fermented foods up to concentrations, because of its endogenous proteolytic activity, ability to degrade lactose and other synergistic effects. Subsequently, L. rhamnosus could reach final titers of 1E+09 CFU ml−1, which is sufficient to comply with the recommended daily dose for probiotics. The specific metabolic interactions between the two strains were derived from the full genome sequences of L. rhamnosus GG and S. thermophilus C106. The piliation of the L. rhamnosus yoba 2012, required for epithelial adhesion and inflammatory signaling in the human host, was stable during growth in milk for two rounds of fermentation. Sachets prepared with the two strains, yoba 2012 and C106, retained viability for at least 2 years. Conclusions A stable dried seed culture has been developed which facilitates local and low-cost production of a wide range of fermented foods that subsequently act as delivery vehicles for beneficial bacteria to communities in east Africa. Electronic supplementary material The online version of this article (doi:10.1186/s12934-015-0370-x) contains supplementary material, which is available to authorized users.

Contains fulltext : 35606teusink.pdf (Publisher’s version ) (Closed access) 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.

Contains fulltext : 36088teusink.pdf (Publisher’s version ) (Open Access) A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.

Hepatic VLDL and glucose production is enhanced in type 2 diabetes and associated with hepatic steatosis. Whether the derangements in hepatic metabolism are attributable to steatosis or to the increased availability of FA metabolites is not known. We used methyl palmoxirate (MP), an inhibitor of carnitine palmitoyl transferase I, to acutely inhibit hepatic FA oxidation and investigated whether the FAs were rerouted into VLDL secretion and whether this would affect hepatic glucose production. After an overnight fast, male APOE3*Leiden transgenic mice received an oral dose of 10 mg/kg MP. Administration of MP led to an 83% reduction in plasma β-hydroxybutyrate (ketone body) levels compared with vehicle-treated mice (0.47 ± 0.07 vs. 2.81 ± 0.16 mmol/l, respectively; P < 0.01), indicative of impaired ketogenesis. Plasma FFA levels were increased by 32% and cholesterol and insulin levels were decreased by 17% and 50%, respectively, in MP-treated mice compared with controls. MP treatment led to a 30% increase in liver triglyceride (TG) content. Surprisingly, no effect on hepatic VLDL-TG production was observed between the groups at 8 h after MP administration. In addition, the capacity of insulin to suppress endogenous glucose production was unaffected in MP-treated mice compared with controls. In conclusion, acute inhibition of FA oxidation increases hepatic lipid content but does not stimulate hepatic VLDL secretion or reduce insulin sensitivity. Copyright © 2005 by the American Society for Biochemistry and Molecular Biology, Inc. Chemicals / CAS: 3 hydroxybutyrate dehydrogenase, 9028-38-0; cholesterol, 57-88-5; insulin, 9004-10-8; palmoxiric acid, 68170-97-8, 81556-15-2; apolipoprotein E3 (Leidein); Apolipoprotein E3; Apolipoproteins E; Fatty Acids, Nonesterified; Lipoproteins, VLDL; RNA, Messenger

The biotechnology industry continuously explores new ways to improve the performance of microbial strains in fermentation processes. Recent focus has been on new genome-wide modelling approaches in functional genomics, which aim to take full advantage of genome sequence data, transcription profiling, proteomics and metabolite profiling for strain improvement. The integration of global metabolic models with genetic and regulatory models will be essential for the practice of metabolic engineering for strain improvement to move forward, simply because we cannot rely on our intuition to grasp the complexity of the biological systems involved.

The prospect of understanding the relationship between the genome and the physiology of an organism is an important incentive to reconstruct metabolic networks. The first steps in the process can be automated and it does not take much effort to obtain an initial metabolic reconstruction from a genome sequence. However, such a reconstruction is certainly not flawless and correction of the many imperfections is laborious. It requires the combined analysis of the available information on protein sequence, phylogeny, gene-context and co-occurrence but is also aided by high-throughput experimental data. Simultaneously, the reconstructed network provides the opportunity to visualize the "omics" data within a relevant biological functional context and thus aids the interpretation of those data.