Showing papers by "Bas Teusink published in 2012"

FAME, the Flux Analysis and Modeling Environment

Abstract: Background: The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists. Results: The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at http://f-a-m-e.org/. Conclusions: With FAME, we present the community with an open source, user-friendly, web-based “one stop shop” for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike.

Metabolic shifts: a fitness perspective for microbial cell factories

Abstract: Performance of industrial microorganisms as cell factories is limited by the capacity to channel nutrients to desired products, of which optimal production usually requires careful manipulation of process conditions, or strain improvement. The focus in process improvement is often on understanding and manipulating the regulation of metabolism. Nonetheless, one encounters situations where organisms are remarkably resilient to further optimization or their properties become unstable. Therefore it is important to understand the origin of these apparent limitations to find whether and how they can be improved. We argue that by considering fitness effects of regulation, a more generic explanation for certain behaviour can be obtained. In this view, apparent process limitations arise from trade-offs that cells faced as they evolved to improve fitness. A deeper understanding of such trade-offs using a systems biology approach can ultimately enhance performance of cell factories.

Role of phosphate in the central metabolism of two lactic acid bacteria – a comparative systems biology approach

Abstract: 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.

Standardized assay medium to measure Lactococcus lactis enzyme activities while mimicking intracellular conditions.

Abstract: Knowledge of how the activity of enzymes is affected under in vivo conditions is essential for analyzing their regulation and constructing models that yield an integrated understanding of cell behavior. Current kinetic parameters for Lactococcus lactis are scattered through different studies and performed under different assay conditions. Furthermore, assay conditions often diverge from conditions prevailing in the intracellular environment. To establish uniform assay conditions that resemble intracellular conditions, we analyzed the intracellular composition of anaerobic glucose-limited chemostat cultures of L. lactis subsp. cremoris MG 1363. Based on this, we designed a new assay medium for enzyme activity measurements of growing cells of L. lactis, mimicking as closely as practically possible its intracellular environment. Procedures were optimized to be carried out in 96-well plates, and the reproducibility and dynamic range were checked for all enzyme activity measurements. The effects of freezing and the carryover of ammonium sulfate from the addition of coupling enzymes were also established. Activities of all 10 glycolytic and 4 fermentative enzymes were measured. Remarkably, most in vivo-like activities were lower than previously published data. Yet, the ratios of Vmax over measured in vivo fluxes were above 1. With this work, we have developed and extensively validated standard protocols for enzyme activity measurements for L. lactis.

A Specific Mutation in the Promoter Region of the Silent cel Cluster Accounts for the Appearance of Lactose-Utilizing Lactococcus lactis MG1363

Abstract: 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.

Optimality Principles in the Regulation of Metabolic Networks

Abstract: 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.

Understanding Regulation of Metabolism through Feasibility Analysis

Abstract: Understanding cellular regulation of metabolism is a major challenge in systems biology. Thus far, the main assumption was that enzyme levels are key regulators in metabolic networks. However, regulation analysis recently showed that metabolism is rarely controlled via enzyme levels only, but through non-obvious combinations of hierarchical (gene and enzyme levels) and metabolic regulation (mass action and allosteric interaction). Quantitative analyses relating changes in metabolic fluxes to changes in transcript or protein levels have revealed a remarkable lack of understanding of the regulation of these networks. We study metabolic regulation via feasibility analysis (FA). Inspired by the constraint-based approach of Flux Balance Analysis, FA incorporates a model describing kinetic interactions between molecules. We enlarge the portfolio of objectives for the cell by defining three main physiologically relevant objectives for the cell: function, robustness and temporal responsiveness. We postulate that the cell assumes one or a combination of these objectives and search for enzyme levels necessary to achieve this. We call the subspace of feasible enzyme levels the feasible enzyme space. Once this space is constructed, we can study how different objectives may (if possible) be combined, or evaluate the conditions at which the cells are faced with a trade-off among those. We apply FA to the experimental scenario of long-term carbon limited chemostat cultivation of yeast cells, studying how metabolism evolves optimally. Cells employ a mixed strategy composed of increasing enzyme levels for glucose uptake and hexokinase and decreasing levels of the remaining enzymes. This trade-off renders the cells specialized in this low-carbon flux state to compete for the available glucose and get rid of over-overcapacity. Overall, we show that FA is a powerful tool for systems biologists to study regulation of metabolism, interpret experimental data and evaluate hypotheses.

A specific mutation in the promoter region of the silent cel

Abstract: Lactococcus lactis MG1363 3 Ana Solopova, Herwig Bachmann, Bas Teusink , Jan Kok, Ana Rute Neves and 4 Oscar P. Kuipers . 5 6 Department of Molecular Genetics, University of Groningen, Groningen Biomolecular 7 Sciences and Biotechnology Institute, Nijenborgh 7, 9747 AG Groningen, the 8 Netherlands. 9 Vrije Universiteit Amsterdam, Faculty of Earth and Life Sciences (FALW), Systems 10 Bioinformatics IBIVU / NISB, De Boelelaan 1085, 1081 HV Amsterdam, the 11 Netherlands. 12 Instituto de Tecnologia Quimica e Biologica, Universidade Nova de Lisboa, Av. da 13 Republica, 2780-157 Oeiras, Portugal. 14 Kluyver Centre for Genomics of Industrial Fermentation, Groningen/Delft, the 15 Netherlands. 16

Inferring differences in the distribution of reaction rates across conditions

Abstract: 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.