Bacterial Adaptation through Distributed Sensing of Metabolic Fluxes

Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Molecular Systems Biology (Impact Factor: 10.87). 09/2010; 6(9):355. DOI: 10.1038/msb.2010.10
Source: PubMed


The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.

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Available from: Judith B Zaugg, Sep 30, 2015
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    • "Additionally, enzyme kinetics and performance of nonnative genes/proteins are currently not covered in GEMs. Although the first genome-scale and reduced constraint-based models that take regulation into account have emerged for E. coli (Kotte et al. 2010; Covert et al. 2001) they have so far only been created for certain regulatory systems present in yeast (Christensen et al. 2009). Although the lack of regulatory predicting power in GEMs hinders the quality of predictions for metabolic engineering purposes, the combination of modeling data with real experiments can precisely identify the regions which are highly regulated at post-transcriptional level and allows one to investigate those regions more thoroughly using bottom-up approaches (described below). "
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    ABSTRACT: Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology, often, the aim is to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression, however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally non-existent, but well characterized components into a living organism. To describe these systems kinetic models are often used, and to integrate these systems with the standard metabolic engineering approach it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.This article is protected by copyright. All rights reserved.
    FEMS Yeast Research 08/2014; DOI:10.1111/1567-1364.12199 · 2.82 Impact Factor
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    • "With the concept of ‘sytems biology’ coming to the stage of biological research, construction of kinetic models has been the primary focus in a substantial number of studies [1-4]. Kinetic models are mechanistic representations of biological systems. "
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    ABSTRACT: Background Kinetic models can present mechanistic descriptions of molecular processes within a cell. They can be used to predict the dynamics of metabolite production, signal transduction or transcription of genes. Although there has been tremendous effort in constructing kinetic models for different biological systems, not much effort has been put into their validation. In this study, we introduce the concept of resampling methods for the analysis of kinetic models and present a statistical model invalidation approach. Results We based our invalidation approach on the evaluation of a kinetic model’s predictive power through cross validation and forecast analysis. As a reference point for this evaluation, we used the predictive power of an unsupervised data analysis method which does not make use of any biochemical knowledge, namely Smooth Principal Components Analysis (SPCA) on the same test sets. Through a simulations study, we showed that too simple mechanistic descriptions can be invalidated by using our SPCA-based comparative approach until high amount of noise exists in the experimental data. We also applied our approach on an eicosanoid production model developed for human and concluded that the model could not be invalidated using the available data despite its simplicity in the formulation of the reaction kinetics. Furthermore, we analysed the high osmolarity glycerol (HOG) pathway in yeast to question the validity of an existing model as another realistic demonstration of our method. Conclusions With this study, we have successfully presented the potential of two resampling methods, cross validation and forecast analysis in the analysis of kinetic models’ validity. Our approach is easy to grasp and to implement, applicable to any ordinary differential equation (ODE) type biological model and does not suffer from any computational difficulties which seems to be a common problem for approaches that have been proposed for similar purposes. Matlab files needed for invalidation using SPCA cross validation and our toy model in SBML format are provided at
    BMC Systems Biology 05/2014; 8(1):61. DOI:10.1186/1752-0509-8-61 · 2.44 Impact Factor
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    • "Modeling behavior like these requires a kinetic approach and is beyond the scope of GEMs. One interesting example of how kinetic modeling has provided insight into the emergence of complex behaviors is the model of metabolic adaption in E. coli (Kotte et al., 2010). Here, a kinetic formulation of the reactions of the central metabolism, including their transcriptional and translational regulation, was shown to be capable of reproducing system-level metabolic adjustments through a mechanism termed distributed sensing of intracellular metabolic fluxes. "
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    ABSTRACT: An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.
    Metabolic Engineering 04/2014; 24. DOI:10.1016/j.ymben.2014.03.007 · 6.77 Impact Factor
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