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Hoefnagel, Marcel H. N.; Burgt, Ate van der; Martens, Dirk E.; Hugenholtz, Jeroen; Snoep, Jacky L.

doi:10.1023/a:1020313409954

Cited by 38 publications

(11 citation statements)

References 4 publications

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“…Since PEP is the substrate for the PTS system that takes up glucose, the increase in PEP has been rationalized as a strategy to ensure prompt uptake of glucose once available again. In fact, Hoefnagel et al already made a similar observation in their in-silicon cell model [53]. They also proposed that F16bP activation, but also inorganic phosphate (Pi) effects on PK and regulation of PFK by PEP are important for an increase in PEP and slow depletion of F16bP.…”

Section: Reviewmentioning

confidence: 76%

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Systems biology of lactic acid bacteria: a critical review

2011

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Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism (and forgetting about the enzymes that catalyze them), as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute.There has been a tendency in the Systems Biology community to think that the collection and integration of data should continue ad infinitum, or that we will otherwise not be able to understand the systems that we study in their details. However, it may sometimes be useful to take a step back and consider whether the knowledge that we already have may not explain the system behaviour that we find so intriguing. Reasoning about systems can be difficult, and may require the application of mathematical techniques. The reward is sometimes the realization of unexpected conclusions, or in the worst case, that we still do not know enough details of the parts, or of the interactions between them.We will discuss a number of cases, with a focus on LAB-related work, where a typical systems approach has brought new knowledge or perspective, often counterintuitive, and clashing with conclusions from simpler approaches. Also novel types of testable hypotheses may be generated by the systems approach, which we will illustrate. Finally we will give an outlook on the fields of research where the systems approach may point the way for the near future.

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Section: Reviewmentioning

confidence: 76%

“…Backed up by on experimental evidence Hoefnagel [53] stressed the importance of Pi as a free variable, rather than an input variable used by Voit [57]. However, he did not mention what we think is the most important consequence of considering Pi as a free variable: it renders the total pool of phosphate in the cell as a conserved pool.…”

Section: Reviewmentioning

confidence: 99%

Systems biology of lactic acid bacteria: a critical review

2011

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“…The starting point of our analysis is a stoichiometric representation of the central metabolism of Lactococcus lactis , defined here as the carbon and energy metabolism of this organism that generates most of its free-energy and C3 carbon precursors during fermentative growth. Drawing upon earlier kinetic models [3] , [4] , [7] , [9] , [15] and several available genome-scale reconstructions [5] , [31] , a set of enzymes involved in fermentative metabolism of L. lactis was selected. A graphical overview is shown in Figure 1 .…”

Section: Resultsmentioning

confidence: 99%

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

et al. 2014

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Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.

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“…The Lactococcus lactis glycolytic pathway has been explored experimentally in great detail [26], [27]. The glycolytic pathway converts glucose to lactate through a series of reaction steps.…”

Section: Resultsmentioning

confidence: 99%

A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

et al. 2011

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One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

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Cited by 38 publications

References 4 publications

Systems biology of lactic acid bacteria: a critical review

Systems biology of lactic acid bacteria: a critical review

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

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