3,567 research outputs found
The bug in the machine. Control analysis at constant input flux rather that at constant substrate concentration
No full textModelling pyruvate distribution in Lactococcus lactis: a kinetic model to support metabolic engineering strategies.
No full textControl and metabolic dynamics: How the frequency of glycolytic oscillations in Saccharomyces cerevisiae is controlled by glucose transport.
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Hierarchical control of DNA supercoiling in Escherichia coli: How to study homeostatically controlled systems using control analysis.
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Enzyme IICBGlc of the phosphoenolpyruvate: glucose phosphotransferase system controls the growth rate of Escherichia coli at fixed, low glucose concentrations as determined using glucose-limited chemostats
No full textInter-reciprocity applied to electrical networks
No full textElectrical Engineering, Mathematics and Computer Scienc
DNA supercoiling in Escherichia coli is under tight and subtle homeostatic control, involving gene-expression and metabolic regulation of both topoisomerase I and DNA gyrase
No full textDNA of prokaryotes is in a nonequilibrium. structural state, characterized as 'active' DNA supercoiling. Alterations in this state affect many life processes and a homeostatic control of DNA supercoiling has been suggested [Menzel, R. & Gellert. M. (1983) Cell 34, 105-113]. We here report on a new method for quantifying homeostatic control of the high-energy state of in vivo DNA. The method involves making small perturbation in the expression of topoisomerase 1, and measuring the effect on DNA supercoiling of a reporter plasmid and on the expression of DNA gyrase. In a separate set of experiments the expression of DNA gyrase was manipulated and the control on DNA supercoiling and topoisomerase I expression was measured [part of these latter experiments has been published in Jensen, P.R., van der Weijden, C.C., Jensen, L.B., Westerhoff, H.V. & Snoep, J.L. (1999) Eur. J. Biochem. 266. 865-877]. Of the two regulatory mechanisms via which homeostasis is conferred, regulation of enzyme activity or regulation of enzyme expression, we quantified the first to be responsible for 72% and the latter for 28%. The gene expression regulation could be dissected to DNA gyrase (21%) and to topoisomerase 1 (7%). On a scale from 0 (no homeostatic control) to I (full homeostatic control) we quantified the homeostatic control of DNA supercoiling at 0.87. A 10% manipulation of either topoisomerase I or DNA gyrase activity results in a 1.3% change of DNA supercoiling only. We conclude that the homeostatic regulation of the nonequilibrium DNA structure in wild-type Escherichia coli is almost complete and subtle (i.e. involving at least three regulatory mechanisms)
Deciphering living networks : Perturbation strategies for functional genomics
Full text linkThesis: Deciphering living networks: Perturbation strategies for functional genomics Alberto de la Fuente [email protected] Molecular Cell Physiology Free University Amsterdam Advisors: Prof.Dr. H.V. Westerhoff Prof.Dr. J.L. Snoep Supervisor: Dr. P.J. Mendes Using modern experimental techniques it is possible to measure the concentrations of a great many, and ultimately all, cellular constituents such as mRNAs, proteins and metabolites. Given these experimental technologies, astronomical amounts of new data will appear. To enable us to see the forest for the trees, we need to find ways in which best to analyze the data so as to obtain better understanding of biochemical systems and predictive power. When those new ways of analyzing the data are found, this may even lead to a preference for a certain type of data or certain experimental methodologies. This may then help direct experimentation towards the highest possible impact for understanding of biochemical systems. Ideally, the three levels of biochemical organization, i.e. mRNAs, proteins and metabolites, are studied all together in an integrated fashion. However, due to the number of components and complexity of such integrated systems it is reasonable to try to decompose the system and to study the subsystems or to use simplified descriptions of the whole system. It will be important to decompose the system into subsystems that behave in isolation in much the same way as they do when they are embedded in the whole system. This is exactly what I deal with in my dissertation; on the one hand I show how and when it is possible to study the systems properties of metabolism in vivo, ignoring the effects of gene and protein expression, and on the other hand I develop a quantitative concept in terms of Metabolic Control Analysis to describe the properties of the whole system in a simplified form, i.e. as a gene network a description of only the dynamics of gene expression without explicit accounting for metabolites and proteins. This concept enables the inference of the topology of such gene networks from experimental data. The analysis guides the experimenter towards the specific experiments that need to be done in order to be able to infer the interactions between genes on a genome scale. After introducing the relevant preliminaries in Chapter 1, in Chapter 2 I introduce the concept of hierarchical biochemical systems and show how to express their properties in terms of properties of the individual flux-disconnected modules of which it is composed. In particular, I focus on the study of metabolic systems. I propose several methods with the goal of distinguishing regulation that takes place at the metabolic level only from regulation that involves transcription or translation, thus quantifying the relative importance of each of these processes to the global systems behavior. I verify the experimental applicability of these methods by analyzing data obtained by simulation of a biochemical system. In Chapter 3 I introduce the concept of the gene network. Gene networks are network models in which the nodes represent gene activities (mRNA levels) and the edges correspond to regulatory interactions between them. Such models are highly phenomenological because they do not represent explicitly the proteins and metabolites that mediate those interactions. I show the use of Regulatory Strengths to quantify gene-gene interactions and show how to express these coefficients in terms of the biochemical system underlying these interactions. This approach establishes a clear and formal link between the phenomenological gene network modeling and more detailed approaches considering the hierarchical structuring of biochemical networks as introduced in Chapter 2.Snoep, J.L. [Promotor]Westerhoff, H.V. [Promotor]Mendes, P. [Copromotor
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