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Fundamentals of Enzyme Kinetics
01 Jan 1979-
TL;DR: Basic Principles of Chemical Kinetics Introduction to Enzyme Kinetics "Alternative" Enzymes Practical Aspects of Kinetics Deriving Steady-state Rate Equations Reversible Inhibition and Activation Tight-binding and Irreversible Inhibitors
Abstract: Basic Principles of Chemical Kinetics Introduction to Enzyme Kinetics "Alternative" Enzymes Practical Aspects of Kinetics Deriving Steady-state Rate Equations Reversible Inhibition and Activation Tight-binding and Irreversible Inhibitors Reactions of More than One Substrate Use of Isotopes for Studying Enzyme Mechanisms Effect of pH on Enzyme Activity Temperature Effects on Enzyme Activity Regulation of Enzyme Activity Multienzyme Systems Fast Reactions Estimation of Kinetic Constants Standards for Reporting Enzymology Data Solutions and Notes to Problems Index
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TL;DR: This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equation, stochastic equations, and so on.
Abstract: The spatiotemporal expression of genes in an organism is determined by regulatory systems that involve a large number of genes connected through a complex network of interactions. As an intuitive understanding of the behavior of these systems is hard to obtain, computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This report reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, ordinary and partial differential equations, stochastic equations, Boolean networks and their generalizations, qualitative differential equations, and rule-based formalisms. In addition, the report discusses how these formalisms have been used in the modeling and simulation of regulatory systems.
2,739 citations
TL;DR: This work has analyzed the chemical reactions controlling transcript initiation and translation termination in a single such "genetically coupled" link as a precursor to modeling networks constructed from many such links.
Abstract: In cellular regulatory networks, genetic activity is controlled by molecular signals that determine when and how often a given gene is transcribed. In genetically controlled pathways, the protein product encoded by one gene often regulates expression of other genes. The time delay, after activation of the first promoter, to reach an effective level to control the next promoter depends on the rate of protein accumulation. We have analyzed the chemical reactions controlling transcript initiation and translation termination in a single such “genetically coupled” link as a precursor to modeling networks constructed from many such links. Simulation of the processes of gene expression shows that proteins are produced from an activated promoter in short bursts of variable numbers of proteins that occur at random time intervals. As a result, there can be large differences in the time between successive events in regulatory cascades across a cell population. In addition, the random pattern of expression of competitive effectors can produce probabilistic outcomes in switching mechanisms that select between alternative regulatory paths. The result can be a partitioning of the cell population into different phenotypes as the cells follow different paths. There are numerous unexplained examples of phenotypic variations in isogenic populations of both prokaryotic and eukaryotic cells that may be the result of these stochastic gene expression mechanisms.
1,955 citations
TL;DR: This paper looked at some of the RSM articles published during the last few years to identify common mistakes made in the application and the limitations of RSM.
1,780 citations
Cites background from "Fundamentals of Enzyme Kinetics"
...where m is the reaction rate, S is the substrate concentration, Km Michaelis–Menten constant and V is the limiting reaction rate (Cornish-Bowden, 1999)....
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...In fact, Michaelis–Menten equation defines a curve which is a rectangular hyperbola through the origin, with asymptotes S = Km and v = V (Cornish-Bowden, 1999)....
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TL;DR: The rate enhancements afforded by chymotrypsin and subtilisin in the transesterification reaction in octane are of the order of 100 billion-fold; covalent modification of the active center of the enzymes by a site-specific reagent renders them catalytically inactive in organic solvents.
948 citations
TL;DR: The data suggest that a dilemma exists, namely, that either “intrinsic” KS or μmax can be measured but both cannot be determined at the same time, which should result in a competitive advantage of a cell capable of mixed-substrate growth because it can grow much faster at low substrate concentrations than one would expect from single- substrate kinetics.
Abstract: Growth kinetics, i.e., the relationship between specific growth rate and the concentration of a substrate, is one of the basic tools in microbiology. However, despite more than half a century of research, many fundamental questions about the validity and application of growth kinetics as observed in the laboratory to environmental growth conditions are still unanswered. For pure cultures growing with single substrates, enormous inconsistencies exist in the growth kinetic data reported. The low quality of experimental data has so far hampered the comparison and validation of the different growth models proposed, and only recently have data collected from nutrient-controlled chemostat cultures allowed us to compare different kinetic models on a statistical basis. The problems are mainly due to (i) the analytical difficulty in measuring substrates at growth-controlling concentrations and (ii) the fact that during a kinetic experiment, particularly in batch systems, microorganisms alter their kinetic properties because of adaptation to the changing environment. For example, for Escherichia coli growing with glucose, a physiological long-term adaptation results in a change in KS for glucose from some 5 mg liter−1 to ca. 30 μg liter−1. The data suggest that a dilemma exists, namely, that either “intrinsic” KS (under substrate-controlled conditions in chemostat culture) or μmax (under substrate-excess conditions in batch culture) can be measured but both cannot be determined at the same time. The above-described conventional growth kinetics derived from single-substrate-controlled laboratory experiments have invariably been used for describing both growth and substrate utilization in ecosystems. However, in nature, microbial cells are exposed to a wide spectrum of potential substrates, many of which they utilize simultaneously (in particular carbon sources). The kinetic data available to date for growth of pure cultures in carbon-controlled continuous culture with defined mixtures of two or more carbon sources (including pollutants) clearly demonstrate that simultaneous utilization results in lowered residual steady-state concentrations of all substrates. This should result in a competitive advantage of a cell capable of mixed-substrate growth because it can grow much faster at low substrate concentrations than one would expect from single-substrate kinetics. Additionally, the relevance of the kinetic principles obtained from defined culture systems with single, mixed, or multicomponent substrates to the kinetics of pollutant degradation as it occurs in the presence of alternative carbon sources in complex environmental systems is discussed. The presented overview indicates that many of the environmentally relevant apects in growth kinetics are still waiting to be discovered, established, and exploited.
715 citations