Some notes on the theory of reaction rates: Enzymatic reactions

1963 ◽  
Vol 25 (2) ◽  
pp. 141-154 ◽  
Author(s):  
Vojtech Ličko
Keyword(s):  
Reaction Rates ◽  
Enzymatic Reactions

scholarly journals A three-tiered colloidosomal microreactor for continuous flow catalysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hua Wu ◽  
Xuanlin Du ◽  
Xiaohui Meng ◽  
Dong Qiu ◽  
Yan Qiao
Keyword(s):  
Continuous Flow ◽  
High Performance ◽  
Rational Design ◽  
Packed Column ◽  
Building Blocks ◽  
Reaction Rates ◽  
Biological Species ◽  
Hybrid Nanoparticles ◽  
Enzymatic Reactions ◽  
Design Integration

AbstractIntegrative colloidosomes with hierarchical structure and advanced function may serve as biomimetic microreactors to carry out catalytic reactions by compartmentalizing biological species within semipermeable membranes. Despite of recent progress in colloidosome design, integration of biological and inorganic components into tiered structures to tackle the remaining challenges of biocatalysis is highly demanded. Here, we report a rational design of three-tiered colloidosomes via the Pickering emulsion process. The microreactor consists of crosslinked amphiphilic silica-polymer hybrid nanoparticles as the semipermeable shell, an enzyme-incorporated catalytic sub-layer, and a partially-silicified adsorptive lumen. By leveraging confinement and enrichment effect, we demonstrate the acceleration of lipase-catalyzed ester hydrolysis within the microcompartment of organic-inorganic hybrid colloidosomes. The catalytic colloidosomes are further assembled into a closely packed column for enzymatic reactions in a continuous flow format with enhanced reaction rates. The three-tiered colloidosomes provide a reliable platform to integrate functional building blocks into a biomimetic compartmentalized microreactor with spatially controlled organization and high-performance functions.

scholarly journals Ionic liquids as (co)solvents for enzymatic reactions

2006 ◽  
Vol 12 (3) ◽  
pp. 181-186 ◽  
Author(s):  
Muzafera Paljevac ◽  
Maja Habulin ◽  
Zeljko Knez
Keyword(s):  
Ionic Liquid ◽  
Ionic Liquids ◽  
Immobilized Lipase ◽  
Rhizomucor Miehei ◽  
Reaction Rates ◽  
Synthetic Activity ◽  
Enzymatic Reactions ◽  
Lipozyme Rm Im ◽  
Catalyzed Reactions ◽  
Enzyme Catalyzed Reactions

Ionic liquids are low melting point salts that represent an exciting new class of reaction solvents. Many reactions show advantages when carried out in ionic liquids, either with regard to enhanced reaction rates, improved selectivity, or easier reuse of catalysts. To ascertain the influence of ionic liquids on the enzyme activity, three different ionic liquids 1-butyl-3-methylimidazolium chloride ([bmim] [CI]) 1-butyl-3-methylimidazolium hexafluorophosphate ([bmim] [PF6]) and 1-butyl-3-methylimidazolium tetrafluoroborate ([bmim][BF4]) were synthesized and investigated as potential media for the hydrolysis of carboxymethyl cellulose, catalyzed by non-immobilized cellulase from Humicola insolens (Celluzyme 0,7T) and for ester synthesis, catalyzed by immobilized lipase from Rhizomucor miehei (Lipozyme RM IM). Enzyme-catalyzed reactions were performed in a batch stirred reactor at atmospheric pressure. Celluzyme 0,7T showed better activity in hydrophobic ionic liquid ([bmim] [PF6]), as compared to hydrophilic ionic liquid ([bmim] [BF4]). In the case of Lipozyme RM IM, the synthetic activity of the enzyme was strongly reduced by incubating the enzyme in ionic liquids.

scholarly journals Parameter estimation in tree graph metabolic networks

2016 ◽  
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known, which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: Firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is very fast compared to the usually applied methods, since it is not based on an iterative scheme. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals Parameter estimation in tree graph metabolic networks

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2417
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D. Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals The effect of macromolecular crowding on single-round transcription by E. coli RNA polymerase

2017 ◽  
Author(s):  
SangYoon Chung ◽  
Eitan Lerner ◽  
Yan Jin ◽  
Soohong Kim ◽  
Yazan Alhadid ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Transcription Initiation ◽  
Macromolecular Crowding ◽  
Reaction Rates ◽  
Scaled Particle Theory ◽  
Hard Sphere Model ◽  
Enzymatic Reactions ◽  
Crowding Effects ◽  
Cellular Environment

ABSTRACTBiological reactions in the cellular environment differ physicochemically from those performed in dilute buffer solutions due to, in part, slower diffusion of various components in the cellular milieu, increase in their chemical activities, and modulation of their binding affinities and conformational stabilities.In vivotranscription is therefore expected to be strongly influenced by the ‘crowdedness’ of the cell. Previous studies of transcription under macromolecular crowding conditions have focused mainly on multiple cycles of RNAP-Promoter associations, assuming that the association is the rate-determining step of the entire transcription process. However, recent reports demonstrated that late initiation and promoter escape could be the rate-determining steps for some promoter DNA sequences. The investigation of crowding effects on these steps undersingle-roundconditions is therefore crucial for better understanding of transcription initiationin vivo. Here, we have implemented anin vitrotranscription quenched-kinetics single-molecule assay to investigate the dependence of transcription reaction rates on the sizes and concentrations of crowders. Our results demonstrate an expected slowdown of transcription kinetics due to increased viscosity, and an unexpected enhancement in transcription kinetics by large crowding agents (at a given viscosity). More importantly, the enhancement’s dependence on crowder size significantly deviates from hard-sphere model (scaled-particle theory) predictions, commonly used for description of crowding effects. Our findings shed new light on how enzymatic reactions are affected by crowding conditions in the cellular milieu.

scholarly journals Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway

2016 ◽  
Vol 2 (1) ◽  
pp. e1501235 ◽  
Author(s):  
Markus A. Keller ◽  
Andre Zylstra ◽  
Cecilia Castro ◽  
Alexandra V. Turchyn ◽  
Julian L. Griffin ◽  
...  
Keyword(s):  
Pentose Phosphate Pathway ◽  
Metabolic Networks ◽  
Reaction Rates ◽  
Structural Features ◽  
Pentose Phosphate ◽  
Resonance Spectroscopy ◽  
Enzymatic Reactions ◽  
Evolutionary Origins ◽  
Sugar Phosphates ◽  
Neutral Conditions

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks.

Inhibition of Enzymatic Reactions. A Rapid Method to Determine the Index pI50

2002 ◽  
Vol 57 (5-6) ◽  
pp. 496-499 ◽  
Author(s):  
David Kulhavý ◽  
Alexander Čegan ◽  
Karel Komers ◽  
Jaromír Mindl
Keyword(s):  
Rapid Method ◽  
Mixed Type ◽  
Enzymatic Reaction ◽  
Reaction Rates ◽  
Fast Method ◽  
Enzymatic Reactions ◽  
Initial Substrate ◽  
The One ◽  
Hydrolysis Of

The activity of every substance I inhibiting an enzymatic reaction can be approximately evaluated by the index pI50. This paper describes a simple and fast method of estimate and/or determination of this index. The method is based on the linearity of the dependence of the ratio of reaction rates of uninhibited and inhibited reaction vs. concentration of the inhibitor at constant initial substrate and enzyme concentrations for fully competitive, noncompetitive, uncompetitive and mixed type of inhibition by the one inhibitor. The validity of the method is demonstrated by four inhibitors of hydrolysis of acetylthiocholine by butyrylcholine esterase.

scholarly journals Parameter estimation in tree graph metabolic networks

2016 ◽  
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known, which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: Firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is very fast compared to the usually applied methods, since it is not based on an iterative scheme. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals Parameter estimation in tree graph metabolic networks

2016 ◽  
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known, which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: Firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is very fast compared to the usually applied methods, since it is not based on an iterative scheme. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

SEM analysis of the change in the reaction rates with deposit structures in the copper-iron cementation system

1978 ◽  
Vol 36 (1) ◽  
pp. 456-457
Author(s):  
V. Annamalai ◽  
L.E. Murr
Keyword(s):  
Structural Characteristics ◽  
Secondary Electron Emission ◽  
Copper Deposit ◽  
Reaction Rates ◽  
Sem Analysis ◽  
Experimental Conditions ◽  
Major Drawback ◽  
Emission Mode ◽  
Scrap Iron ◽  
Image Position

Economical recovery of copper metal from leach liquors has been carried out by the simple process of cementing copper onto a suitable substrate metal, such as scrap-iron, since the 16th century. The process has, however, a major drawback of consuming more iron than stoichiometrically needed by the reaction.Therefore, many research groups started looking into the process more closely. Though it is accepted that the structural characteristics of the resultant copper deposit cause changes in reaction rates for various experimental conditions, not many systems have been systematically investigated. This paper examines the deposit structures and the kinetic data, and explains the correlations between them.A simple cementation cell along with rotating discs of pure iron (99.9%) were employed in this study to obtain the kinetic results The resultant copper deposits were studied in a Hitachi Perkin-Elmer HHS-2R scanning electron microscope operated at 25kV in the secondary electron emission mode.

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