Correlation Metric Construction: Theory of Statistical Construction of Reaction Mechanisms

2006 ◽  
Author(s):  
John Ross ◽  
Igor Schreiber ◽  
Marcel O. Vlad
Keyword(s):  
Time Series ◽  
Chemical Reactions ◽  
Reaction Pathway ◽  
Reaction System ◽  
Chemical Species ◽  
Biochemical Networks ◽  
Statistical Dependence ◽  
Nand Gate ◽  
Regulatory Structure ◽  
Genome Study

In chapter 5, we studied the responses of chemical species in a reaction system to pulse perturbations and showed the deduction of direct, causal connectivities by chemical reactions—the reaction pathway—and the reaction mechanism from such measurements. The causal connectivites give the information on how the chemical species are connected by chemical reactions. In this chapter we turn to another source of information about chemical species in a reaction system, that of correlations among the species. Correlations of concentrations are measures of the statistical dependence of the concentration of one species on that of one or more of the other species in the system. Such correlations can be determined from measurements of time series of concentrations collected around a stationary state (nonequilibrium or equilibrium). We shall show that from concentration correlations it is possible to construct a skeletal diagram of the reaction system that gives a graphical measure of strong control and regulatory structure in reaction networks, gives some information on connectivity, leads to information on the reaction pathway and mechanism, and may simplify the analysis of such networks by identifying possible, nearly separable subsystems. We begin with the demonstration and explanation of this approach by analyzing some abstract reaction models. In chapter 8 we show the utility of this “correlation metric construction” (CMC) with application to time-series measurements on a part of glycolysis, and in chapter 13 on an extensive genome study. Consider a chemical system as shown in fig. 7.1. Mechanisms of this type are common in biochemical networks. For example, the subnetwork of fig. 7.1 containing S3 to S5 is based on a simple model of fructose interconversion in glycolysis and the subnetwork composed of S6 to S7 is similar to the phosphorylation/dephosphorylation cycles found in cyclic cascades. As an aside, this mechanism performs the function of a biochemical NAND gate, another example of a chemical computational function (see chapter 4).

Introduction

2006 ◽  
Author(s):  
John Ross ◽  
Igor Schreiber ◽  
Marcel O. Vlad
Keyword(s):  
Reaction Mechanism ◽  
Chemical Kinetics ◽  
Chemical Engineering ◽  
Reaction Pathway ◽  
Reaction System ◽  
Chemical Species ◽  
Pulse Method ◽  
Elementary Reaction ◽  
Elementary Reactions ◽  
Arbitrary Strength

Chemical kinetics as a science has existed for more than a century. It deals with the rates of reactions and the details of how a given reaction proceeds from reactants to products. In a chemical system with many chemical species, there are several questions to be asked: What species react with what other species? In what temporal order? With what catalysts? And with what results? The answers constitute the macroscopic reaction mechanism. The process can be described macroscopically by listing the reactants, intermediates, products, and all the elementary reactions and catalysts in the reaction system. The present book is a treatise and text on the determination of complex reaction mechanisms in chemistry and in chemical reaction systems that occur in chemical engineering, biochemistry, biology, biotechnology, and genomics. A basic knowledge of chemical kinetics is assumed. Several approaches are suggested for the deduction of information on the causal chemical connectivity of the species, on the elementary reactions among the species, and on the sequence of the elementary reactions that constitute the reaction pathway and the reaction mechanism. Chemical reactions occur by the collisions of molecules, and such an event is called an elementary reaction for specified reactant and product molecules. A balanced stoichiometric equation for an elementary reaction yields the number of each type of molecule according to conservation of atoms, mass, and charge. Figure 1.1 shows a relatively simple reaction mechanism for the decomposition of ozone by light, postulated to occur in a series of three elementary steps. (The details of collisions of molecules and bond rearrangements are not discussed.) All approaches are based on the measurements of the concentrations of chemical species in the whole reaction system, not on parts, as has been the practice. One approach is called the pulse method, in which a pulse of concentration of one or more species of arbitrary strength is applied to a reacting system and the responses of as many species as possible are measured. From these responses causal chemical connectivities may be inferred. The basic theory is explained, demonstrated on a model mechanism, and tested in an experiment on a part of glycolysis.

Experimental Test and Applications of Correlation Metric Construction

2006 ◽  
Author(s):  
John Ross ◽  
Igor Schreiber ◽  
Marcel O. Vlad
Keyword(s):  
Time Series ◽  
Steady State ◽  
Reaction Mechanism ◽  
Experimental Test ◽  
Reaction Pathway ◽  
Chemical Species ◽  
Reaction Network ◽  
Sampling Period ◽  
The Matrix ◽  
Time Lagged

In this chapter we present an experimental test case of the deduction of a reaction pathway and mechanism by means of correlation metric construction from time-series measurements of the concentrations of chemical species. We choose as the system an enzymatic reaction network, the initial steps of glycolysis. Glycolysis is central in intermediary metabolism and has a high degree of regulation. The reaction pathway has been well studied and thus it is a good test for the theory. Further, the reaction mechanism of this part of glycolysis has been modeled extensively. The quantity and precision of the measurements reported here are sufficient to determine the matrix of correlation functions and, from this, a reaction pathway that is qualitatively consistent with the reaction mechanism established previously. The existence of unmeasured species did not compromise the analysis. The quantity and precision of the data were not excessive, and thus we expect the method to be generally applicable. This CMC experiment was carried out in a continuous-flow stirred-tank reactor (CSTR). The reaction network considered consists of eight enzymes, which catalyze the conversion of glucose into dihydroxyacetone phosphate and glyceraldehyde phosphate. The enzymes were confined to the reactor by an ultrafiltration membrane at the top of the reactor. The membrane was permeable to all low molecular weight species. The inputs are (1) a reaction buffer, which provides starting material for the reaction network to process, maintains pH and pMg, and contains any other species that act as constant constraints on the system dynamics, and (2) a set of “control species” (at least one), whose input concentrations are changed randomly every sampling period over the course of the experiment. The sampling period is chosen such that the system almost, but not quite, relaxes to a chosen nonequilibrium steady state. The system is kept near enough to its steady state to minimize trending (caused by the relaxation) in the time series, but far enough from the steady state that the time-lagged autocorrelation functions for each species decay to zero over three to five sampling periods. This long decay is necessary if temporal ordering in the network is to be analyzed.

Oscillatory Reactions

2006 ◽  
Author(s):  
John Ross ◽  
Igor Schreiber ◽  
Marcel O. Vlad
Keyword(s):  
Chemical Reactions ◽  
Negative Feedback ◽  
Exothermic Reaction ◽  
Reaction System ◽  
Chemical Species ◽  
Heat Removal ◽  
Solid Catalyst ◽  
Distinct Property ◽  
Present Context ◽  
Flow Through

Oscillating chemical reactions have the distinct property of a periodic or aperiodic oscillatory course of concentrations of reacting chemical species as well as temperature. This behavior is due to an interplay of positive and negative feedback with alternating dominance of these two dynamic effects. For example, an exothermic reaction produces heat that increases temperature, which in turn increases reaction rate and thus produces more heat. Such a thermokinetic effect is thus autocatalytic and represents a positive feedback. When run in a flow-through reactor with a cooling jacket, the autocatalysis is eventually suppressed if the reactant is consumed faster than it is supplied. At the same time, the excess heat is being removed via the jacket, which tends to quench the system. The latter two processes are inhibitory and represent a negative feedback. If the heat removal is slow enough so as not to suppress entirely the autocatalysis, but fast enough for temperature to drop before there is enough reactant available via the feed to restore autocatalysis, then there are oscillations in both temperature and concentration of the reactant. Examples of these thermokinetic oscillations are combustion reactions, which typically take place either in homogeneous gaseous or liquid phase or in the presence of a solid catalyst, thus representing a heterogeneous reaction system. Of more interest in the present context are reactions where thermal effects are often negligible, or the system is maintained at constant temperature, as is the case with homogeneous chemical reactions taking place in a thermostated flow-through reactor, as well as biochemical reactions in living cells and organisms. Autocatalysis can easily be realized in isothermal systems, where instead of a heat-producing reaction there will typically be a closed reaction pathway, such that species involved are produced faster by reactions along the pathway than they are consumed by removal reactions. As an example, let us examine the well-known Belousov–Zhabotinsky (BZ) reaction of bromate with malonic acid catalyzed by cerium ions in acidic solution.

Catalytic Resonance Theory: Parallel Reaction Pathway Control

2019 ◽  
Author(s):  
M. Alexander Ardagh ◽  
Manish Shetty ◽  
Anatoliy Kuznetsov ◽  
Qi Zhang ◽  
Phillip Christopher ◽  
...  
Keyword(s):  
Chemical Reactions ◽  
Active Site ◽  
Active Sites ◽  
Reaction Pathway ◽  
Control Strategies ◽  
Oscillation Amplitude ◽  
Catalytic Performance ◽  
Parallel Reaction ◽  
Resonance Theory ◽  
Static Conditions

Catalytic enhancement of chemical reactions via heterogeneous materials occurs through stabilization of transition states at designed active sites, but dramatically greater rate acceleration on that same active site is achieved when the surface intermediates oscillate in binding energy. The applied oscillation amplitude and frequency can accelerate reactions orders of magnitude above the catalytic rates of static systems, provided the active site dynamics are tuned to the natural frequencies of the surface chemistry. In this work, differences in the characteristics of parallel reactions are exploited via selective application of active site dynamics (0 < ΔU < 1.0 eV amplitude, 10<sup>-6</sup> < f < 10<sup>4</sup> Hz frequency) to control the extent of competing reactions occurring on the shared catalytic surface. Simulation of multiple parallel reaction systems with broad range of variation in chemical parameters revealed that parallel chemistries are highly tunable in selectivity between either pure product, even when specific products are not selectively produced under static conditions. Two mechanisms leading to dynamic selectivity control were identified: (i) surface thermodynamic control of one product species under strong binding conditions, or (ii) catalytic resonance of the kinetics of one reaction over the other. These dynamic parallel pathway control strategies applied to a host of chemical conditions indicate significant potential for improving the catalytic performance of many important industrial chemical reactions beyond their existing static performance.

Determination of Complex Reaction Mechanisms

2006 ◽  
Author(s):  
John Ross ◽  
Igor Schreiber ◽  
Marcel O. Vlad
Keyword(s):  
Reaction Mechanism ◽  
Reaction Mechanisms ◽  
Reaction Pathway ◽  
Chemical Species ◽  
Rate Coefficients ◽  
Chemical System ◽  
Evolutionary Development ◽  
Complex Reaction ◽  
Oscillatory Reactions

In a chemical system with many chemical species several questions can be asked: what species react with other species: in what temporal order: and with what results? These questions have been asked for over one hundred years about simple and complex chemical systems, and the answers constitute the macroscopic reaction mechanism. In Determination of Complex Reaction Mechanisms authors John Ross, Igor Schreiber, and Marcel Vlad present several systematic approaches for obtaining information on the causal connectivity of chemical species, on correlations of chemical species, on the reaction pathway, and on the reaction mechanism. Basic pulse theory is demonstrated and tested in an experiment on glycolysis. In a second approach, measurements on time series of concentrations are used to construct correlation functions and a theory is developed which shows that from these functions information may be inferred on the reaction pathway, the reaction mechanism, and the centers of control in that mechanism. A third approach is based on application of genetic algorithm methods to the study of the evolutionary development of a reaction mechanism, to the attainment given goals in a mechanism, and to the determination of a reaction mechanism and rate coefficients by comparison with experiment. Responses of non-linear systems to pulses or other perturbations are analyzed, and mechanisms of oscillatory reactions are presented in detail. The concluding chapters give an introduction to bioinformatics and statistical methods for determining reaction mechanisms.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

scholarly journals A Review on Recent Progress of Glycan-Based Surfactant Micelles as Nanoreactor Systems for Chemical Synthesis Applications

2021 ◽  
Vol 2 (1) ◽  
pp. 168-186
Author(s):  
Bahareh Vafakish ◽  
Lee D. Wilson
Keyword(s):  
Chemical Synthesis ◽  
Chemical Reactions ◽  
Recent Progress ◽  
Chemical Species ◽  
Reaction Rates ◽  
Bulk Solution ◽  
Surfactant Micelles ◽  
Conventional Surfactant ◽  
Recent Developments ◽  
To Receive

The nanoreactor concept and its application as a modality to carry out chemical reactions in confined and compartmentalized structures continues to receive increasing attention. Micelle-based nanoreactors derived from various classes of surfactant demonstrate outstanding potential for chemical synthesis. Polysaccharide (glycan-based) surfactants are an emerging class of biodegradable, non-toxic, and sustainable alternatives over conventional surfactant systems. The unique structure of glycan-based surfactants and their micellar structures provide a nanoenvironment that differs from that of the bulk solution, and supported by chemical reactions with uniquely different reaction rates and mechanisms. In this review, the aggregation of glycan-based surfactants to afford micelles and their utility for the synthesis of selected classes of reactions by the nanoreactor technique is discussed. Glycan-based surfactants are ecofriendly and promising surfactants over conventional synthetic analogues. This contribution aims to highlight recent developments in the field of glycan-based surfactants that are relevant to nanoreactors, along with future opportunities for research. In turn, coverage of research for glycan-based surfactants in nanoreactor assemblies with tailored volume and functionality is anticipated to motivate advanced research for the synthesis of diverse chemical species.

Analysis of chemical effects on reflected-shock flow fields in combustible gas

1985 ◽  
Vol 160 ◽  
pp. 29-45 ◽  
Author(s):  
Yasunari Takano ◽  
Teruaki Akamatsu
Keyword(s):  
Heat Release ◽  
Chemical Reactions ◽  
Induction Time ◽  
Combustion Process ◽  
Chemical Species ◽  
Flow Fields ◽  
Rate Equations ◽  
Reflected Shock ◽  
Combustible Gas ◽  
Analytical Results

This paper analyses effects of chemical reactions on reflected-shock flow fields in shock tubes. The method of linearized characteristics is applied to analyse gasdynamic disturbances due to chemical reactions. The analysis treats cases where combustible gas is highly diluted in inert gas, and assumes that flows are one-dimensional and that upstream flows in front of the reflected-shock waves are in the frozen state. The perturbed gasdynamic properties in the reflected-shock flow fields are shown to be expressible mainly in terms of a heat-release function for combustion process. In particular, simple relations are obtained between the heat-release function and the physical properties at the end wall of a shock tube. As numerical examples of the analysis, the present formulation is applied to calculate gasdynamic properties in the reflected-shock region in a H2–O2–Ar mixture. Procedures are demonstrated for calculation of the heat-release function by numerically integrating rate equations for chemical species. The analytical results are compared with rigorous solutions obtained numerically by use of a finite-difference method. It is shown that the formulation can afford exact solutions in cases where chemical behaviours are not essentially affected by gasdynamic behaviours. When the induction time of the combustion process is reduced to some extent owing to gasdynamic disturbances, some discrepancies appear between analytical results and rigorous solutions. An estimate is made of the induction-time reduction, and a condition is written down for applicability of the analysis.

Systems biology markup language: Level 2 and beyond

2003 ◽  
Vol 31 (6) ◽  
pp. 1472-1473 ◽  
Author(s):  
A. Finney ◽  
M. Hucka
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Chemical Species ◽  
Biochemical Networks ◽  
Markup Language ◽  
Exchange Format ◽  
Systems Biology Markup Language ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.

The Present and the Future Perspectives of Biological Network Inference

2011 ◽  
pp. 118-140 ◽  
Author(s):  
Paola Lecca ◽  
Alida Palmisano
Keyword(s):  
Reaction Kinetics ◽  
Network Structure ◽  
Network Inference ◽  
Biological Network ◽  
Reaction Pathway ◽  
Model Identification ◽  
Chemical Species ◽  
Kinetics Parameters ◽  
Common Opinion ◽  
Genomic Biology

Biological network inference is based on a series of studies and computational approaches to the deduction of the connectivity of chemical species, the reaction pathway, and the reaction kinetics of complex reaction systems from experimental measurements. Inference for network structure and reaction kinetics parameters governing the dynamics of a biological system is currently an active area of research. In the era of post-genomic biology, it is a common opinion among scientists that living systems (cells, tissues, organs and organisms) can be understood in terms of their network structure as well as in term of the evolution in time of this network structure. In this chapter, the authors make a survey of the recent methodologies proposed for the structure inference and for the parameter estimation of a system of interacting biological entities. Furthermore, they present the recent works of the authors about model identification and calibration.

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