Quantitative use of model compound information: Monte Carlo simulation of the reactions of complex macromolecules

McDermott, Joseph P.; Libanati, Cristian; LaMarca, Concetta; Klein, Michael T.

doi:10.1021/ie00097a004

Cited by 44 publications

(31 citation statements)

References 7 publications

(7 reference statements)

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“…The first one was adapted from Gillespie's classic work (1976) on simple molecules and the second one has been used by McDermott et al (1990) to simulate lignin degradation. Although the subject of chemical reaction kinetics stochastic modeling has been treated in many theoretical publications (McQuarrie, 1967;Gillespie, 1977;Turner, 1977;Lopez-Serran0 et al, 1980;McDermott and Klein, 1986;Train and Klein, 1988), very little emphasis has been given to the confrontation with actual experimental kinetic data.…”

Section: Simulating the Hydrolytic Decomposition Of A Polysaccharidementioning

confidence: 99%

“…The state of the molecule is updated and then the nonreducing bonds are verified for a reaction in a similar fashion. This simulates the ability of the whole molecule to present more than a reactive site at a time and it also prevents the nonreducing bonds of remaining unreactive during the remaining part of the At in which it was formed (McDermott et al, 1990). Due to the memoryless property of the Markov process itself, the process can be stopped at any time t + At.…”

Section: Aiche Journalmentioning

confidence: 99%

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A Monte Carlo analysis of acid hydrolysis of glycosidic bonds in polysaccharides

Pinto

Kaliaguine

1991

AIChE Journal

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Section: Simulating the Hydrolytic Decomposition Of A Polysaccharidementioning

confidence: 99%

Section: Aiche Journalmentioning

confidence: 99%

A Monte Carlo analysis of acid hydrolysis of glycosidic bonds in polysaccharides

Pinto

Kaliaguine

1991

AIChE Journal

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“…Therefore, in some cases, in describing the reaction, it is easier to apply the stochastic method instead of analytical procedures (Kurtz, 1972). One of the major stochastic methods is based on the Monte Carlo technique (McDermott et al, 1986(McDermott et al, , 1990Carbonell et al, 1998;Pinto and Kaliaguine, 1991). In principle, to simulate numerically the polymer hydrolysis, a (pseudo)random number that points out the polymer linkage attacked by an enzyme is generated.…”

Section: Introductionmentioning

confidence: 99%

Iteration model of starch hydrolysis by amylolytic enzymes

Wojciechowski¹,

Kozioł²,

Noworyta³

2001

Biotech & Bioengineering

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An elaborate computer program to simulate the process of starch hydrolysis by amylolytic enzymes was been developed. It is based on the Monte Carlo method and iteration kinetic model, which predict productive and non-productive amylase complexes with substrates. It describes both multienzymatic and multisubstrate reactions simulating the "real" concentrations of all components versus the time of the depolymerization reaction the number of substrates, intermediate products, and final products are limited only by computer memory. In this work, it is assumed that the "proper" substrate for amylases is the glucoside linkages in starch molecules. Dynamic changes of substrate during the simulation adequately influence the increase or decrease of reaction velocity, as well as the kinetics of depolymerization. The presented kinetic model, can be adapted to describe most enzymatic degradations of a polymer. This computer program has been tested on experimental data obtained for alpha- and beta-amylases.

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“…One approach to elucidating the chemistry of heavy hydrocarbons (for example, coals, heavy oils, and kerogens) involves the selection of and experimentation with simple compounds that serve as models of important reactive moieties within the complex feedstock. This approach, termed chemical modeling, has been implemented successfully in the past to provide insight into the reaction fundamentals of coals (Poutsma, 1990;Squire et al, 1986;Gavalas et al, 1981), lignins (McDermott et al, 1990;Train and Klein, 1988), and petroleum asphaltenes (Savage and Klein, 1989a;Neurock et al, 1989;Hikita et al, 1989).…”

Section: Introductionmentioning

confidence: 99%