Dynamic biochemical reaction process analysis and pathway modification predictions

Conejeros, Raúl; Vassiliadis, Vassilios S.

doi:10.1002/(sici)1097-0290(20000505)68:3<285::aid-bit7>3.0.co;2-x

Cited by 10 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) The activities of the three enzymes ACVS, IPNS, and AT are decreased according to zero-order kinetics with respect to the time t , (see eqs −).…”

Section: Theorymentioning

confidence: 99%

“…In fermentation processes, it is important to identify the enzyme that most controls the reactions directly associated with the formation of a desired metabolite inside a microbial cell. − If the activity of this identified enzyme is changed, e.g., by genetic manipulation, it may be possible to greatly increase the production of the desired metabolite. Although such an enzyme may be called “bottleneck enzyme”, we define this as the enzyme that most increases the total amount of the desired metabolite produced as a result of change in its activity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Method for Determination of the Main Bottleneck Enzyme in a Metabolic Reaction Network by Dynamic Sensitivity Analysis

Shiraishi

Suzuki

2008

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

In fermentation processes, it is important to identify the bottleneck enzyme in a given metabolic reaction network, because increasing its activity would lead to an enhancement in the productivity of a desired metabolite. To efficiently identify the bottleneck enzyme, the present work discusses a systematic approach using dynamic logarithmic gains, i.e., percentage changes in the concentration of the desired metabolite in the response to an infinitesimal percentage change in enzyme activities, as an indicator. A penicillin V fed-batch fermentation model proposed by other researchers was used as an example of application. The calculated results showed that not only the time courses of the dynamic logarithmic gains but also the ranking of the magnitudes of their values is changed with time, implying that the bottleneck enzyme is changed with time in this fermentation process. To determine the bottleneck enzyme from such time-transient behaviors, the dynamic logarithmic gains were integrated over the entire fermentation period and divided by the total process time. The time-averaged dynamic logarithmic gains thus calculated were then ranked according to the magnitudes of their values. However, this ranking, corresponding to the ranking of candidate bottleneck enzymes, was not identical to that obtained with regard to the total amount of the desired metabolite produced as a result of finite change in each enzyme activity. This is because the concentration of the substrate for the reaction catalyzed by the relevant enzyme displays a decreasing behavior, so that the degree of increase in the corresponding flux was smaller for the finite increase of the enzyme activity and the total amount of the desired metabolite did not proportionally increase. When the concentration of the substrate for the reaction catalyzed by the most likely bottleneck enzyme displays a decreasing behavior, therefore, it is necessary to check not only the ranking of the time-average dynamic logarithmic gains but also whether the total amount of the desired metabolite is ranked first when the relevant enzyme activity is finitely changed.

show abstract

“…(2) The activities of the three enzymes ACVS, IPNS, and AT are decreased according to zero-order kinetics with respect to the time t , (see eqs −).…”

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method for Determination of the Main Bottleneck Enzyme in a Metabolic Reaction Network by Dynamic Sensitivity Analysis

Shiraishi

Suzuki

2008

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

Science – or not? The status and dynamics of biotechnology

Buchholz

2007

Biotechnology Journal

View full text Add to dashboard Cite

This review traces the emergence of biotechnology as a new scientific discipline since the 1980s, when it became a major economic force. Significant changes in theoretical perception, research strategies, aims, and experimental methods, mainly in genetic engineering techniques, occurred during this period. The article is based on an analysis of its scientific status over four decades: the 60s and 70s when work in the field proceeded in different disciplines with a low level of coherence and little integration, then a significant change during the 80s and 90s when common approaches and the merging of molecular biology and biochemical engineering created a new discipline. The analysis covers scientific highlights and outstanding technical progress, presenting two studies undertaken by scientific and governmental agencies in Germany and the USA, as well as results of interviews and a questionnaire dealing with the scientific status of biotechnology. Answers to the questionnaire were obtained from internationally known scientists and from young scientists with biotechnology degrees. The results collected trace the transition of biotechnology from heterogeneous specialties and approaches towards a scientific discipline of its own. A hypothesis is put forward suggesting a new common paradigm allowing for a coherent perception the of phenomena observed.

show abstract

Multicriteria optimization of biochemical systems by linear programming: Application to production of ethanol by Saccharomyces cerevisiae

Vera

Atauri

Cascante

et al. 2003

Biotech & Bioengineering

View full text Add to dashboard Cite

In this study we present a method for simultaneous optimization of several metabolic responses of biochemical pathways. The method, based on the use of the power law formalism to obtain a linear system in logarithmic coordinates, is applied to ethanol production by Saccharomyces cerevisiae. Starting from an experimentally based kinetic model, we translated it to its power law equivalent. With this new model representation, we then applied the multiobjective optimization method. Our intent was to maximize ethanol production and minimize each of the internal metabolite concentrations. To ensure cell viability, all optimizations were carried out under imposed constraints. The different solutions obtained, which correspond to alternative patterns of enzyme overexpression, were implemented in the original model. We discovered few discrepancies between the S-system-optimized steady state and the corresponding optimized state in the original kinetic model, thus demonstrating the suitability of the S-system representation as the basis for the optimization procedure. In all optimized solutions, the ATP level reached its maximum and any increase in its activity positively affected the optimization process. This work illustrates that in any optimization study no single criteria is of general application being the multiobjective and constrained task the proper way to address it. It is concluded that the proposed multiobjective method can serve to carry out, in a single study, the general pattern of behavior of a given metabolic system with regard to its control and optimization.

show abstract

Dynamic biochemical reaction process analysis and pathway modification predictions

Cited by 10 publications

References 22 publications

Method for Determination of the Main Bottleneck Enzyme in a Metabolic Reaction Network by Dynamic Sensitivity Analysis

Method for Determination of the Main Bottleneck Enzyme in a Metabolic Reaction Network by Dynamic Sensitivity Analysis

Science – or not? The status and dynamics of biotechnology

Multicriteria optimization of biochemical systems by linear programming: Application to production of ethanol by Saccharomyces cerevisiae

Contact Info

Product

Resources

About