Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network

Stalidzāns, Egils; Mozga, Ivars; Sulins, Jurijs; Zikmanis, Pēteris

doi:10.1109/tcbb.2016.2550451

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…A full scan of adjustable parameter combinations is suggested, with the aim of finding a small and efficient set of adjustable parameters. Alternatively, the total optimization potential approach (Stalidzans et al, 2017b) or evolutionary search strategies may be applied.…”

Section: Discussionmentioning

confidence: 99%

“…The total optimization potential (TOP) approach (Stalidzans et al, 2017b) is used to assess the biotechnological potential of particular AP combination. TOP is OF value of full set of AP.…”

Section: Model Optimization Software and Task Settingmentioning

confidence: 99%

“…Several industrial applications have demonstrated the potential of a model-based design approach (Nielsen et al, 2014). At the same time, it is important to determine the smallest number of modifications that have the largest impact on strain improvement (Mozga and Stalidzans, 2014;Nikolaev, 2010;Rodrıǵuez-Acosta et al, 1999;Sendín et al, 2010;Stalidzans et al, 2017b;Stephanopoulos and Simpson, 1997). A small number of manipulated parameters is advantageous for reducing risks of both unexpected side-effects and costs of model-based design implementation in vivo.…”

Section: Introductionmentioning

confidence: 99%

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Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model

et al. 2017

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The application of biologically and biochemically relevant constraints during the optimization of kinetic models reduces the impact of suggested changes in processes not included in the scope of the model. This increases the probability that the design suggested by model optimization can be carried out by an organism after implementation of design in vivo. A case study was carried out to determine the impact of total enzyme activity and homeostatic constraints on the objective function values and the following ranking of adjustable parameter combinations. The application of constraints on the model of sugar cane metabolism revealed that a homeostatic constraint caused heavier limitations of the objective function than a total enzyme activity constraint. Both constraints changed the ranking of adjustable parameter combinations: no "universal" constraint-independent top-ranked combinations were found. Therefore, when searching for the best subset of adjustable parameters, a full scan of their combinations is suggested for a small number of adjustable parameters, and evolutionary search strategies are suggested for a large number. Simultaneous application of both constraints is suggested.

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Section: Discussionmentioning

confidence: 99%

“…The total optimization potential (TOP) approach (Stalidzans et al, 2017b) is used to assess the biotechnological potential of particular AP combination. TOP is OF value of full set of AP.…”

Section: Model Optimization Software and Task Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model

et al. 2017

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“…It can be applied both for kinetic and stoichiometric models. In case of kinetic models, the comparison of optimization results for a different number of adjustable parameters may be required [ 35 ]. The optimization runs for many adjustable parameter combinations and can be done also in an automated way [ 36 ].…”

Section: Organism-level Constraintsmentioning

confidence: 99%

Model-based metabolism design: constraints for kinetic and stoichiometric models

Stalidzāns

Seiman

Peebo

et al. 2018

Biochemical Society Transactions

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The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.

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“…Still, in many cases the engineering ideas are generated intuitively keeping in mind a specific organism and product, and the engineering process is built around that idea. To find the full scope of optimisation potential offered by available technologies, a model-based screening of solution space and ranking of designs according to the selected criteria is necessary [12] .…”

Section: Introductionmentioning

confidence: 99%

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

Stalidzāns

Dāce

2021

Computational and Structural Biotechnology Journal

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Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes. In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables ab initio improvement of the biotechnological production design.

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Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network

Cited by 6 publications

References 32 publications

Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model

Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model

Model-based metabolism design: constraints for kinetic and stoichiometric models

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

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