The normalized normal constraint method for generating the Pareto frontier

Messac, Achille; Ismail-Yahaya, Amir; Mattson, Christopher A.

doi:10.1007/s00158-002-0276-1

Cited by 653 publications

(505 citation statements)

References 14 publications

Supporting

Mentioning

497

Contrasting

Unclassified

Order By: Relevance

“…The change in Pareto curve at high-albumin synthesis also caused flux changes (Fig. 10b), including a moderate increase in gluconeogenic fluxes (2-6), TCA cycle fluxes (8,13), urea cycle fluxes (14,15) and urea secretion (16), a significant increase in b-oxidation (40), electron transport (43, 44), lipid storage (57), NADPH (46) and GSH (48) synthesis, significant decrease in lipid uptake (52) and albumin synthesis (47). Additionally, there was a decrease in uptake of both gluconeogenic and ketogenic amino acids.…”

Section: Casementioning

confidence: 99%

“…At the anchor point of ATP generation on the Pareto curve, flux changes caused by introduction of the measurements (Fig. 10d) significantly increased gluconeogenesis fluxes (2-6), urea cycle fluxes (14,15), and GSH (48) synthesis, moderately increased TCA cycle flux (8), urea secretion (16), and b-oxidation (40), decreased electron transport (44), lipid uptake (52), albumin synthesis (47), and NADPH (46). Additionally, there was decreased uptake of both gluconeogenic and ketogenic amino acids.…”

Section: Casementioning

confidence: 99%

“…5b and Table 5. There was a marginal increase in urea cycle fluxes (14)(15), a decrease in lipid uptake (52) and lipid stored (57), and a significant increase in aspartate uptake (69). Additionally, the uptake of both gluconeogenic amino acids (60, 67, 69, 71, 73, 76, 77, 79, and 81) and ketogenic amino acids (72, 78) increased.…”

Section: Casementioning

confidence: 99%

“…At high-albumin secretion, the associated flux differences (Fig. 10c) included a significant increase in gluconeogenic fluxes (2-4), TCA cycle flux (8), and GSH (48) synthesis, a moderate increase in b-oxidation (40), a decrease in urea cycle fluxes (14,15), urea secretion (16), lipid uptake (52) and albumin (47), a moderate decrease in electron transport (44) and NADPH (46) synthesis. At the anchor point of ATP generation on the Pareto curve, flux changes caused by introduction of the measurements (Fig.…”

Section: Casementioning

confidence: 99%

“…The class of Generate First-Choose Later (GCFL) multi-objective optimization approaches entails first generating a representative set of Pareto solutions, and then choosing the most suitable and appropriate solution within this set. The Normal Constraint (NC) method, 15 unlike other popular methods such as the Normal Boundary Intersection (NBI) method, can generate complete Pareto frontiers for multi-objective problems from the full range of Pareto bi-objective solutions. The NC method essentially generates an even distribution of Pareto points throughout the complete Pareto frontier; and it is guaranteed to yield any Pareto point in the feasible design space.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

et al. 2007

View full text Add to dashboard Cite

Abstract-Flux balance analysis (FBA) provides a framework for the estimation of intracellular fluxes and energy balance analysis (EBA) ensures the thermodynamic feasibility of the computed optimal fluxes. Previously, these techniques have been used to obtain optimal fluxes that maximize a single objective. Because mammalian systems perform various functions, a multi-objective approach is needed when seeking optimal flux distributions in such systems. For example, hepatocytes perform several metabolic functions at various levels depending on environmental conditions; furthermore, there is a potential benefit to enhance some of these functions for applications such as bioartificial liver (BAL) support devices. Herein we developed a multi-objective optimization approach that couples the normalized Normal Constraint (NC) with both FBA and EBA to obtain multi-objective Pareto-optimal solutions. We investigated the Pareto frontiers in gluconeogenic and glycolytic hepatocytes for various combinations of liver-specific objectives (albumin synthesis, glutathione synthesis, NADPH synthesis, ATP generation, and urea secretion). Next, we evaluated the impact of experimental flux measurements on the Pareto frontiers. We found that measurements induce dramatic changes in Pareto frontiers and further constrain the network fluxes. This multi-objective optimality analysis may help explain certain features of the metabolic control of hepatocytes, which is relevant to the response to hepatocytes and liver to various physiological stimuli and disease states.

show abstract

Section: Casementioning

confidence: 99%

Section: Casementioning

confidence: 99%

Section: Casementioning

confidence: 99%

Section: Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

et al. 2007

View full text Add to dashboard Cite

show abstract

A multiobjective short‐term optimal operation model for a cascade system of reservoirs considering the impact on long‐term energy production

Zhong

Stanko

et al. 2015

Water Resources Research

View full text Add to dashboard Cite

This paper examines the impact of short-term operation on long-term energy production. We propose a multiobjective optimization model for the short-term, daily operation of a system of cascade reservoirs. The two objectives considered in the daily model are: (1) minimizing the total amount of water released and (2) maximizing the stored energy in the system. Optimizing short-term operation without considering its impact on long-term energy production does not guarantee maximum energy production in the system. Therefore, a major goal of this paper is to identify desirable short-term operation strategies that, at the same time, optimize long-term energy production. First, we solve the daily model for 1 month (30 days) using a nondominated genetic algorithm (NSGAII). We then use the nondominated solutions obtained by NSGAII to assess the impact on long-term energy production using a monthly model. We use historical monthly inflows to characterize the inflow variability. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) in average hydrology scenarios, the solution maximizing stored energy produces the most overall long-term energy production; (2) in moderately wet hydrology scenarios, the solution minimizing water released outperforms the maximizing stored energy solution; and (3) when extremely wet hydrology scenarios are expected, a compromise solution is the best strategy.

show abstract

Bibliography

2017

Evolutionary Algorithms

View full text Add to dashboard Cite

The normalized normal constraint method for generating the Pareto frontier

Cited by 653 publications

References 14 publications

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

A multiobjective short‐term optimal operation model for a cascade system of reservoirs considering the impact on long‐term energy production

Bibliography

Contact Info

Product

Resources

About