Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling

Costanza, Jole; Carapezza, Giovanni; Angione, Claudio; Lió, Píetro; Nicosia, Giuseppe

doi:10.1007/978-3-642-33636-2_9

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…However, elitism preserves the best solution irrespective of its age. Finally, remaining solutions of the population are assigned rank and sorted as per Pareto-front-selection operation [8].…”

Section: Multi-objective Immunological Algorithm (Optia-ii)mentioning

confidence: 99%

Design and characterization of effective solar cells

et al. 2021

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We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

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Section: Multi-objective Immunological Algorithm (Optia-ii)mentioning

confidence: 99%

Design and characterization of effective solar cells

et al. 2021

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“…Several methods have been developed to predict behaviour over time [dynamic FBA (Varma and Palsson, 1994) and dynamic FVA (Maldonado et al, 2018)], integrate regulation [regulatory FBA (Covert et al, 2001)] and integrate other data types [integrated FBA (Covert et al, 2008) and integrated dynamic FBA (Lee et al, 2008)]. Furthermore, other CBM methods and simulators have been developed to expand their applications, e.g., multi-objective function analyses (Costanza et al, 2012; Zakrzewski et al, 2012), whole human cell metabolic analyses (Fisher et al, 2013), integrate multiple simulation formats (Liao et al, 2012; Wu et al, 2016; Heirendt et al, 2017), of which all could be adapted for use in future primary mitochondrial research.…”

Section: Challenges In the Systems Understanding Of The Mitochondrionmentioning

confidence: 99%

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

et al. 2019

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Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, “bottom-up” systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

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“…One of the development is multiobjective optimization that produces a set of non-dominated solutions between two competing objectives such as production rate and growth rate [ 13 ], [ 14 ], [ 15 ], [ 16 ]. Several methods have been developed to solve the issues of competing objectives, including Linear Physical Programming based Flux Balance Analysis (LPPFBA), Noninferior Set Estimation (NISE) with FBA, Genetic Design through Multi-objective Optimisation (GDMO) and others [ 17 ], [ 18 ], [ 19 ]. The advantages of these methods are the decision-makers, which are industrialists or biologists, may have various solutions instead of one single solution.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

Lee

Mohamad

Choon

et al. 2020

Journal of Integrative Bioinformatics

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The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

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Multi-objective Optimisation, Sensitivity and Robustness Analysis in FBA Modelling

Cited by 9 publications

References 30 publications

Design and characterization of effective solar cells

Design and characterization of effective solar cells

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

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