Flux balance analysis of primary metabolism in the diatom <i>Phaeodactylum tricornutum</i>

Kim, Joomi; Fabris, Michele; Baart, Gino; Kim, Min Kyung; Goossens, Alain; Vyverman, Wim; Falkowski, Paul G.; Lun, Desmond S.

doi:10.1111/tpj.13081

Cited by 75 publications

(75 citation statements)

References 86 publications

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“…Currently, the models accurately predict growth with hundreds of different nitrogen and carbon sources [30, 32]. Furthermore, most models are able to reproduce more than one growth mode, including mixotrophy in the models i CZ843 [32], i CZ946 [33], i RJ1321 [38], i RC1080 [26], AlgaGEM [25], i NS934 [37], i LB1027 [53], and a model for Nannochloropsis sp. [36].…”

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

“…Experimentally, highest biomass yields have been reported for autotrophic conditions, while lowest were obtained under heterotrophic growth in P. tricornutum [53], Synechocystis sp. [39], C. reinhardtii [23], and Chlorella sp.…”

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

“…So, it is crucial to determine and adjust these constraints if needed for each growth condition. For example, under autotrophic growth the biomass yields have been reported to be close to 100%, since no carbon is lost in the process [23, 39, 44, 53, 79], thus experimental measurements can be used directly. On the other hand, under heterotrophic growth conditions a significant carbon loss as CO 2 in oleaginous microalgae has been reported to vary between 37% [39] to 40% for Synechocystis sp.…”

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

“…[40], 50% for C. reinhardtii [23] and 50.5% for Chlorella sp. [79] as a result of the carbon input flux being lost as CO 2 due to energy production through the TCA cycle and the oxidative pentose phosphate pathway (PPP) [23, 39, 44, 53, 79]. Mixotrophic biomass yields tend to be higher than under heterotrophy, since part of the released CO 2 is fixed once again [39].…”

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

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Advances in metabolic modeling of oleaginous microalgae

Tibocha‐Bonilla

Zuñiga

Godoy-Silva

et al. 2018

Biotechnol Biofuels

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Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.

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Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

Section: Lessons Learned From Metabolic Modeling Of Oleaginous Phototmentioning

confidence: 99%

See 2 more Smart Citations

Advances in metabolic modeling of oleaginous microalgae

Tibocha‐Bonilla

Zuñiga

Godoy-Silva

et al. 2018

Biotechnol Biofuels

View full text Add to dashboard Cite

show abstract

“…For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17].…”

mentioning

confidence: 99%

Of (Biological) Models and Simulations

Ling¹

2016

MOJPB

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All models are wrong, but some are useful. Box and Draper [1].All models are right… Most are useless. Tarpey [2]The role of models has been recognized as an important aspect to the scientific method [3] for more than 70 years [4]. In recent years, there are increasing interests in metabolic modeling in biology [5,6]. The process of building a model is known as modeling. Generally speaking, models can be classified as either material models or formal models. Rosenblueth & Wiener [7] consider material models to be a physical representation of the object under study whereas formal models are symbolic or logical representations of the object under study. For example, a scale down model of an airplane for testing its aerodynamic properties in a wind tunnel is a material model. The main purpose of a scaleddown material model is the ease of study, which had been clearly demonstrated by a study on fireplace more than 200 years ago [8].A formal model, on the other hand, is an abstract but unambiguous description of a physical object or phenomenon, usually in logical or mathematical constructs. Commonly used mathematical constructs for building formal models are rate equations, such as differential equations [9], or state transitions, such as petri nets [10]. Other modeling formalisms had been reviewed by Machado [11]. Besides differentiating models by their underlying formalism, models can also be classified by their informational complexity. At the lowest level, the presence or absence of associations between 2 entities (such as cells or molecules) can be represented as a Boolean term. This results in a binary state petri net or Boolean network, which can be used to study cycles and reachability. For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17]. The main advantage of kinetic models over steady state model is the ability to simulate each state (or molecules in the context of metabolic modeling) across time, in addition to the ability to model homeostasis [12]. This will allow for identification of bottlenecks in the metabolism [18].With advancements in computing power, formal models, being constructed in mathematical or logical format, render it possible for computation. Such computation or execution of formal models is commonly known as simulation. Hence, by definition, one can "build a model", then "run or execute a model" or "run or execute a simulation" but in no means, one can "build a simulation". The ability to render execution of a model; thus, an executable or simulatable model; defines the fundamental difference between a material model an...

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