2020
DOI: 10.1186/s12859-019-3325-0
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Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina

Abstract: BackgroundThe green microalga Dunaliella salina accumulates a high proportion of β-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data.ResultsThe reconstruction resulted in a network model with 221 reactions and 212 metabol… Show more

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Cited by 159 publications
(51 citation statements)
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“…The secondary structure provided a new set of features and by merging with the original sequence we generated some more features. This technique provided good predictive performance in Zheng et al’s pre-miRNA detection [ 23 ]. The encoding process is shown in Fig 1 .…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The secondary structure provided a new set of features and by merging with the original sequence we generated some more features. This technique provided good predictive performance in Zheng et al’s pre-miRNA detection [ 23 ]. The encoding process is shown in Fig 1 .…”
Section: Methodsmentioning
confidence: 99%
“…CNN has already proven to be useful in computer vision problems. Recently CNN has been producing satisfactory results in nucleotide-based datasets [ 14 , 20 23 ]. In this work, we employed a CNN model where multiple channels of convolution layers with different sized filters are applied separately.…”
Section: Introductionmentioning
confidence: 99%
“…Similar computational predictions are recently under development also for economically relevant algal species. They simulate the complex network of photosynthetic light reactions and their regulation, as well as algal metabolic reactions in different environmental conditions, like illumination regime, nutrient and CO 2 availability (Chang et al, 2011;Du et al, 2018;Perin et al, 2019;Fachet et al, 2020;Toyoshima et al, 2020). Remarkably, robust mathematical models rely on a deep understanding of the cell physiology, to include as many parameters as possible in the simulation.…”
Section: Computational Simulationsmentioning
confidence: 99%
“…Both the second and the third community’s concerns are scientific publications mining. Specifically, the former focuses on the analysis of bibliographical data from scientific publications, while the latter emphasizes on (1) the “knowledge unit”, in terms of Subject-Predicate-Object (SPO) triples extracted from the scientific text (Kilicoglu et al 2020 ), or (2) the “computable knowledge object”, expressed in code such as disease prediction models, learned from big data (Friedman and Flynn 2019 ; Flynn et al 2018 ). The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge at the level of knowledge units (Kilicoglu et al 2012 ).…”
Section: Introductionmentioning
confidence: 99%