DescriptionResearch topic - Current challenges in modeling cellular metabolism
About this Research Topic
Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms ranging from microbes to human cells. These have been used for multiple applications within biomedical research and industrial biotechnology.
Despite these advancements, there are still major challenges in the modeling of cellular metabolism. This requires a community effort in order to overcome some of the current obstacles. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. This topic is divided into four main cornerstones that build this frontier:
(1) Integration of modeling approaches:
Mainly addressing the convergence of different modeling directions, with a focus on the conciliation between detail and scalability through integration of fine-grained (e.g: kinetic) and large-scale (e.g: constraint-based) models.
(2) Integration of heterogeneous data sources:
Coping with the increasing availability of experimental data, through the development of methods for integration of multiple omics (e.g: transcriptomics, proteomics, metabolomics) into large-scale metabolic models.
(3) Integration with other biological processes:
Metabolism provides the energy and building blocks to multiple cellular processes, and is also tightly connected with gene regulation. The integration of metabolic and other biological networks is fundamental for a true systems-level understanding of metabolism.
(4) Model standardization:
Widely adopted standards such as SBML have facilitated sharing and comparison of models, although this process can still hampered by lack of consensus in the adoption of nomenclatures and identifiers. Furthermore, these standards will need to evolve in order to accommodate the next generation of models that will couple metabolism and the cellular machinery.
Authors are encouraged to submit original research work, short reviews, and opinion articles focusing on any of the subtopics described above.
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