Accepted_test

Analysis of context-specific genome-scale models for metabolism of fast- and slow-growing chicken breeds

by Kaplan V.S. | Kulyashov M.A. | Volyanskaya A.R. | Yevshin I.S. | Shagimardanova E.I. | Gusev O.A. | Kolpakov F.A. | Akberdin I.R. | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia | Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Science, Novosibirsk, Russia | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia | Regulatory Genomics Research Center, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia | Regulatory Genomics Research Center, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia | Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sirius, Russia

Abstract ID: 515

Event: BGRS-abstracts

Sections: [Sym 2] Section “Reconstruction computational analysis and modeling of gene networks and metabolic pathways”

Synthetic biology, a rapidly advancing field, involves constructing new organisms with specific traits through genome synthesis or deep reorganization using genetic engineering, bioengineering, systems biology and bioinformatics. Systems biology, integrating omics data and metabolic modeling, offers a promising strategy for enhancing biotechnological properties. Genome-scale metabolic models (GEMs) are invaluable for understanding organism metabolism, while context-specific models enable studying metabolism under specific conditions. Despite advancements, the molecular basis of growth differences among chicken breeds remains poorly understood. The main goal of the study is to explore metabolic differences between fast- and slow-growing chicken breeds using context-specific genome-scale models (csGEMs). Our pan-genomic GRCg6a assembly was used as a basis for transcriptomics profiling. We improved the model's quality, rendering it more conducive to biological interpretation, according to Memote test-suite. By integrating transcriptomic data into iES1300 model, we obtaining csGEMs, from which we identified key metabolic reactions and genes linked to growth. Context-specific models of fast-growing muscle tissues exhibited notable disparities from predictions for slow-growing tissues regarding growth rates predicted by the flux balance analysis. Our study elucidated the critical metabolic pathways and regulatory nodes influencing the varying growth rates observed in fast- and slow-growing chicken breeds.