Segarra-Queralt, MariaNeidlin, MichaelTió, LauraMonfort, JordiMonllau García, Juan CarlosGonzález Ballester, Miguel Ángel, 1973-Alexopoulos, Leonidas G.Piella Fenoy, GemmaNoailly, Jérôme2022-10-192022-10-192022Segarra-Queralt M, Neidlin M, Tio L, Monfort J, Monllau JC, González Ballester MÁ, Alexopoulos LG, Piella G, Noailly J. Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments. Sci Rep. 2022 Mar 9;12(1):3856. DOI: 10.1038/s41598-022-07776-22045-2322http://hdl.handle.net/10230/54483In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.application/pdfeng© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41598-022-07776-2BiochemistryComputational biology and bioinformaticsSystems biologyinfo:eu-repo/semantics/openAccess