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Title:
Building a Digital Twin for Rheumatoid Arthritis one cell at a time

Authors:
Anna Niarakis

Abstract:
Rheumatoid arthritis is an autoimmune disorder that triggers a multifaceted joint inflammation. Conventional antibody treatments prove ineffective in approximately 40% of cases. Our work focuses on constructing a dynamic ‘digital joint,’ aiming at simulating the disease’s progression, diverse treatment responses, and the potential risks associated with novel pharmaceutical therapies (1). Leveraging bulk and single-cell omics data, logic-based and data-driven modelling techniques, we have constructed a multicellular joint representation, intricately capturing the interactions between immune and resident cells, inflammation processes, and cartilage integrity (2-6). Through collaboration with Sanofi R&D, we have successfully built one of the most comprehensive multicellular models (>1000 biomolecules), which is currently undergoing peer review. The multicellular model for the RA joint comes after many years of work on modelling RA, which include the unveiling of metabolic reprogramming within affected cells (7-8) and even identifying promising therapeutic targets and combinations in RA fibroblasts (9) and macrophages (10). Ongoing efforts aim to enrich the RA model with more cell types by developing an agent-based model and methods to correlate in silico data with actual patient imaging, providing a critical bridge between simulated models and real-world clinical observations.

1. Laubenbacher, R., Niarakis, A., Helikar, T. et al. Building digital twins of the human immune system: toward a roadmap. npj Digit. Med. 5, 64 (2022).
2. Zerrouk, N., Aghakhani, S., Singh, V., Augé, F. & Niarakis, A. A mechanistic cellular atlas of the rheumatic joint. Front. Syst. Biol. 2, 925791 (2022).
3. Singh V, Ostaszewski M, Kalliolias GD, Chiocchia G, Olaso R, Petit-Teixeira E, Helikar T, Niarakis A. Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model. Genom Comput Biol. 2018;4(1):e100050.
4. Aghamiri SS, Singh V, Naldi A, Helikar T, Soliman S, Niarakis A. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics. 2020 Aug 15;36(16):4473-4482.
5. Zerrouk, N., Miagoux, Q., Dispot, A., Elati, M. & Niarakis, A. Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference. Sci. Rep. 10, 16236 (2020).
6. Quentin Miagoux, Vidisha Singh, Dereck de Mézquita, Valerie Chaudru, Mohamed Elati, Elisabeth Petit-Teixeira, Anna Niarakis, Inference of an integrative, executable network for rheumatoid arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map. J. Pers. Med. 11, 785 (2021).
7. Aghakhani S, Soliman S, Niarakis A. Metabolic reprogramming in Rheumatoid Arthritis Synovial Fibroblasts: A hybrid modeling approach. PLoS Comput Biol. 2022 Dec 12;18(12):e1010408.
8. Aghakhani S, Zerrouk N, Niarakis A. Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches. Cancers. 2021; 13(1):35.
9. Singh, V., Naldi, A., Soliman, S., Niarakis, A. A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint. npj Syst Biol Appl 9, 33 (2023).
10. Zerrouk N, Alcraft R, Hall B, Auge F, Niarakis A, Large-scale computational modelling of the M1 and M2 synovial macrophages in Rheumatoid Arthritis, npj Syst Biol Appl accepted