Following its first edition in 2023 (BIDT2023), the second “Building Immune Digital Twins” symposium will take place at Institut Pascal, on the Paris-Saclay campus, from May 24 to June 4, 2027. It will bring together international experts in immunology, computational modelling, artificial
intelligence, clinical medicine and drug development to advance digital twins of the human immune system.
The programme will focus on modelling inflammatory responses in physiological and pathological contexts, such as sepsis, autoimmune diseases, cancer and vaccination.
Through lectures, brainstorming discussions, hackathons and collaborative project sessions, participants will explore how mechanistic and data-driven approaches can be combined to develop an
inflammation-focused immune digital twin prototype. Clinicians, academic researchers and industry representatives will jointly address implementation, applicability and scalability of a general immune system model, as well as more focused versions representing innate immune
mechanisms in specific disease areas.
By strengthening interdisciplinary partnerships, the symposium aims to establish a collaborative framework for designing immune digital twins to support drug discovery and therapeutic development while enabling patient-specific predictions to guide the management
of immune-mediated disorders.
ORGANISERS :
Dr Anna Niarakis is a Full Professor of Computational Systems Biology at the University of Toulouse, head of the Computational Systems Biology Hub, at the Center of Integrative Biology, CNRS/UT. Dr Niarakis has a broad scientific background in Biochemistry, Biology, and Pharmaceutical Technology, and completed postdoctoral studies in Computational Systems Biology and Bioinformatics (ENS, Paris; Institut Gustave Roussy), with expertise in complex human diseases, especially autoimmune diseases. She is the Disease Maps consortium co-leader coordinating the COVID-19 Disease Map initiative. She leads the Working Group “ Building Immune Digital Twins”, supported by the Research Data Alliance -Europe. Dr Niarakis has significant expertise in academic teaching and mentoring, course design and management, and is active in organizing and participating in international summer and winter schools of Computational Systems Biology, such as the Wellcome Trust Advanced Course "Computational Systems Biology for Complex Human Disease: From Static to Dynamic Representations of Disease Mechanisms."
Dr Philippe Moingeon received his PhD in immunology from Paris XI University and an MBA from Open University Business School (Newcastle, UK). As a former post-doctoral fellow and Assistant Professor at the Dana Farber Cancer Institute, Department of Pathology, Harvard Medical School (Boston, USA), he has extensive experience in the development of biologicals. He held several senior positions in the vaccine industry, including Head of the Cancer Vaccine Program, General Secretary for Research and Development at Aventis Pasteur, and Senior Vice President of Research and Pharmaceutical Development at Stallergenes. He joined Servier in 2017 to lead the Therapeutic area of immuno-inflammation. He is an adjunct professor of AI in health at Université Paris-Saclay and a French Academy of Pharmacy member. With 200+ publications in peer-reviewed journals, his scientific interests in the last seven years have been related to AI and computational modelling supporting drug development, with applications to autoimmune, inflammatory and neurological diseases.
Dr Gary An is the Green and Gold Professor of Trauma and Critical Care Surgery, Surgery and Vice-Chairman for Surgical Research in the Department of Surgery at the University of Vermont Larner College of Medicine. He is a clinically active trauma/critical care surgeon who has worked since 1999 on applying complex systems analysis, agent-based modelling and in silico trials to study sepsis, inflammation, wound healing, host-pathogen interactions and cancer. His work consists of developing multiscale computer simulations of biological systems and integrating machine learning and artificial intelligence with such models to represent the individual diversity within clinical populations for the discovery and development of therapeutic control modalities. As many of these methods apply to the translation of the digital twin concept from industry to the medical arena, he has been very active in developing methods for creating mechanism-based medical digital twins to fulfill the promise implied by the use of the term.