ProjectRecon4IMD – Reconstruction and Computational Modelling for Inherited Metabolic Diseases
Basic data
Acronym:
Recon4IMD
Title:
Reconstruction and Computational Modelling for Inherited Metabolic Diseases
Duration:
01/06/2023 to 31/05/2027
Abstract / short description:
Our overall objectives are to accelerate the diagnosis, and enable personalised management, of inherited metabolic diseases (IMDs).
Established academic technology for statistical genomic analysis, deep learning-based prediction of protein structure, and whole-
body metabolic network modelling shall be applied to generate personalised computational models, given patient-derived genomic,
transcriptomic, proteomic and metabolomic data. To train diagnostic models, a comprehensive clinical team will recruit 1,945 diagnosed
patients with a wide variety of IMDs, then validate the clinical utility of personalised computational models on a set of 685 undiagnosed
patients. An enhanced human metabolic network reconstruction, especially for lipid metabolism, reaction kinetics and inherited metabolic
disease pathways, will increase the predictive capacity of cellular and whole-body metabolic network models. As an exemplar for other
IMDs, personalised computational modelling will be used to identify compensatory and aggravating mechanisms that associate with
clinical severity in Gaucher disease. The predictive capacity of personalised models will be validated by comparison with additional
empirical investigations of protein structure and function as well as metabolomics, tracer-based metabolomics and proteomics of patient-
derived in vitro disease models. To maximise the potential for impact, personalised modelling software will be developed to be generally
applicable to a broad variety of IMDs, and implemented in a way that is both accessible to clinicians and admissible to regulatory
authorities. Sustainability will be promoted by development of a roadmap for a European foundation to aid personalised diagnosis and
management of IMDs, informed by broad stakeholder consultation. This is a unique opportunity to realise the potential of personalised
computational modelling for a broad set of rare diseases, which is a field where European collaboration is an essential for progress.
Established academic technology for statistical genomic analysis, deep learning-based prediction of protein structure, and whole-
body metabolic network modelling shall be applied to generate personalised computational models, given patient-derived genomic,
transcriptomic, proteomic and metabolomic data. To train diagnostic models, a comprehensive clinical team will recruit 1,945 diagnosed
patients with a wide variety of IMDs, then validate the clinical utility of personalised computational models on a set of 685 undiagnosed
patients. An enhanced human metabolic network reconstruction, especially for lipid metabolism, reaction kinetics and inherited metabolic
disease pathways, will increase the predictive capacity of cellular and whole-body metabolic network models. As an exemplar for other
IMDs, personalised computational modelling will be used to identify compensatory and aggravating mechanisms that associate with
clinical severity in Gaucher disease. The predictive capacity of personalised models will be validated by comparison with additional
empirical investigations of protein structure and function as well as metabolomics, tracer-based metabolomics and proteomics of patient-
derived in vitro disease models. To maximise the potential for impact, personalised modelling software will be developed to be generally
applicable to a broad variety of IMDs, and implemented in a way that is both accessible to clinicians and admissible to regulatory
authorities. Sustainability will be promoted by development of a roadmap for a European foundation to aid personalised diagnosis and
management of IMDs, informed by broad stakeholder consultation. This is a unique opportunity to realise the potential of personalised
computational modelling for a broad set of rare diseases, which is a field where European collaboration is an essential for progress.
Keywords:
computational modelling
patient stratification
metabolic network
inherited metabolic disease
genomics
Involved staff
Managers
Faculty of Medicine
University of Tübingen
University of Tübingen
Faculty of Medicine
University of Tübingen
University of Tübingen
Contact persons
Faculty of Medicine
University of Tübingen
University of Tübingen
Other staff
Faculty of Medicine
University of Tübingen
University of Tübingen
Faculty of Medicine
University of Tübingen
University of Tübingen
Local organizational units
Institute of Medical Genetics and Applied Genomics
Department of Diagnostic Laboratory Medicine
Hospitals and clinical institutes, Faculty of Medicine
Hospitals and clinical institutes, Faculty of Medicine
Funders
Brüssel, Belgium