ANR CPJ : Rupamanjari Majumder (2024-2028)
Rupamanjari Majumder is the scientific leader of this ANR funded project : Heart-by-Numbers: A Digital Platform for Multi-Scale Management of Cardiac Arrhythmias
Cardiac arrhythmias are among the leading causes of sudden cardiac death worldwide. Although numerous genetic, molecular and structural factors have been implicated, understanding how these abnormalities interact across biological scales to generate life-threatening electrical disturbances remains a major challenge. Current experimental and computational approaches typically investigate isolated mechanisms, limiting their ability to connect molecular alterations with whole-heart function and clinical outcomes. Consequently, translating laboratory discoveries into improved diagnosis, risk prediction and therapy remains difficult.
The Heart-by-Numbers (HBN) project aims to address this challenge by developing a comprehensive digital platform that integrates the cardiac electrical conduction system into a physiologically realistic, multi-scale computational model. Combining detailed electrophysiological models with anatomical organization, structural heterogeneity and patient-specific data, HBN will simulate cardiac electrical activity from ion channels to the whole heart and its electrocardiographic (ECG) manifestations. This integrated framework will enable systematic investigation of the mechanisms underlying inherited and acquired cardiac arrhythmias.
A key objective is to bridge experimental and clinical research by generating personalized digital heart models. Experimental observations and clinical electrophysiological and imaging data will be combined with machine learning to identify disease-specific ECG signatures, improve risk stratification, predict disease progression, and support the identification of therapeutic targets.
Beyond disease modelling, HBN will serve as a virtual platform for evaluating innovative treatment strategies. In particular, the project will investigate optopharmacology, an emerging light-controlled approach that modulates ion channel function without genetic modification. By integrating models of drug diffusion, light propagation and cardiac electrophysiology, HBN will enable systematic evaluation of minimally invasive strategies for cardiac rhythm control before experimental or clinical implementation.
By integrating computational modelling, experimental validation and clinical data within a single framework, Heart-by-Numbers will provide a powerful resource for translational cardiac research. The platform is expected to improve mechanistic understanding of arrhythmias, support personalized medicine, reduce reliance on animal experimentation, and accelerate the development of safer and more effective anti-arrhythmic therapies.
Cardiac arrhythmias are among the leading causes of sudden cardiac death worldwide. Although numerous genetic, molecular and structural factors have been implicated, understanding how these abnormalities interact across biological scales to generate life-threatening electrical disturbances remains a major challenge. Current experimental and computational approaches typically investigate isolated mechanisms, limiting their ability to connect molecular alterations with whole-heart function and clinical outcomes. Consequently, translating laboratory discoveries into improved diagnosis, risk prediction and therapy remains difficult.
The Heart-by-Numbers (HBN) project aims to address this challenge by developing a comprehensive digital platform that integrates the cardiac electrical conduction system into a physiologically realistic, multi-scale computational model. Combining detailed electrophysiological models with anatomical organization, structural heterogeneity and patient-specific data, HBN will simulate cardiac electrical activity from ion channels to the whole heart and its electrocardiographic (ECG) manifestations. This integrated framework will enable systematic investigation of the mechanisms underlying inherited and acquired cardiac arrhythmias.
A key objective is to bridge experimental and clinical research by generating personalized digital heart models. Experimental observations and clinical electrophysiological and imaging data will be combined with machine learning to identify disease-specific ECG signatures, improve risk stratification, predict disease progression, and support the identification of therapeutic targets.
Beyond disease modelling, HBN will serve as a virtual platform for evaluating innovative treatment strategies. In particular, the project will investigate optopharmacology, an emerging light-controlled approach that modulates ion channel function without genetic modification. By integrating models of drug diffusion, light propagation and cardiac electrophysiology, HBN will enable systematic evaluation of minimally invasive strategies for cardiac rhythm control before experimental or clinical implementation.
By integrating computational modelling, experimental validation and clinical data within a single framework, Heart-by-Numbers will provide a powerful resource for translational cardiac research. The platform is expected to improve mechanistic understanding of arrhythmias, support personalized medicine, reduce reliance on animal experimentation, and accelerate the development of safer and more effective anti-arrhythmic therapies.
Updated on 23 July 2026.