Learning to define the dynamic processes that underlie cellular biology presents new avenues for technology development.
Focusing on the principles that drive dynamic cellular processes is a new frontier in science. Not only does it require multidisciplinary expertise, but we are also engaged in developing new technological platforms to advance this field. A prime example is the use of digital images to train machine intelligence to process biological data in new and innovative ways. We envision that machine learning and AI will play a fundamentally important role in creating predictive modelling capability for use in research and in the development of medical interventions.
Software available for download from GitHub:
Microscopy fusion-creating integrated models of cell structure. An R-statistics package script for simulating anatomically realistic distributions of RyR clusters around mitochondria and the contractile machinery.
A physics-based simulation of heart cell structure and function. A set of codes to simulate excitation-contraction coupling in cardiac cells using FE methods. The code uses the OpenCMISS libraries to set up the simulation framework.
Algorithms for automatic segmentation of cardiac cell architectural components from large serial block face imaging (SBF-SEM) datasets.