LinTlMaT – A novel statistical learning approach for tracing cell lineage

Dr. Hamim Zafar from IIT, Kanpur, in collaboration with researchers from Carnegie Mellon University, has developed a statistical learning method called ‘LinTlMaT’ that can reconstruct cell lineages for an individual organism or at the species-level.

Our body consists of many cells that function continuously to keep us alive. Though the cells in different organs contain almost similar DNA, they perform different functions. Starting as the single-cell they divide over time and ancestor cells give rise to descendant cells forming a lineage tree.

The process of cell transformation from one type to another is called cellular differentiation. Understanding this process is crucial to know what goes on in normal human development process and also what goes wrong in pathologies such as cancer. LinTlMat is the research developed by Dr. Hamim Zafar, to understand this challenging process.

The method and its application on the whole-organism lineage reconstruction have been reported in an article published in the journal Nature Communications. The research team consists of Dr. Hamim Zafar, joint faculty in the CSE and BSBE departments of IIT, Kanpur, Chieh Lin (co-first author in the study), and Professor Ziv Bar-Joseph from Carnegie Mellon University.

“Our methods will have a lot of applications in understanding normal as well as pathological development in different diseases. LinTlMaT will be a powerful method for the biologists who are studying development in model organisms or cancer tissues,” said Dr. Zafar.