Our Innovation

We model the Biological world of DNA, RNA and Protein with Cellular Automata (CA). CA was pioneered by father of modern Computing, Jon von Neumann and our founder is a renowned researcher in the field.

Why are we Different?

Molecular Dynamic (MD) simulation has been the de-facto "bottom-up" framework to model interacting forces at atomic/molecular level. It has to handle exponential complexity associated with many-body problem. Cost and time associated is high and hence limited to local interactions rather than running comprehensive system level simulations. Many statistical methods also have been used for Genomics and Preotemics with some success.

Another relatively new approach is with Artificial Intelligence (AI)/Machine Learning (ML). The "top-down" AI/ML approach is in sharp contrast to the historic "bottom-up" modeling. Unfortunately, to identify a consistent pattern, voluminous data is required and biological data is still atypical for AI/ML, too sparse and incomplete, too biased and too noisy!

We have taken the best of both worlds - "bottom-up" modeling and with "top-down" algorithmic mapping! It uses the information of the building blocks of the bio-molecules, but abstracts the information in a CA model. It is hence super-fast, runs on a normal desktop computer and can model much more complex systems - like interaction of a protein with a macro-molecule. Our need for prior data is much more restricted and constrained compared to AI/ML approach. It just needs enough information to map it to the underlying model based on information retrieved from physical (atom, molecules, side chain, backbone) domain.