How Machine Learning is Decoding the Language of Cells to Accelerate Drug Discovery with Samantha Dale Strasser Cellular Intelligence TRANSCRIPT

Samantha Dale Strasser, VP of Strategy at Cellular Intelligence, is developing an AI-native platform to decode how cells communicate and signal with each other using a proprietary capsule technology to collect massive datasets on cellular behavior. The data is used to train functional virtual cell signaling models that predict and control cell fate, accelerating drug discovery and regenerative medicine applications. Currently, Cellular Intelligence is advancing a Parkinson's cell therapy program acquired from Novo Nordisk, which is showing promising safety results.

Samantha explains, "So, big picture at Cellular Intelligence, we're actually driving at understanding the language of cells. So understanding how cells communicate, how they signal, and leveraging that to then understand how not only to understand and predict cell behavior, but also fundamentally control cell fate. And that's critical for drug discovery in patients, as it allows us a unique ability to then treat disease through areas such as design of protocols for regenerative medicine or looking at identification of new targets, prioritizing drugs. It really unlocks a wealth of applications when being able to understand how cells respond to signals."

"Cells themselves, if we take a step back, are the fundamental unit of biology and how we function as a system. And it has been looked at as a critical aspect to tease out and understand. However, the challenge has been really seeing this on a comprehensive scale. If you think about all the different signaling molecules, time dependencies, concentrations, you're left with an immense set of behaviors that can arise. And notably, cells at different states -different snapshots of how they behave - respond to signals differently. So there has been a challenge in the field of truly understanding comprehensively the language of how cells signal and then how to use that effectively for an application within drug discovery."

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