The Consortium for Cell-Inspired Systems Engineering

  "When you want to know how things really work, study them when they're coming apart.”                                                                                                                                        - William Gibson

The Consortium for Cell-Inspired Systems Engineering (ConCISE) is building a bridge between the life sciences and computer engineering to develop a novel paradigm for enhancing artificial intelligence (AI). As the iNetMed’s engineering arm, ConCISE is building virtual human cells to better understand how they degenerate or go rogue, to determine how we can manipulate cellular communication, as well as engineer smarter machines that can sense, decide, act, learn, and adapt – the way cells do. 

The Human Cell

Although scientists who are engaged in shepherding the march of the artificial neural networks (ANN), the most popular of AI methodology, have achieved high processing speed, accuracy, and performance, existing ANNs have limitations.  Simulating human decision-making and cognition remains unconquered, and the ‘gold-standard’ of automation, i.e., level-5, in which machines function without human intervention, remains imperfect. When it comes to making ‘smarter’ networks, size alone does not matter, even enormous networks (137 billion neurons and larger) cannot replicate human intelligence and judgment. 

ConCISE seeks to decode natural intelligence. While most computer science researchers focus on neural networks that mimic the brain, our scientists draw inspirations from a different biological machine – a single human cell, the veritable computational unit of life -- to innovate a radically different paradigm for AI. Cells routinely demonstrate innate intelligence: automatically sensing their environment, interpreting that information, deciding what to do next, and acting on those decisions.   

Understanding Disease Pathways

Working closely with UC San Diego’s Institute of Engineering and Medicine (IEM) and others, ConCISE scientists and engineers seek to enrich the neural network.  Rather than increasing the number of computational units, ConCISE will give each computational unit the features associated with cellular decision-making; noise attenuation, high efficiency, reduced trade-offs between robustness and flexibility, ultra-sensitivity, and adaptability. ConCISE researchers have uncovered fundamental biological circuits that make cells both flexible and decisive and are taking their cues from both healthy and diseased human cells.  Consider how a tumor responds to the most toxic therapies, constantly testing new ways to avoid treatment and survive.  This is more than just a random event, it’s a robust and adaptable system.  The organizing principles behind this natural intelligence remain a mystery.  However, ConCISE is interpreting these rules to capture them in silico, empowering us to simulate and predict cell behavior and develop more advanced AI networks. 

Artificial Intelligence

While deciphering cellular intelligence, ConCISE researchers have stumbled upon fundamental biological circuits that make cells both flexible and decisive. This unique circuitry follows well-known computing principles, such as control theory and feedback regulation, like TCP/IP networks, which control an individual computer’s connection to the internet.

ConCISE is translating insights into the workings of protein switches and signaling circuits within our cells into mathematical equations to create an alternative paradigm for the development of AI methodologies. On the flip side, these AI methodologies will be essential to solving many of the big data biology problems iNetMed is now tackling. This work is helping ConCISE researchers understand disease pathways that could eventually lead to new therapies for many incurable conditions.