In 2000, a collaborative effort of biologists and computer scientists culminated in one of the key scientific achievements of the century—the complete sequencing of the human genome. Now researchers have embarked on a far grander research challenge: to understand the vast information processing system that makes up an organism.
Encoded in an organism's genome is all the information needed to build it from a single cell and then maintain it over time. This master instruction set is carried out via proteins that circulate and interact with other proteins, creating complex biological networks. EECS researchers, together with biologists, are trying to understand these networks and the flows of information within them and between them. "EECS is about systems-style thinking, and that's what's needed," says Berkeley EECS Professor Michael Jordan. "There are many different levels of scale, but they all interrelate—that's what leads to life."
At Berkeley, EECS faculty are engaged in several efforts to better understand the algorithms of living systems. Jitendra Malik, who specializes in computer vision, is using pattern-recognition tools to solve a key mystery of life: how embryonic cells, all of which contain the same instruction set, evolve into cells that perform vastly different functions. Jose Carmena is trying to understand and exploit the power of a particularly important cellular network, the brain. He develops brain-machine interfaces—devices that process brain signals and use them to operate computers or prosthetic limbs. Ming Wu has developed a sophisticated tool that enables researchers to move large numbers of cells with precision. And in separate projects, Jordan and Richard Karp are using combinatorial methods and tools of statistical inference to understand protein functions and interactions.
"EECS has all the right tools to understand complex biological processes," says Wu. "It has the optical engineering skills, the control, the pattern recognition, and the virtual reality, as well as the algorithmic tools to analyze data and interpret results."