Biological systems must solve problems to survive, and their solutions can be viewed as “algorithms.” Our goal is to uncover these algorithms, translate them to improve computer science, and use them to spark new hypotheses about biological function and dysfunction.
Saket Navlakha’s lab studies “algorithms in nature,” i.e., how collections of molecules, cells, and organisms process information and solve interesting computational problems critical for survival. Indeed, there are many shared goals and constraints faced by biological and engineered systems, including: (1) the use of distributed networks as a backbone for information processing and communication; (2) trade-offs between optimization criteria, including efficiency, robustness, and adaptability; and (3) the need to develop low-cost, scalable solutions that conserve important metabolic or physical resources. An algorithmic perspective on biological problem-solving can lead to two ends: (1) new biological algorithms that are simple, flexible, and robust for use in computer science applications, and (2) quantitative frameworks to predict behavior, raise testable hypotheses, and guide experiments. Our lab has most recently focused on studying neural circuit computation and plant architecture optimization from this perspective.
How to tune out common odors and focus on important ones
May 11, 2020
The fly brain uses a simple computing trick to ignore prevalent odors and focus on newer but rarer odorants.
What Google could learn from a fruit fly
January 21, 2020
By tapping into life’s algorithms, scientists are finding elegant solutions to some of the hardest problems in computer science.
Saket Navlakha taps the power of biology and computers
December 17, 2019
Associate Professor Saket Navlakha is bringing ideas about “algorithms in nature” to the Simons Center for Quantitative Biology.