Competition/Tracks/Information Processing
Information Processing Track
Information Processing in iGEM covers a diverse range of projects. Like the Foundational Advance track, Information Processing teams are not trying to solve a real world problem with practical applications, but to tackle an interesting problem that might otherwise not attract attention. Teams enter this track if they are attempting projects such as building elements of a biological computer, creating a game using biology or working on a signal processing challenges.
Engineering ways to make biological systems perform computation is one of the core goals of synthetic biology. We generally work at the DNA level, engineering systems to function using BioBricks. In most biological systems, protein-protein interactions are where the majority of processing takes place. Being able to design proteins to accomplish computation would allow for systems to function on a much faster timescale than the current transcription-translation paradigm. These are some of the challenges that face teams entering projects into the Information Processing track in iGEM.
You will find images and abstracts of the winning Information Processing teams from 2013 to 2015 in the page below. Also, follow the links below to see projects from all the Information Processing track teams.
UCFS 2015
Talk Alpha to Me
Cellular communities exhibit both asocial and social behaviors through sensing and secreting the same extracellular molecule, eliciting population-wide behaviors such as quorum sensing, cell differentiation, and averaging. Drawing inspiration from collective behaviors and cellular decision-making in biological systems, our team aims to engineer a synthetic model to understand the factors that play into reshaping community phenotypes. We have engineered novel sense-and-secrete circuits in yeast by repurposing the endogenous mating pathway and using fluorescent reporters to read out individual and community responses to a stimulus. We aspire to understand how intercellular signaling can shepherd noisy individual responses into robust community level behaviors. Particularly, we hope that by tuning parameters such as receptor level, secretion rate, signal degradation, and spatial retention, we will be able to customize communication to model natural systems and elicit distinct community phenotypes.
