How Can Compressive Sensing and Advanced Matrix Factorizations enable Synthetic Biology ?

At the UC Berkeley Conference on Machine-Learning with Real-time and Streaming Applications (videos are here), we have several examples of streaming algorithms dedicated to data that cannot be stored. In the turning point, it was obvious that we were entering a new era in synthetic biology  as we are creating more bio-code and bio-data than we can physically store. Here are some food for thoughts for the week-end including some references/links [1,2,3,4] and how in this field, we are clearly departing from Moore's law (second video). Enjoy!

[1] Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology by Baojun Wang, Richard I Kitney, Nicolas Joly  & Martin Buck

[2] Videos: Group Testing Designs, Algorithms, and Applications to Biology

[3]  It's quite simply, the stuff of Life..

[4] Videos of the UC Berkeley Conference: "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications"


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