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Programming Biological Cells Ron Weiss, George Homsy, Radhika Nagpal Tom Knight, Gerald Sussman, Nick Papadakis MIT Artificial Intelligence Laboratory Motivation Goal: program biological cells Characteristics small (E.coli: 1x2m , 109/ml) self replicating energy efficient Potential applications “smart” drugs / medicine agriculture embedded systems Approach high-level program logic circuit genome microbial circuit compiler Outline: Building biological digital circuits compute, connect gates, store values High-level programming issues Compute: Biological Inverter signal = protein concentration level computation = protein production + decay [A] [A] = 0 [Z] = 1 [Z] [A] = 1 [Z] = 0 Z A Inverter Behavior Simulation model based on l phage biochemistry [A] [ active gene ] [Z] time (x100 sec) Connect: Ring Oscillator Connected gates show oscillation, phase shift [A] [B] [C] time (x100 sec) Memory: RS Latch _ R = A _ S B _ [R] _ [S] [B] [A] time (x100 sec) Microbial Circuit Design logic circuit genome microbial circuit compiler protein DB Assigning proteins is hard. BioSPICE: Simulate a colony of cells BioSPICE BioSPICE Prototype protein level simulator intracellular circuits, intercellular communication protein concentration Simulation snapshot cell High Level Programming Requires a new paradigm colonies are amorphous cells multiply & die often expose mechanisms cells can perform reliably Microbial programming language example: pattern generation using aggregated behavior Conclusions + Future Work Biological digital gates are plausible Now: Implement digital gates in E. coli Also: Analyze robustness/sensitivity of gates Construct a protein kinetics database Study proteinprotein interactions for faster logic circuits