Programming Biological Cells Ron Weiss, George Homsy, Radhika Nagpal Tom Knight, Gerald Sussman, Nick Papadakis MIT Artificial Intelligence Laboratory
Dec 20, 2015
Programming Biological Cells
Ron Weiss, George Homsy, Radhika Nagpal
Tom Knight, Gerald Sussman, Nick PapadakisMIT Artificial Intelligence Laboratory
Goal: program biological cells
Characteristics small (E.coli: 1x2m , 109/ml)
self replicating
energy efficient
Potential applications “smart” drugs / medicine
agriculture
embedded systems
Motivation
Approach
Building biological digital circuits compute, connect gates, store values
High-level programming issues
Outline:
logiccircuit
microbialcircuit
compiler
genomehigh-levelprogram
Compute: Biological Inverter
signal = protein concentration level computation = protein production + decay
[A][Z]
= 0= 1
[A] [Z]
[A][Z]
= 1= 0
Z
A
activegene
Inverter Behavior
Simulation model based on phage biochemistry
[A]
[Z]
[ ]
time (x100 sec)
Connect: Ring Oscillator
Connected gates show oscillation, phase shift
time (x100 sec)
[A]
[C]
[B]
B_S
_R
Memory: RS Latch
time (x100 sec)
_[R]
[B]
_[S]
[A]
=A
Microbial Circuit Design
Assigning proteins is hard. BioSPICE: Simulate a colony of cells
logiccircuit
microbialcircuit
compiler
genome
protein DBBioSPICE
BioSPICE
Prototype protein level simulator intracellular circuits, intercellular
communication
Simulationsnapshot
cell
proteinconcentration
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