Download Programming Biological Cells

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: 1x2m , 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 proteinprotein interactions for faster logic
circuits