3. Who we are
Professor
Associate Prof in the ECE department
Artificial Intelligence and Synthetic Biology
Lance
Lafontaine
3rd year molecular biology student
Member of iGEM’13
Anas
Nawwaf Kharma
Ambri
3rd year Computer Engineering student
5. Background
Synthetic
biology is growing rapidly
Synthetic biology is the design and
construction of biological devices and
systems for useful purposes.
Glowing plants: http://tinyurl.com/me5zn7j
6. Cellular automata
A
cellular automaton consists of a regular
grid of cells, each in one of a finite
number of states, such as on and off
A cell’s next state is a function of its
neighbors’ current state
This function defines rules, and some of
these rules are Turing-complete
7. Conway’s game of life
Simple
rules, simulating
over- and underpopulation
Easily programmable
9. The Biology Behind
Comput E.Coli
Four biological problems:
Clocking
the Cells
Predictable Cell Communication
Implementing a Biological Logic Function
Introducing a Cellular Memory
11. Clocking Cells
Negative feedback loops cycles
gas synthesis and perception: an
autonomous biological clock!
Our Novel Protein:
E.coli compatible Ethylene
Inducible Histidine Kinase
(or EEHK)
Ethylene Biosynthesis
12. Predictable Cell
Communication
Problem:
Cell colonies can’t tell their right
neighbor from their left or from
themselves.
Solution: Staggering unique AHL signals in
triplet to achieve a bacterial perceived
sense of directionality.
15. Implementing a Biological
Logic Function
We designed a pair of synthetic ribozymes: pieces of RNA that
destroy a target individually, but not when together… An XOR Gate!
16. Introducing a Cellular Memory
A previous iGEM team designed this part for use in a completely
different project. We modified it slightly to serve our purpose: it allows
for retention of exposure to specific proteins, turning it ON or OFF.
17. Why Synthetic Biology is
Awesome
All
parts designed and used are
completely modular. Anyone can stick
these into E.coli or another organism and
expect the same result every time.
Easily engineering an organism that will
perform tasks to better serve mankind.
19. Teams
“Wet
lab” team
Constituted mostly
of biology students
Modeling specialist
Web designer
Software
team
Constituted mostly
of engineers
Projects very
diverse
Provides support to
the wet lab team
20. Software projects
AI
to simulate and generate genetic
circuits
Genetic circuits are hard. Software to the
rescue!
DNA
annealing to solve NP hard problems
Traveling salesman problem solved through
DNA annealing
Puzzle-edge detection not solved - yet
21. Ultimate goals
Participating
at the iGEM finals at MIT
200+ teams expected in November 2014
Chance to compete in the very soughtafter software track
Learning
about a very cool domain
22. To participate
Send your resume to
igem.concordia@gmail.com
Explain what you would be most interested in
Modeling
Graphic design
Front-end
Backend
Algorithms
Expect to learn a lot
23. After
Once
an idea for the wet lab team is
decided:
Brainstorming of ideas
Creation of teams
You get to choose what to work on
Active
development to start in May
Projects to run throughout the summer
