Developing a Bacterial XOR Gate and Hash Function Hunter iGEM 2012
Project Motivation & Structure ➲ Non-biology majors (including several CS) with an interest in quantitative biology... We wanted to work on biological computing. ➲ We found implementations of classic CS algorithms using bacteria. ➲ Applications for biological computation: Data security, drug delivery, biomedical sensors, etc.
Project Goals ➲ Develop bacterial XOR gate ➲ Integrate XOR gate into combinatorial cir- cuit (hash function) ➲ Model and improve XOR gate designs
What are logic gates? ➲ Computation with machines involves com- binations of basic logical building blocks called logic gates. Here's a XOR gate:
What are hash functions? What are hash functions? A hash function encodes data as other data, opens the door to cryptographic hash func- tions and biologically encoded data.
Bacterial logic gates ➲ Bacterial colonies act as gates or gate components. ➲ Combinatorial logic circuits require signaling and bacterial colonies use quorum sensing to send signals, so they're a natural fit. ➲ Gram negative bacteria use both universal and strain-specific signals (HSL signals).
Signaling paradigms ➲ Signals are passed through environment (agar plate) from colony to colony. ➲ Spatial considerations significant challenge. ➲ Simple signaling versus chained signaling for a mixed system.
Modeling to understand ➲ Using Python we simulated a bacterial XOR hash function and developed kinetic reac- tion model using rule based modeling.
Existing bacterial XOR designs are cumbersome
Evaluating XOR designs ➲ Complex XOR gates with multiple simpler gates ➲ Simpler designs included hybrid promoters which toggle each other ➲ Even simpler is a design exploiting competi- tive polymerase activity with opposing promoters.
We identified an elegant XOR de- sign...
...with a flaw. Bacterial Hash Function Using DNA-Based XOR Logic Reveals Unexpected Behavior of the LuxR Promoter, A. Malcolm Campbell, et. al.
Validate the data ➲ We transformed the Davidson XOR design and characterized the fluorescent protein expression. ➲ Our results mirrored the published results supporting the theory that backwards tran- scriptional activity was occuring.
A plan to fix the promoter ➲ Consensus search for possible promoter sequences utilized RSAT online tools.
Site directed mutagenesis Antunes, L.C.M., R.B.R. Ferrerira, C.P. Lostroh, and E.P. Greenberg ➲
Challenges ➲ We experienced difficulty with ligation and transforming ligated parts. ➲ Using quorum sensing in synthetic biology is tricky due to promoter cross-activity. ➲ Our lab was focused on cell biology so we had to perfect bacterial cloning methods from scratch.
Next Steps ➲ Apply our plan for site directed mutagenesis ➲ Evaluate additional promoters with competi- tive polymerase activity for logic gates use ➲ Evaluate additional designs using hybrid promoters, etc. ➲ Use functional gates in combinatorial circuits
Feedback from synthetic biologists working on biological computation ➲ “The challenge seems to be in finding the kinds of problems that are well-suited to biocomputation and that target set of problems changes regularly with ad- vances in parts, tools, techniques, and imagination.” ➲ “I worked in the lab that invented bacterial computers, and I do not see a future in it. Synthetic biology de- vices will never compete with silicon computers, and no one should be trying to make them.”
Acknowledgements ➲ Students at Hunter College: Daniel Packer, Dylan Sun, Melanie Balmick, Clara Ng, Ephrayim Kishko, Mark Rukhman, Anna Feitzinger, Henna Ahmed, Yaroslav Mel- nyk, Victoria Tarasova, Svitlana Tchumek ➲ Hunter College Faculty Advisors: Dr. Weigang Qiu, Dr. Derrick Brazill and our other Advisors: Dr. David Reeves, Sung Won Lim, Dr. Malcolm Campbell (Davidson). ➲ Special thanks to Hunter College, the wonderful Quantita- tive Biology program, chair of the CS dept Dr. Virginia Teller, the office of the president of Hunter, and William DeLoache.
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