Solvinga4NodeTravelingSalesmanProblemusingthe0 hin/hixC0...
1
www.PosterPresentations.com an k xt. me, new a to s ext. o This skill If yo wor Ver Go t tem prin set subm Mod This Righ clic in th but MAS Imp TEX plac PHO from TAB post has BOX Mod To c COL own © 2 B p The Traveling Salesman Problem • An optimization problem in combinatorics that asks “given a group of cities and the distances between any two of them, what is the shortest route that visits all the cities?” • Critical for the shipping industry The Bacterial Computer • Used hin/hixC system to solve an encoded graph • Encoded distance between cities using varied ribosome binding strength Creating our Own Biobrick • Engineered our own blue-fluorescent protein (BFP) gene halves • Designed primers using an online primer tool – made them in such a way that the chromophore sequence would not be altered • Inserted a hixC site in between the two gene halves to create the fourth node of our pathway The Encoded Path • Used HPP-A2 as a starting construct because it already contained three nodes: two RFP gene halves, two GFP gene halves, and a double terminator • Attached our new node with a moderate ribosome binding site (RBS B0032) to HPP-A2 to create our composite pathway: HPP-A2 + RBS + BFP1 + hixC + BFP2 Background Notebook April 2014 • Received materials from iGEM headquarters and New England BioLabs • Techstravaganza outreach event • Worked on perfecting the transformation procedure using RFP as a control May 2014 • Officially started experimentation for the project • Troubleshooting: transformants of HPP-A0 and HPP-A1 did not fluoresce under UV light • Solution: use cells that express T7 RNA Polymerase • Isolated DNA invertase Hin part for later use • Transformed HPP-A2, HPP-A2 + Hin, and hixC parts using T7 Express E.coli cells • Again, A2 + Hin did not work properly. • Decided to reorder all parts and remake our stock of ampicillin plates • tjSTAR Presentation and Slivoskey Grant Presentation • Received HPP-A0, HPP-A2, and HPP-A2 + Hin from iGEM headquarters • Started creating the BFP node • PCR reaction for both gene halves using specially designed primers June 2014 • Transformations finally worked • HPP-A0 glowed a distinct yellow under UV light, and HPP-A2 without Hin did not (as expected). Decided to use HPP-A2 to construct our composite pathway • Purified the PCR products and performed a miniprep for hixC • Created the composite pathway, HPP-A2 + RBS + BFP 1 + hixC + BFP 2 using a series of restriction digests, ligations, transformations, and minipreps Modeling Testing We hoped to run two gels to see if our new node and composite pathway were made properly. We also planned to test our pathway and check the fluorescent properties of our node by transforming both in E.coli, but due to lack of time and resources, we were unable to determine definitively whether our efforts were successful. If we did have the time however, we would have followed this protocol to determine a false positive to true solution ratio in order to determine the efficiency of our bacterial computer in solving the Traveling Salesman Problem. Screening for all correct phenotypes If some cells do not contain antibiotic resistance or do not fluoresce a certain color under UV light (bright white in this case), then we know that they do not represent a solution to the Traveling Salesman Problem. However, with this comes the possibility of a false positive solution. Using PCR to separate false positive solutions from true solutions In the case of false positives, all the gene halves are properly aligned to produce fluorescence – the first half is followed by the second. Yet the resulting path is not logical because it requires moving between nodes without following an edge. It has already been established that the length of a pathway that solves the Traveling Salesman Problem is the length of all split genes, and that the first and last node of a pathway is never flippable. Thus, PCR primers can be designed for the first and last node, and running the PCR products on a gel should give the lengths of the amplified DNA. If the path is false-positive solution, then the resulting bands should theoretically be too small to be the most optimal solution to the Traveling Salesman Problem. Detecting Fluorescence Using Spectroscopy To count the number of colonies that fluoresced a certain color on a plate, we would have used a fluorometer. In order to use the fluorometer for our construct, we would have first run scans to find the optimal excitation and emission wavelengths for the split BFP genes that we created. Once these wavelengths have been determined, the fluorometer could be used to determine the emission wavelengths of the fluorescent colonies and we could compare them to the previously determined optimal results. Depending on the number of colonies, they could be counted either by hand or by taking pictures and analyzing them through a program such as ImageJ. By compiling the results of all these different methods, it would be possible to determine the number of fluorescent colonies, how many of them were true solutions compared to false positives, and ultimately achieve our goal of evaluating the efficiency of our system. Tes7ng and Results Conclusions We successfully designed and created a 4-node pathway with varied ribosome binding ability in order to solve the Traveling Salesman Problem, but we were ultimately unsuccessful in implementing the pathway by testing its efficiency. However, this proof-of-concept experiment has the potential to demonstrate the applicability of synthetic biology to the solving of NP-complete problems, and could validate synthetic biology as a feasible approach to computing in the future. In order to demonstrate its feasibility to solve a wide range of problems in theoretical computer science however, various constructs will need to be developed that are designed to simulate other common problems, such as the clique problem and the rank coloring problem. Furthermore, it is recommended that in the future, more time be allotted to troubleshooting. Although we spent significantly more time for experimentation for this year’s experiment than we did for last year’s, we ran into several problems with the transformation and had to repeatedly fix certain variables of our procedure so that we ended up with a favorable result. To circumvent this issue, we recommend future researchers to be completely aware of the unique resources and environment within their lab so that they can adapt their protocols more easily. In addition, since we worked in a school laboratory, finding sufficient time to finish certain procedures was a bit of a hassle. For most of the year, we were only able to work during 45 - 1 hour 30 minute blocks, which is not enough time to complete a substantial portion of experimentation. To work around this issue, we recommend that future groups truly work as a team - map out everyone’s schedules and find out who is available when. Not everyone needs to be available for a procedure to get done; as long as one person can be in the lab, the job can be finished. References 2007 Davidson College and Missouri Western State University Hamilton Pathfinders. Retrieved from http://2007.igem.org/Davidson_Missouri_W Baumgardner, J., Acker, K., Adefuye, O., Crowley, S. T., DeLoache, W., Dickson, J. O., . . . Eckdahl, T. T. (2009). Solving a Hamiltonian Path Problem with a bacterial computer. Journal of Biological Engineering, 3(11). http://dx.doi.org/10.1186/1754-1611-3-11 Acknowledgements Mentors We would like to thank Dr. Mary Susan Burnett, a DNA teacher, researcher, and lab director who is currently employed at the Thomas Jefferson High School for Science and Technology. She guided us through the more difficult procedures in our experiment, and taught us all of the proper techniques and safety guidelines that we employed throughout our work. Sponsors We would also like to thank Brian Becker and Precision Economics for their generous sponsorship of our project. Without their donations and support, much of the work that we did would not be possible. Thomas Jefferson High School for Science and Technology/Jefferson_VA_SciCOS Sahitya Allam, Archis R. Bhandarkar, William Woodruff, Ramya Radhakrishnan, Wiliam Liu, Peter Suzuki, Thuy Vi Nguyen, Kira Becker, MaUhew Kramer, Lucas Kang Solving a 4Node Traveling Salesman Problem using the hin/hixC Recombinant System Bacterial Simulation This section models the probability that a given edgelist become a solution after K number of flips. This is modeled by generating 10,000 different strands all with random starting conditions, and measuring how many flips it takes to achieve a solution to the traveling salesman problem. This is modeled using a 4 node graph with 5 edges. Markov Chain Model This section uses a Markov Chain model to determine what the distribution is of the edgelists after K flips. A transition matrix is used to determine what percentage of edgelists change to a new state. In this model the probability is equal for changing states. For example, in the edge list ”abcd”, it is equality likely to transition to ”bacd”, ”acbd”, or ”abdc”. . Human Prac7ces • A few of the members of our 2013 iGEM team started a club known as the Synthetic Biology Society. This club focused on providing informational lectures, conducting outreach activities, and recruiting new members for the 2014 iGEM competition. • A major component of Synthetic Biology Society’s outreach activities was the prep labs. In these labs, club members were introduced to the resources available in the biotech lab, as well as some basic techniques with broad applications in synthetic biology and related fields. These techniques followed the order of the 3A assembly process, to give participants the skills necessary to participate in the iGEM competition in the future. • Another major outreach event was the annual Techstravaganza, which is hosted by another club at our school, and allows elementary and middle school students to visit booths that introduce them to various STEM fields and ideas. We ran two booths, one in which students could model the structure of DNA using candy and in the other we allowed students to perform DNA extraction on bananas. • Towards the end of the year, we gave two presentations for the One Question and Slivoskey Grants, which are awarded to clubs at our school. The One Question Grant presentation was at tjSTAR, an annual symposium to showcase research done by students at our school and to bring in speakers from various fields of science and technology. Both of these presentations focused on our outreach efforts and future plans. • We created a facebook page for our team, taking advantage of social media to post updates on our progress and other interesting news in the field of synthetic biology. Thuy Vi Nguyen is learning how to streak bacteria on a plate
Transcript of Solvinga4NodeTravelingSalesmanProblemusingthe0 hin/hixC0...
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The Traveling Salesman Problem • An optimization problem in combinatorics that asks “given a group of cities and the distances
between any two of them, what is the shortest route that visits all the cities?” • Critical for the shipping industry
The Bacterial Computer • Used hin/hixC system to solve an encoded graph • Encoded distance between cities using varied ribosome binding strength
Creating our Own Biobrick • Engineered our own blue-fluorescent protein (BFP) gene halves • Designed primers using an online primer tool – made them in such a way that the chromophore
sequence would not be altered • Inserted a hixC site in between the two gene halves to create the fourth node of our pathway
The Encoded Path • Used HPP-A2 as a starting construct because it already contained three nodes: two RFP gene halves,
two GFP gene halves, and a double terminator • Attached our new node with a moderate ribosome binding site (RBS B0032) to HPP-A2 to create our
composite pathway: HPP-A2 + RBS + BFP1 + hixC + BFP2
Background
Notebook April 2014 • Received materials from iGEM headquarters and New England BioLabs • Techstravaganza outreach event • Worked on perfecting the transformation procedure using RFP as a control
May 2014 • Officially started experimentation for the project • Troubleshooting: transformants of HPP-A0 and HPP-A1 did not fluoresce under UV light • Solution: use cells that express T7 RNA Polymerase • Isolated DNA invertase Hin part for later use • Transformed HPP-A2, HPP-A2 + Hin, and hixC parts using T7 Express E.coli cells • Again, A2 + Hin did not work properly. • Decided to reorder all parts and remake our stock of ampicillin plates • tjSTAR Presentation and Slivoskey Grant Presentation • Received HPP-A0, HPP-A2, and HPP-A2 + Hin from iGEM headquarters • Started creating the BFP node • PCR reaction for both gene halves using specially designed primers
June 2014 • Transformations finally worked • HPP-A0 glowed a distinct yellow under UV light, and HPP-A2 without Hin did not (as expected).
Decided to use HPP-A2 to construct our composite pathway • Purified the PCR products and performed a miniprep for hixC • Created the composite pathway, HPP-A2 + RBS + BFP 1 + hixC + BFP 2 using a series of restriction
digests, ligations, transformations, and minipreps
Modeling
Testing We hoped to run two gels to see if our new node and composite pathway were made properly. We also
planned to test our pathway and check the fluorescent properties of our node by transforming both in E.coli, but due to lack of time and resources, we were unable to determine definitively whether our efforts were successful. If we did have the time however, we would have followed this protocol to determine a false positive to true solution ratio in order to determine the efficiency of our bacterial computer in solving the Traveling Salesman Problem.
Screening for all correct phenotypes If some cells do not contain antibiotic resistance or do not fluoresce a certain color under UV light
(bright white in this case), then we know that they do not represent a solution to the Traveling Salesman Problem. However, with this comes the possibility of a false positive solution.
Using PCR to separate false positive solutions from true solutions In the case of false positives, all the gene halves are properly aligned to produce fluorescence – the
first half is followed by the second. Yet the resulting path is not logical because it requires moving between nodes without following an edge. It has already been established that the length of a pathway that solves the Traveling Salesman Problem is the length of all split genes, and that the first and last node of a pathway is never flippable. Thus, PCR primers can be designed for the first and last node, and running the PCR products on a gel should give the lengths of the amplified DNA. If the path is false-positive solution, then the resulting bands should theoretically be too small to be the most optimal solution to the Traveling Salesman Problem.
Detecting Fluorescence Using Spectroscopy To count the number of colonies that fluoresced a certain color on a plate, we would have used a
fluorometer. In order to use the fluorometer for our construct, we would have first run scans to find the optimal excitation and emission wavelengths for the split BFP genes that we created. Once these wavelengths have been determined, the fluorometer could be used to determine the emission wavelengths of the fluorescent colonies and we could compare them to the previously determined optimal results. Depending on the number of colonies, they could be counted either by hand or by taking pictures and analyzing them through a program such as ImageJ.
By compiling the results of all these different methods, it would be possible to determine the number of fluorescent colonies, how many of them were true solutions compared to false positives, and ultimately achieve our goal of evaluating the efficiency of our system.
Tes7ng and Results Conclusions
We successfully designed and created a 4-node pathway with varied ribosome binding ability in order to solve the Traveling Salesman Problem, but we were ultimately unsuccessful in implementing the pathway by testing its efficiency. However, this proof-of-concept experiment has the potential to demonstrate the applicability of synthetic biology to the solving of NP-complete problems, and could validate synthetic biology as a feasible approach to computing in the future. In order to demonstrate its feasibility to solve a wide range of problems in theoretical computer science however, various constructs will need to be developed that are designed to simulate other common problems, such as the clique problem and the rank coloring problem. Furthermore, it is recommended that in the future, more time be allotted to troubleshooting. Although we spent significantly more time for experimentation for this year’s experiment than we did for last year’s, we ran into several problems with the transformation and had to repeatedly fix certain variables of our procedure so that we ended up with a favorable result. To circumvent this issue, we recommend future researchers to be completely aware of the unique resources and environment within their lab so that they can adapt their protocols more easily. In addition, since we worked in a school laboratory, finding sufficient time to finish certain procedures was a bit of a hassle. For most of the year, we were only able to work during 45 - 1 hour 30 minute blocks, which is not enough time to complete a substantial portion of experimentation. To work around this issue, we recommend that future groups truly work as a team - map out everyone’s schedules and find out who is available when. Not everyone needs to be available for a procedure to get done; as long as one person can be in the lab, the job can be finished.
References 2007 Davidson College and Missouri Western State University Hamilton Pathfinders. Retrieved from
http://2007.igem.org/Davidson_Missouri_W Baumgardner, J., Acker, K., Adefuye, O., Crowley, S. T., DeLoache, W., Dickson, J. O., . . . Eckdahl, T. T. (2009). Solving a Hamiltonian Path Problem with a bacterial computer. Journal of Biological Engineering, 3(11). http://dx.doi.org/10.1186/1754-1611-3-11
Acknowledgements
Mentors
We would like to thank Dr. Mary Susan Burnett, a DNA teacher, researcher, and lab director who is currently employed at the Thomas Jefferson High School for Science and Technology. She guided us through the more difficult procedures in our experiment, and taught us all of the proper techniques and safety guidelines that we employed throughout our work.
Sponsors
We would also like to thank Brian Becker and Precision Economics for their generous sponsorship of our project. Without their donations and support, much of the work that we did would not be possible.
Thomas Jefferson High School for Science and Technology/Jefferson_VA_SciCOS
Sahitya Allam, Archis R. Bhandarkar, William Woodruff, Ramya Radhakrishnan, Wiliam Liu, Peter Suzuki, Thuy Vi Nguyen, Kira Becker, MaUhew Kramer, Lucas Kang
Solving a 4-‐Node Traveling Salesman Problem using the hin/hixC Recombinant System
Bacterial Simulation
This section models the probability that a given edgelist become a solution after K number of flips. This is modeled by generating 10,000 different strands all with random starting conditions, and measuring how many flips it takes to achieve a solution to the traveling salesman problem. This is modeled using a 4 node graph with 5 edges.
Markov Chain Model
This section uses a Markov Chain model to determine what the distribution is of the edgelists after K flips. A transition matrix is used to determine what percentage of edgelists change to a new state. In this model the probability is equal for changing states. For example, in the edge list ”abcd”, it is equality likely to transition to ”bacd”, ”acbd”, or ”abdc”.
.
Human Prac7ces
• A few of the members of our 2013 iGEM team started a club known as the Synthetic Biology Society. This club focused on providing informational lectures, conducting outreach activities, and recruiting new members for the 2014 iGEM competition.
• A major component of Synthetic Biology Society’s outreach activities was the prep labs. In these labs, club members were introduced to the resources available in the biotech lab, as well as some basic techniques with broad applications in synthetic biology and related fields. These techniques followed the order of the 3A assembly process, to give participants the skills necessary to participate in the iGEM competition in the future.
• Another major outreach event was the annual Techstravaganza, which is hosted by another club at our school, and allows elementary and middle school students to visit booths that introduce them to various STEM fields and ideas. We ran two booths, one in which students could model the structure of DNA using candy and in the other we allowed students to perform DNA extraction on bananas.
• Towards the end of the year, we gave two presentations for the One Question and Slivoskey Grants, which are awarded to clubs at our school. The One Question Grant presentation was at tjSTAR, an annual symposium to showcase research done by students at our school and to bring in speakers from various fields of science and technology. Both of these presentations focused on our outreach efforts and future plans.
• We created a facebook page for our team, taking advantage of social media to post updates on our progress and other interesting news in the field of synthetic biology.
Thuy Vi Nguyen is learning how to streak bacteria on a plate
