Team:TokyoTech/Description
E-Turing
Formation of Turing patterns in a synthetic bacterial population under more natural environment
Project Inspiration
We have been amazed and focused on animal pattern and here's a reason: Our PI, Prof. Tagawa loves jokes, but sometimes it falls flat. One day, we were talking with him in the office. When he said a canned joke as usual, Moe, our team leader took her eyes off with boredom and suddenly found one thing: the skin patterns of zebrafish in his aquarium look so amazing! At the same time, she remembered that Nishikigoi, Japanese carp have been well-known for its beautiful skin patterns recently. (Below is a photograph of the Nishikigoi in the member's house.)
Figure: Japanese Nishikigoi (Cyprinus carpio)
Many have belived that animal skin patterns (not necessarily fish skin patterns) could be explained by the theory of Turing pattern which the late Alan Turing, a British Mathematician proposed in 1952. Although over 60 years have passed since then, it is still challenging to reproduce the pattern by hand. Therefore, we decided to study about Turing pattern and its applications in our life.
Inspiration from past iGEM teams
- iGEM UT-Tokyo (2015) developed a strategy for pattern formation using mathematical modeling. They used linear differential equation, which is a standard method, but requires vast calculation. We tried to improve the process of modeling in a more efficient way using cellular automata.
- iGEM KU_Leuven (2015) designed a circuit capable of forming patterns in a controlled way. Their system can trigger formation at desired points in time, using a modified and high-temperature-sensitive lambda repressor (cI). We thought this temperature-based circuit control is effective, but the other physical stimuli have not been explored yet. Thus, we tried to use cold temperature and light as a regulator of genetic circuit.
Inspiration from previous research
- Karig et al. (2018) tried to recreate stochastic Turing patterns in a synthetic bacterial population. In this study, the inter-bacterial communication is sustained by Rhl system for inhibiting rapid diffusion, and on the other hand, Las system for activating slow diffusion. The method used in this study is not necessarily highly versatile when considering the future application to higher organisms and the consistency with their pattern formation. Specifically, in this study, they use a localized IPTG as the initiator of the circuit, but this is not a realistic method because higher organisms can easily move away from the spot. Therefore, we use cold temperature and light.
Project Description
We fine-tuned the behavior of bacteria so that temperature and light can initiate the formation of Turing pattern in bacterial population. We also created a new model that can regenerate a missed part of fingerprint to enhance the value of technique developed in wet lab and made the most of social perspectives through human practices. Here are the descriptions for Wet Lab, Dry Lab and Human Practices.
Wet Lab
To visualize Turing patterns in a synthetic bacterial population, we designed a new genetic circuit which bacteria use AHL to communicate, and physical stimuli can regulate the expression level. The following video helps you grasp the big picture of the circuit and mechanism.
Parts Characterization (Bronze #5)
We selected two promoters to characterize: BBa K575029, which is LasR (with RBS B0034), coupled with a LasR/PAI1 (AHL, 3-oxo-C12-HSL) inducible promoter, RBS (Part B0034), and a GFP reporter.
Parts Validation (Silver #1)
We designed BBa_K3259002 and BBa_K3259003, which are constructs induced by the combination of physical stimuli and AHL. Those parts help quantify the expression level of BBa_K3259000.
Dry Lab
Based on interviews with Prof. Kondo (Osaka University) in Human Practice, the âfingerprintâ pattern which is a part of identity for each of us is based on the Turing pattern theory. This time, a simulation was performed to restore a fingerprint from a rough fingerprint image using a Turing pattern, inspired by the experimental results at Wet Lab.
We succeeded in calculating and visualizing Turing patterns using cellular automata without solving linear differential equations. After that, we established a method for pre-processing and correcting fingerprint data, and then performed a parameter search that can repair missing fingerprint data.
For functionalizing the cellular automaton equation, the phase value and randomization coefficient were set in a form suitable for the restoration of fingerprint data, and a complete restoration mechanism was finally constructed.
Human Practices
We made a new model of IHP (Integrated Human Practices) by integrating both design of lab works and social engagement from sociocultural perspectives, and visualizing the route of IHP that we followed.
Collaborations and traditional human practices gave us a huge impact to the improvement of project design in both Wet Lab and Dry Lab. We collaborated with several Japanese iGEM teams at iGEM Japan Meetup 2019 and iGEM Japan Summer Meetup 2019 and received a plenty of advices about the experimental procedures and etc. In addition, we have been engaged with several kinds of HP activities that extend from discussion with experts of bioethics and pattern formation to questionnaire at Tokyo Tech Open Campus (school festival) and establishment of method for future team building.
References
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David Karig, K. Michael Martini, Ting Lu, Nicholas A. DeLateur, Nigel Goldenfeld, and Ron Weiss (2018).
Stochastic Turing patterns in a synthetic bacterial population . Proceedings of the National Academy of Sciences ,115(26), 6572-6577 - Team: ETH_Zurich ETH_Zurich - 2012.igem.org. (2012). Retrieved June 28, 2019, from Igem.org
- Team:TokyoTech - 2016.igem.org. (2016). Retrieved June 28, 2019, from Igem.org
- Team:TokyoTech - 2017.igem.org. (2017). Retrieved June 28, 2019, from Igem.org
- Team:TokyoTech - 2012.igem.org. (2012). Retrieved June 28, 2019, from Igem.org
