Team:HZAU-China/Description
Description
1.Overview
1.1 What is AR?
With the unprecedented rapid development in computer technology and the significant promotion in computing capacity, it is undeniable truth that computer and computer products such as smartphone has played a great role in our lives. People spend their spare time using twitter and facebook, analyze data through computer applications, they can’t live without high-tech computer products. Contrary to the reality world that we actually exist in, computer creates a virtual world. Though computer itself is tangible, what emerges on the screen is an intangible product of a series of logical combination from electronic circuit. Our iGEM team in 2015 proposed the concept of “Mixed Reality Cell”, constructed by the interaction between the genetic oscillator and the e-oscillator, eventually leads to coupling and synchronization. However, existing in a world based on reality rather than mixed reality, computers are utilized as a tool to enhance one’s perception on reality and meet substantial requirements. This is a reality-oriented mixed-reality, which can also be called augmented reality (AR).
Figure .1 Augmented reality.
1.2 BioPaFiAR
The growth of bacteria complies with certain laws, among which the easiest would be the law of reproduction-diffusion, under whose control the shape of colony would appears to be round. If a total regulation on bacterial growth is added, for example the enhancement on its motility, the size of colony will be larger; if a local regulation is imposed on bacteria growth, the round shape of the colony will disappear; if the regulation is time-dependent, the regulation will make adjustment according to the certain circumstance of the bacterial colony at specific time. Therefore, the shape of the bacterial lawn will match our intended design. In contrary to the isolated state in nature, the cells and computer together forms a system, which is composed of both reality and virtual world, and reality occupied most part of the augmented reality system. In the system, the pattern formation of the bacteria population will be established as required. So we design this project to propose a concept that biochemistry reaction in different kinds of biological stimulation can be mimicked by placing cells in AR system, such as synthesis of enzymes, signal transduction, and bio-pattern formation. In addition, in the near future, this AR system can be applied in medical field or in our daily lives. For example, biological material 3D printing, and in vitro induction of tissue or organ. Welcome to our project: BioPaFiAR!
Figure 2.Welcome to BioPafiar!
2. Background
2.1 Bio-pattern formation
Bio-pattern formation is the establishment of spatial patterns in morphogenesis. The process of morphogenesis is of great importance to our perception and creation of life. However, it is a complicated process which has long been studied but still not totally understood. Models have been built to illustrate the principle of pattern formation throughout the century, among those are Reaction-Diffusion Model (Turing model)(1), kinetic model of density-suppressed motility, and Clock-Wavefront Model(2). Recently, synthetic biology increasingly plays a significant role in unveiling the mystery of bio-pattern formation, with the concept of “build life to understand it”. By constructing genetically engineered circuits, scientists revealed the relationship between density and motility, and further the mechanism of pattern formation in bacterial community.
Figure .3 Pattern formation in nature.
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Bio-pattern formation is a significant field of research in synthetic biology. The process of morphogenesis is of great importance to our perception and creation of life. However, bio-pattern formation is a complicated process which has been studying since the 1950s. Alan Turing, father of computer as well as a prestigious mathematician, first proposed of the Reaction-Diffusion Model(1), which is also known as Turing model, to explain the basic chemical principle of bio-pattern formation. In this self-recovery system, interaction between two diffusible substances, short-range positive feedback and long-range negative feedback, leads to pattern formation. And the pattern is tuned according to different initial condition. This archetypical model has been emphasized, tested and improved incessantly. Meanwhile, other models have appeared, for instance, the Clock-Wavefront Model and the kinetic model of density-suppressed motility. The Clock-Wavefront Model aims to elucidate the mechanism of pattern formation of somite in animal morphogenesis. Assuming that there’s a clock which is a coupled cellular oscillation, and a wavefront which in fact is rapid cell change moving along axis in cell growth, somite pattern formation is driven by alteration in oscillation when wavefront reaches the boundary of the cell community. As for model of density-suppressed motility, whose purpose is to demonstrate the pattern formation of a community of unicellular organisms such as bacteria, unique pattern can be formed relating the motility of bacteria to local cell density. These models are all confirmed through experimental demonstration.
With the rapid development in synthetic biology and systematic biology in the 21st century, scientists increasingly tend to build life to understand bio-pattern formation. In 2005, a synthetic multicellular system was built by Subhayu Basu et al(3). Through ‘band-detect’ gene networks, genetically engineered receiver cells respond to user-defined ranges of chemical molecule AHL concentrations produced by sender cells by producing different concentration of GFP. If sender cells are placed in the center, receiver cells will form a ring-like configuration due to the gradient of AHL.
Figure .4 band-detect gene networks.
Bio-pattern formation is the process of moulding specific spatial structure. In reality, to obtain particular shape of substance, we would sculpt a delicately designed mould in a traditional way or adopt modern 3D-printing technology. Method developed by Subhayu Basu is equivalent to mould sculpture, since the growth of bacteria is not considered. Distinguishingly, Chenli liu et al constructed a genetic circuit that works in a single cell rather than a cell community. The circuit is consist of two parts, Density-sensing and Motility-control. Density-sensing part is responsible for producing AHL, while motility-controlling part act as receptor of AHL and further inhibits basic expression of the motility-related protein cheZ. In this self-recovery system, motility of cell is connected with cell density. If a suspension of bacteria is inoculated at the center of the dish, the spatialtemporal pattern of bacterial lawn will appear as a ring-like configuration, as suggested in B&C.
Figure .5 Density-sensing and Motility-control.
2.2 Why and what is motility?
Bio-pattern formation is determined by two major factors, growth and diffusion. The most crucial and controllable one is the pattern cells disperse, which is a reflection of bacterial motility. Hence, in order to study the principle of bio-pattern formation, motility-related gene cheZ is introduced into our project. CheZ is a positive regulating protein that modulates flagella rotation. Expression of CheZ leads bacteria to swim in semisolid agar while its deletion causes cells to tumble incessantly, resulting in a nonmotile phenotype.
Figure .6 Mechanism of cheZ
2.3 Light-switchable TCS
2.3.1 Why light-switch?
When bacteria establish bio-pattern, they receive different kind of signals in the ambient environment they live in. In our project, computer output light signal has been given as a motility-control module, which was devised to modify bacterial motility by regulating the transcription of cheZ, aiming at imitating the environment. The genetically engineered bacteria is able to perceive and distinguish different wavelength as an input signal, and express motility-related gene as an output. Therefore, the motility of the bacteria can be controlled responding to input light signal.
2.3.2 What is light-switchable TCS?
Optogenetics is a prevalent technology wherein light is utilized to mediate molecular biological processes via light-switchable proteins. In our project, a green/red two-component signal transduction system Ccas-CcaR(4) is used to control the gene expression of cheZ according to our need. We also call it the Traffic light model. When green light is on, light-switchable cascade is amplified and output protein PleD(5) is activated, resulting in the expression of cheZ and enhancement in motility. Conversely, when red light is on, the signal pathway is impaired. In a word, green light drives the bacteria to move and swim, while red light makes them stop.
Figure .7 Mechanism of circuits
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2.4 RIBOSWITCH
2.4.1 Why riboswitch?
In order to enhance the robustness of the AR system, to be specific, to filter the leakiness of light-switchable output promoter, riboswitch is incorporated in our project. Riboswitch possess the characteristic that the aptamer is unlikely to respond to the upstream signal and activate downstream expression until it reaches its threshold. Thus, riboswitch can be applied to filter the noise resulting from leakiness.
2.4.2 What is riboswitch?
Riboswitches are cis-regulatory RNA elements residing in the 5’-untranslated region (5’-UTR) of bacterial mRNAs that modulate downstream gene expression upon binding of small specific molecules. A typical riboswitch usually contains two domains, an evolutionarily conserved aptamer domain for binding a target metabolite, and an expression platform for regulating downstream gene expression via a ligand-dependent conformational switch(6).
Figure .8 Mechanism of Riboswitches
To take control of the specific bio-pattern formation of bacteria, relevant software and hardware has been devised.
3. Hardware
3.1 Light Tube Array (LTA)
Two versions of devices are designed to quantify the effect of light-switchable TCS. V1.0 is a roughly handmade one device which can achieve the goal to provide different wavelength of light and fixation of centrifugal tube where bacteria are cultured. In v2.0, based on the platform exploited by Gerhardt(Light Tube Array) (7), we construct the hardware device that is able to precisely regulate the optogenetic circuit of the engineered bacteria utilizing 3D printing technology. This device successfully averts the interference of external light from the outside environment. Meanwhile, with the equipment of Single Chip Microcomputer (SCM), precise regulation on the intensity and wavelength of light can be accomplished. In summary, v2.0 eliminates the interference from ambient environment to the best extent when doing quantitative experiment of light-switchable system, thus improving the reliability and repeatablility of experimental result.
Figure .9 Illustration of light-switchable device.
3.2 Industrial grade Pattern formation
To control the bio-pattern formation of bacteria, we designed a device of industrial level. This device is capable of providing constant temperature and sterile environment that is suitable for bacterial growth. Besides, this device is also responsible for real-time monitoring of bacterial growth, light control over pattern formation, and projecting function.
Figure .10 Illustration of industrial grade Pattern formation
4.Software
we developed a set of sofetware kits related to bacterial motility.
We designed two visualized software based on the principle of reaction-diffusion model and cellular automata model, so that synthetic biologists will have better understanding on the process of bacterial motility and bio-pattern formation. In the software, operators are free to simulate the process If bio-pattern formation by regulating the factors that affect bacterial growth and diffusion.
In order to better support our hardware，We programmed the software that is in line with the industrial level hardware device. When operator enters the pattern they want to form, the software controls the bacterial lawn to grow as preset pattern through real-time monitoring of status of bacterial growth, predict future growing tendency and export the optical pattern to the bacteria as feedback.
Semi-automatic bacterial colony radius calculating software. Error from machinary judgement reduced by semi-manual operation. Value displayed directly at the same time.
Figure .11 Visual interface softwares
a. visual interface (platform: MATLAB) b. right: visual interface (platform: Python)
5.Characterization of Parts
1.BBa_K592003 PcpcG2 promoter, ccaR-regulated. PcpcG2 is a 238bp green-light activated promoter. We did quantitative measurement on this promoter to prove the CcaS/CcaR light-switchable TCS works efficiently. Moreover, to reduce the leakiness, we refactored the promoter by truncating the constitutive promoter region. See PcpcG2-172 (BBa_K2012015)
2.BBa_K819010 Medium cheZ generator. CheZ gene under medium promoter BBa_J23113. We did quantitative measurement on this generator and made precise data analysis on the distance bacteria swim.
3.BBa_K1631018, BBa_K1631019, BBa_K1631020. These three generators are CheZ gene under promoter BBa_J23101, BBa_J23110 and BBa_J23102, respectively. We made quantitative analysis on diffusion rate of E.coli, and debugged the data about promoter strength.
Reference：
1.A. M. Turing, The chemical basis of morphogenesis. 1953. Bull Math Biol 52, 153-197; discussion 119-152 (1990).
2.O. A. Igoshin, D. Kaiser, G. Oster, Breaking symmetry in myxobacteria. Curr Biol 14, R459-462 (2004).
3.S. Basu, Y. Gerchman, C. H. Collins, F. H. Arnold, R. Weiss, A synthetic multicellular system for programmed pattern formation. Nature 434, 1130-1134 (2005).
4.E. J. Olson, L. A. Hartsough, B. P. Landry, R. Shroff, J. J. Tabor, Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nature Methods 11, 449-+ (2014).
5.P. Aldridge, R. Paul, P. Goymer, P. Rainey, U. Jenal, Role of the GGDEF regulator PleD in polar development of Caulobacter crescentus. Mol Microbiol 47, 1695-1708 (2003).
6.A. Serganov, E. Nudler, A decade of riboswitches. Cell 152, 17-24 (2013).
7.K. Gerhardt et al., An open-hardware platform for optogenetics and photobiology. bioRxiv, 055053 (2016).
