Difference between revisions of "Team:HZAU-China/Description"

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             <h2 class="head" >Model</h2><br/>
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             <h2 class="head" >Overview</h2><br/>
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            <p>The density distributions of bacteria in culture medium depends on two factors: motility and multiply of bacteria. For convenience, we will discuss these two factors separately.</p>
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             <div class="random"><a id="A11" ></a></div>
 
             <div class="random"><a id="A11" ></a></div>
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             <h3>motility dynamic model</h3>
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             <h3>What’s the AR</h3>
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             <p>In this project, we are trying to control the motility of bacteria by control the light matrix to make a colony with a specific shape. That the light matrix can affect the motility of bacteria is by influencing the expression of a protein related with motility and this protein is chez in this project. There are two forms of bacterial motility: tumbling and swimming. By tumbling, bacteria can change its swimming direction but not position. But by swimming, bacteria will forward to change its position. In fact, the rate of tumbling and swimming is different from bacteria to bacteria. At the micro level, the angle of tumbling is random, so we have no idea the movement direction of bacteria in any time. But in the view of macroscopic, the probability of all directions are equal. This property is similar to the diffusion of chemical. So, the swimming of bacteria can be seen as the diffusion of bacteria and the rate of diffusion is related to expression of chez.</p>
+
             <p>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). </p>
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            <p>Due to the multiply of bacteria makes little difference to the diffusion, so we don’t consider the multiply of bacteria provisionally.</p>
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             <img src="#">
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             <img src="https://static.igem.org/mediawiki/2016/5/57/T--HZAU-China--model-figure1.jpg" />
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             <p style="text-align:center">Figure .1 Augmented reality.</p><br/><br/><br/>
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             <p style="text-align:center">(Figure1. Simple graph about diffusion of bacteria.)</p>
+
              
−
            <p>In Figure 1, the simple square represent the area of bacterial in coordinate \((x,y)\) approximatively. The number of bacteria in this area is \(S(x,y,t)\) at time t. So, we have:</p>
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             <h3>BioPafiar</h3>
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            <p style="text-align:center">\(S(x,y,t)=\rho(x,y,t)*\Delta x\Delta y\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((1)\)</p>
+
             <p>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.
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             <p>In equation \((1)\), \(\rho(x,y,t)\) represent density of bacteria in that area. \(\Delta x\) and \(\Delta y\) are the smallest increment in the \(x\) and \(y\) axis respectively. After taken the derivative of equation (1), we have:</p>
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Welcome to our project: BioPafiar!
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             <p style="text-align:center">\(\frac {\partial S(x,y,t)}{\partial t}=\frac {\partial\rho(x,y,t)*\Delta x\Delta y}{\partial t}\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((2)\)</p>
+
</p>
−
             <p>The left part of equation \((2)\) represent the change rate of \(S(x,y,t)\). Under the premise of ignore the multiply of bacteria, the change rate depends on the rate of diffusion of bacteria. As shown in Figure 1, bacteria can move along the \(x\) axis and \(y\) axis. Limit that along the \(x\) and \(y\) axis is the positive. So:</p>
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             <img src="#">
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            <p style="text-align:center">\(\frac {\partial\rho (x,y,t)\Delta x\Delta y}{\partial t}=\phi_x(x,y,t)\Delta y- \phi_x(x+\Delta x,y,t)\Delta y+\phi_y(x,y,t)\Delta x-\phi_y(x,y+\Delta y,t)\Delta x\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((3)\)</p>
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             <p style="text-align:center">Figure 2.Welcome to BioPafiar!</p><br/><br/><br/>
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            <p>In equation \((3)\), \(\phi_x\) and \(\phi_y\) represent the diffusion of bacteria in \(x\) and \(y\) axis respectively. Dividing \(\Delta x*\Delta y\) on both sides of equation \((3)\), and we can get:</p>
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−
            <p style="text-align:center">\(\frac {\partial\rho(x,y,t)}{\partial t}=\frac {\phi_x(x,y,t)-\phi_x(x+\Delta x,y,t)}{\Delta x}+\frac {\phi_y(x,y,t)-\phi_y(x,y+\Delta y,t)}{\Delta y}\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((4)\)</p>
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             <h3>Background</h3>
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            <p>In fact, equation \((4)\) is not a strict equation because \(\Delta x\) and \(\Delta y\) are not minimum value. But if let \(\Delta x\) and \(\Delta y\) take a limit to 0, equation \((4)\) will be an equation. So we have:</p>
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             <h4>Bio-pattern formation</h4><br/>
−
             <p style="text-align:center">\(\frac {\partial\rho(x,y,t)}{\partial t}=\lim_{\Delta x\rightarrow 0}\frac {\phi_x(x,y,t)-\phi_x(x+\Delta x,y,t)}{\Delta x}+\lim_{\Delta x\rightarrow 0}\frac {\phi_y(x,y,t)-\phi_y(x,y+\Delta y,t)}{\Delta y}\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((5)\)</p>
+
             <p>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.</p>
−
            <p>This equation is equivalent to:</p>
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             <img src="#">
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             <p style="text-align:center">\(\frac{\partial \rho}{\partial t}=-(\frac{\partial\phi_x}{\partial x}+\frac{\partial\phi_y}{\partial y})\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((6)\)</p>
+
             <p style="text-align:center">Figure .3 Pattern formation in nature.</p><br/><br/><br/>
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            <p>Here, we omit the coordinates. In this equation, \(\phi_x\) and \(\phi_y\) is difficult to be known by us. In fact, the rate of diffusion in \(x\) and \(y\) axis is same. From the assumption of Fourier Heat Equation:</p>
+
 
−
            <p>1.&nbsp;If the temperature is constant within an area, there is no flow of heart.</p>
+
             <p>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. </p>
−
             <p>2.&nbsp;If the temperature difference exists in adjacent area, heart will flow from areas high to low.</p>
+
         
−
             <p>3.&nbsp;For the same kind of material, the bigger the temperature difference of two adjacent area, the faster flow between them.</p>
+
             <p>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.</p>
−
            <p>Similarly, the three characters are suitable in bacteria colony:</p>
+
 
−
             <p>1.&nbsp;If the density of bacteria is constant in an area, the move of bacteria will not lead to the change of density.</p>
+
             <img src="#">
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            <p>2.&nbsp;If the density difference exists in adjacent area, bacteria will swim from high density to low density area on the macro.</p>
+
             <p style="text-align:center">Figure .4 band-detect gene networks.</p><br/><br/><br/>
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            <p>3.&nbsp;The bigger the density of two adjacent area, the faster diffusion between them.</p>
+
 
−
            <p>So, the rate of diffusion is related with the difference of density:</p>
+
             <p>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. </p>
−
             <p style="text-align:center">\(\phi_x = -k\frac{\partial\rho}{\partial x}\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((7)\)</p>
+
−
             <p style="text-align:center">\(\phi_y = -k\frac{\partial\rho}{\partial y}\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((8)\)</p>
+
−
            <p>Combining equation\((7)\) and equation\((8)\) with equation\((6)\), we can get:</p>
+
−
            <p style="text-align:center">\(\frac{\partial\rho}{\partial t} = k(\frac{\partial^2\rho}{\partial x^2}+\frac{\partial^2\rho}{\partial y^2})\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((9)\)</p>
+
−
            <h3>Multiply model</h3>
+
−
             <p>There is a famous model about the growth states of bacteria under the condition of limited space and nutrients called logistic growth model:</p>
+
−
            <p style="text-align:center">\(\frac{d\rho}{dt} = \gamma\rho(1-\frac{\rho}{\rho_s})\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((10)\)</p>
+
−
            <p>In equation \((10)\), \(\rho\) represent the density of bacteria, and \(\gamma\) is growth rate constant, and \(\rho_s\) is saturated density.</p>
+
−
            <h3>Multiply and motility dynamic model of bacterial</h3>
+
−
             <p>Combining equation \((9)\) with equation \((10)\), we have:</p>
+
−
             <p style="text-align:center">\(\frac{\partial\rho}{\partial t}=k(\frac{\partial^2\rho}{\partial x^2}+\frac{\partial^2\rho}{\partial y^2})+\gamma\rho(1-\frac{\rho}{\rho_s})\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((11)\)</p>
+
−
            <p>Reference Chenli liu et al, we have several parameter values,</p>
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−
             <p style="text-align:center">$$k=200~1000\mu m^2/s$$ $$\gamma = 3.89e-4s^{-1}$$ $$\rho_s = 1500cell/\mu m^2$$</p>
+
−
            <p>We will use finite element method to solve this PDE. Defining initial conditions are,</p>
+
−
            <p style="text-align:center">$$\rho=matrix.zeros(200,200)$$ $$\rho[100,100]=100$$</p>
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             <p>\(matrix.zeros(200, 200)\) means a zero matrix with \(200*200\), and the value of the index \([100, 100]\) of this matrix is \(100\). And the boundary conditions are,</p>
+
−
            <p style="text-align:center">$$\rho[0:,]=0$$ $$\rho[100:,0]=0$$ $$\rho[0,0:]=0$$ $$\rho[0,100:]=0$$</p>
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−
            <p>Writhing solve program with python and numpy to solve equation \((11)\), and the result was showed by mayavi, then we can get a video that can be seen in Movie1.</p>
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−
            <video src="https://static.igem.org/mediawiki/2016/e/e2/T--HZAU-China--model-movie1.mp4" controls="controls"></video>
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          <img src="#">
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            <p style="text-align:center">(Movie1)</p>
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          <p style="text-align:center">Figure .5 Density-sensing and Motility-control.</p><br/><br/><br/>
  
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            <h3>Multiply and motility dynamic model under the condition of restricted area</h3>
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          <h4>Why and what is motility?</h4>
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            <p>In Movie1, we can see that colony will be a roundness eventually. If we add a restrictive condition about the area that bacteria can move, then what the colony will become? The way to add area limit is using light to control the motility of bacterias. So we will give green light in a specific area and the remainder will be given red light. And bacterias can move only in this green light area. In equation (11), Bacterial motility is mirrored by parameter k. So the model will be the following equations.</p>
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          <p>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.</p>
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            <p style="text-align:center">\(\frac{\partial\rho}{\partial t}=k(\frac{\partial^2\rho}{\partial x^2}+\frac{\partial^2\rho}{\partial y^2})+\gamma\rho(1-\frac{\rho}{\rho_s})\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((11)\)</p>
+
−
            <p style="text-align:center">\(k=f(x,y)\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((12)\)</p>
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−
            <p>\(k\) in equation \((11)\) means the diffusion rate of colony. \(f(x,y)\) is area limit function, and it’s an image matrix. In this project, we choose Pikachu who is a famous role in a hot AR game Pokimon Go. See this picture in Figure2.</p>
+
  
−
            <img src="https://static.igem.org/mediawiki/2016/c/c7/T--HZAU-China--model-pikaqiu.jpg"/>
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          <img src="#">
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            <p style="text-align:center">(Figure2)</p>
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          <p style="text-align:center">Figure .6 Mechanism of cheZ.</p><br/><br/>
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            <p>The black part in Figure2 means that the parameter \(k\) is normal value. That is \(k=k_normal=200~1000\mu m^2/s\). And the white part in Figure2 means that the parameter \(k\) is a small value which will be set to \(0\). But \(k\) is a matrix in equation \((11)\), and it can be solved out using Figure2. </p>
+
−
            <p style="text-align:center">$$k = img*k_normal/255$$</p>
+
−
            <p>Under the aforementioned initial conditions and boundary conditions, using python program to solve these equations.</p>
+
  
−
            <video src="https://static.igem.org/mediawiki/2016/a/a2/T--HZAU-China--model-movie2.mp4" controls="controls"></video>
+
          <h4>Light-switchable TCS</h4>
−
            <p style="text-align:center">(Movie2)</p>
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          <h5>Why light-switch?</h5>
−
            <p>From Movie2 we can see that a specific pattern is basically formed. But such a pattern formation is just equivalent to using mould. In this project, the pattern formation will be adjusted by computer in real time.</p>
+
          <p>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. </p>
  
−
            <h3>Multiply and motility dynamic model with dynamic regulation</h3>
+
          <h5>What is light-switchable TCS?</h5>
−
            <p>Area limit is static regulation, but dynamic regulation is more suitable in our expectation. In dynamic regulation, we will compare the shape of colony with target picture in real time, and the result of comparing will be converted to light matrix to be irradiate on colony. So, the dynamic model will like the following context.</p>
+
          <p>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.
−
            <p style="text-align:center">\(\frac{\partial\rho}{\partial t}=k(\frac{\partial^2\rho}{\partial x^2}+\frac{\partial^2\rho}{\partial y^2})+\gamma\rho(1-\frac{\rho}{\rho_s})\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((11)\)</p>
+
</p>
−
            <p style="text-align:center">\(k=f(t,\rho,img)\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((13)\)</p>
+
          <img src="#">
−
            <p>Unlike equation\((12)\), there are two additional parameters in equation\((13)\). The two additional parameters are time \(t\) and density \(\rho\) respectively. That means parameter \(k\) will be change in the process of adjustment. And equation\((13)\) can be interpreted as,</p>
+
          <p style="text-align:center">Figure 7. Mechanism of circuits.</p><br/><br/>
−
            <p style="text-align:center">\(\rho_1=\rho*\frac{255}{max(\rho)}\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((14)\)</p>
+
          <a href="#">read more</a>
−
            <p style="text-align:center">\(\rho_2=threshold(\rho_1,THRES_BINARY_INV)\)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\((15)\)</p>
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            <p style="text-
+

Revision as of 15:06, 17 October 2016

body
motility dynamic model
Multiply model
Human Practice
Art Designer
Acknowledgement

Overview


What’s the 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.




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!




Background

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.




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.




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.



Light-switchable TCS

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.

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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