Difference between revisions of "Team:IISc-Bangalore/Collaborations"

 
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<h1>Collaborations</h1>
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<li><a href="#haemocytometry">Software | iGEM India<img src="https://static.igem.org/mediawiki/2017/6/68/T--IISc-Bangalore--navbar_bullet.png" /></a></li>
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<li><a href="#university-of-alberta">Exchange | UAlberta<img src="https://static.igem.org/mediawiki/2017/6/68/T--IISc-Bangalore--navbar_bullet.png" /></a></li>
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<li><a href="#iit-madras">Database | IIT Madras<img src="https://static.igem.org/mediawiki/2017/6/68/T--IISc-Bangalore--navbar_bullet.png" /></a></li>
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Sharing and collaboration are core values of iGEM. We encourage you to reach out and work with other teams on difficult problems that you can more easily solve together.
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<h3>Silver Medal Criterion #2</h3>
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<h1 id="haemocytometry">Cell Counting: Machine Learning</h1>
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Complete this page if you intend to compete for the silver medal criterion #2 on collaboration. Please see the <a href="https://2017.igem.org/Judging/Medals">2017 Medals Page</a> for more information.
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<p>Our initiative to automate cell culture experiments using GCODe extended itself naturally into a software component — to count cells from images automatically. Haemocytometry — a cell counting technique traditionally used to count blood cells — uses a glass slide etched with a grid to provide a systematic way to estimate cell counts. Analyzing haemocytometer images manually is extremely tedious and some cell-counting programs exist to count these cells automatically, but none use the infinite versatility of neural networks.<p>
  
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<h4> Which other teams can we work with? </h4>
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<img src="https://static.igem.org/mediawiki/2017/c/cd/T--IISc-Bangalore--haemocytometry.png" width="100%">
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You can work with any other team in the competition, including software, hardware, high school and other tracks. You can also work with non-iGEM research groups, but they do not count towards the iGEM team collaboration silver medal criterion.
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One step of the initial image-processing based method
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<p>As a result, we tried to develop our own Machine Learning-based cell counting software, using Artificial Neural Networks. To do this, we needed images of cells to train our algorithm to distinguish between cells and background under various conditions of lighting and image quality. We initiated an All-India iGEM team collaboration, in which teams across the country — <a href="https://2017.igem.org/Team:IISER-Pune-India">Team IISER Pune</a>, <a href="https://2017.igem.org/Team:IISER-Mohali-INDIA">Team IISER Mohali</a> and <a href="https://2017.igem.org/Team:IIT-Madras">Team IIT Madras</a> — helped us to gather images that formed the training data sets for our neural network, following a detailed <a href="https://static.igem.org/mediawiki/2017/4/44/T--IISc-Bangalore--hemocytometry.pdf">protocol</a> we sent them. We were not able to achieve the desired results, due to issues with our model. A detailed description of our efforts is given below:</p>
  
 
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In order to meet the silver medal criteria on helping another team, you must complete this page and detail the nature of your collaboration with another iGEM team.
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<a href = 'https://static.igem.org/mediawiki/2017/d/de/T--IISc-Bangalore--Collaboration--ML--CellCounting.pdf'>Cell Counting using Machine Learning: A Haemocytometry Collaboration - IISc iGEM</a>
 
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<h1 id="university-of-alberta">A Literature Exchange: UAlberta</h1>
  
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<p>Soon after we uploaded our title and abstract on our Wiki, <a href="https://2017.igem.org/Team:UAlberta">Team UAlberta</a> contacted us to discuss about gas vesicles, a central part of their project too, although they were trying to express gas vesicles in <i>E. coli</i>. During this period, we exchanged useful information about gas vesicles from existing literature and shared our project ideas with each other, offering helpful suggestions and advice.</p>
  
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<img src="https://static.igem.org/mediawiki/2017/1/18/T--IISc-Bangalore--ualberta.png" width="100%">
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An email from us to UAlberta discussing gas vesicle formation in E. coli
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<p>UAlberta also made multiple attempts to locate a strain of <i>Anabaena flos-aquae</i> for us, but were sadly unable to do so; we ended up purchasing the strain from UTEX Culture Collection of Algae. Later, we and UAlberta exchanged information about modelling gas vesicles — our experience with modeling gas vesicle dynamics in the diffusive regime proved to be helpful to their model while they provided us with a visual simulation of gas vesicle flotation in Java.</p>
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Here are some suggestions for projects you could work on with other teams:
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<h1 id="iit-madras">Database Contribution: IIT Madras</h1>
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<p>As part of <a href="https://2017.igem.org/Team:IIT-Madras">Team IIT Madras'</a> initiative to collect protocols and information about different chassis organisms used in synthetic biology, they reached out to us during the iGEM 2017 All-India Meet-Up to request information from us.</p>
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<img src="https://static.igem.org/mediawiki/2017/2/25/T--IISc-Bangalore--chassidex.png">
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IIT Madras' database of chassis organisms includes our entries for Saccharomyces cerevesiae and Pichia pastoris
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<p>Over the summer, we had worked on <i>Saccharomyces cerevisiae</i> and <i>Pichia pastoris</i> for eukaryotic recombinant protein synthesis, and had protocols and data available, which we promptly submitted to IIT Madras' database as a contribution of two chassis organisms. Kartik from their team commented on our submission: "The data looks amazing! Thanks a lot!"</p>
  
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<li> Improve the function of another team's BioBrick Part or Device</li>
 
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<li> Characterize another team's part </li>
 
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<li> Debug a construct </li>
 
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<li> Model or simulating another team's system </li>
 
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<li> Test another team's software</li>
 
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<li> Help build and test another team's hardware project</li>
 
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<li> Mentor a high-school team</li>
 
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Latest revision as of 02:52, 2 November 2017

  1. Software | iGEM India
  2. Exchange | UAlberta
  3. Database | IIT Madras

Cell Counting: Machine Learning

Our initiative to automate cell culture experiments using GCODe extended itself naturally into a software component — to count cells from images automatically. Haemocytometry — a cell counting technique traditionally used to count blood cells — uses a glass slide etched with a grid to provide a systematic way to estimate cell counts. Analyzing haemocytometer images manually is extremely tedious and some cell-counting programs exist to count these cells automatically, but none use the infinite versatility of neural networks.


One step of the initial image-processing based method

As a result, we tried to develop our own Machine Learning-based cell counting software, using Artificial Neural Networks. To do this, we needed images of cells to train our algorithm to distinguish between cells and background under various conditions of lighting and image quality. We initiated an All-India iGEM team collaboration, in which teams across the country — Team IISER Pune, Team IISER Mohali and Team IIT Madras — helped us to gather images that formed the training data sets for our neural network, following a detailed protocol we sent them. We were not able to achieve the desired results, due to issues with our model. A detailed description of our efforts is given below:

Cell Counting using Machine Learning: A Haemocytometry Collaboration - IISc iGEM

A Literature Exchange: UAlberta

Soon after we uploaded our title and abstract on our Wiki, Team UAlberta contacted us to discuss about gas vesicles, a central part of their project too, although they were trying to express gas vesicles in E. coli. During this period, we exchanged useful information about gas vesicles from existing literature and shared our project ideas with each other, offering helpful suggestions and advice.

An email from us to UAlberta discussing gas vesicle formation in E. coli

UAlberta also made multiple attempts to locate a strain of Anabaena flos-aquae for us, but were sadly unable to do so; we ended up purchasing the strain from UTEX Culture Collection of Algae. Later, we and UAlberta exchanged information about modelling gas vesicles — our experience with modeling gas vesicle dynamics in the diffusive regime proved to be helpful to their model while they provided us with a visual simulation of gas vesicle flotation in Java.

Database Contribution: IIT Madras

As part of Team IIT Madras' initiative to collect protocols and information about different chassis organisms used in synthetic biology, they reached out to us during the iGEM 2017 All-India Meet-Up to request information from us.


IIT Madras' database of chassis organisms includes our entries for Saccharomyces cerevesiae and Pichia pastoris

Over the summer, we had worked on Saccharomyces cerevisiae and Pichia pastoris for eukaryotic recombinant protein synthesis, and had protocols and data available, which we promptly submitted to IIT Madras' database as a contribution of two chassis organisms. Kartik from their team commented on our submission: "The data looks amazing! Thanks a lot!"