Skip to content
Snippets Groups Projects

Compare revisions

Changes are shown as if the source revision was being merged into the target revision. Learn more about comparing revisions.

Source

Select target project
No results found

Target

Select target project
  • 2023/iit-madras
1 result
Show changes
Commits on Source (15)
Hide whitespace changes
Inline Side-by-side
wiki/menu.html
View file @ 4af6db1e
<nav class="navbar navbar-expand-lg navbar-dark sticky-top">
<a class="navbar-brand nav-name" href="https://synthopedia.igemiitm.in/myapp/">Synthopedia</a>
<a class="navbar-brand nav-name" href="{{ url_for('pages', page = '/')}}">Synthopedia</a>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav"
aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
......
wiki/pages/contribution.html
View file @ 4af6db1e
......@@ -10,60 +10,46 @@ endblock %}
<h1>CONTRIBUTIONS</h1>
</div>
<div class="hpHeading">
<h1>CHASSIDEX</h1>
</div>
<div class="hpText row">
<p>
Kushi Athreya, a third-year BSc. Biotechnology student of Shiv Nadar Institute of Imminence has collaborated with
IITM iGEM 2023 to continue the ChassiDex project. The ChassiDex project was started by Team iGEM IIT-Madras as an
online database of host organisms and related information. The resource also includes host specific software tools
and lists guidelines to help start with new host organisms. <br>
<br>
Kushi played a pivotal role in compiling data related to various Model and GRAS organisms, including Animal models
(<i>Zebrafish, Drosophila, C.elegans</i> ), Plant models (<i>Tobacco, Arabidopsis</i>), and prokaryotic models
(<i>Agrobacterium, Bacillus licheniformis, Trichoderma Sp</i>) that we wished to include in the ChassiDex website.
This was followed by uploading the collated data onto a publically accessible GitHub platform as a part of the
ChassiDex project.<br>
<br>
The database can be accessed through the following link:<br><br>
<a href="https://chassidex.github.io/">https://chassidex.github.io/ </a> <br><br>
She has been very determined and optimistic, fostering collaboration throughout our exchange. This experience has
been very positive and enriching at both ends. We look forward to future collaborations.
</p>
</div>
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/contri.png">
</div>
<div class="hpHeading">
<h1>Synthopedia: A web tool for predicting and optimizing RBS sequences</h1>
<h1>Synthopedia: A web tool for predicting and optimizing protein expression</h1>
</div>
<div class="hpText row">
<p>
We have developed a user-friendly website featuring a publicly accessible web tool that serves as the interface for
our dry-lab model and optimizer. Upon accessing the website, users are presented with a choice between two distinct
tools: the RBS Rate Prediction Tool and the RBS Optimization Tool.
tools: the Relative Expression Prediction Tool and the RBS Optimization Tool.
</div>
</p>
<!-- <br><br> -->
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/pred.png">
</div>
<br><br>
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/opti1.png">
</div>
<br><br>
The RBS Rate Prediction Tool accepts the following input parameters: RBS Sequence, Coding Sequence, Temperature,
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/opti1.png">
</div>
<div class="hpText row">
<p>
The Relative Expression Prediction Tool accepts the following input parameters: RBS Sequence, Coding Sequence, Temperature,
Gram Stain of Chassis, 16S rRNA Sequence. It subsequently provides the relative expression level in terms of the RBS
rate, represented on a logarithmic scale.
<br><br>
On the other hand, the RBS Optimization Tool also receives the following inputs: RBS Sequence, Coding Sequence,
Target Relative Expression (expressed in terms of the RBS rate on a logarithmic scale), Temperature, Gram Stain of
Chassis, and 16S rRNA Sequence. This tool then furnishes the optimized RBS sequence along with its corresponding
predicted (logarithmically scaled) RBS rate.
Target Relative Expression, Temperature, Gram Stain of
Chassis, and 16S rRNA Sequence. This tool then furnishes the optimized RBS sequence along with its corresponding predicted (logarithmically scaled) relative expression level.
<br><br>
We rigorously evaluated our tool by subjecting it to a comprehensive benchmarking exercise against cutting-edge rate
prediction tools, including the acclaimed RBS Calculator v2.1 by Salis et al., RBS Designer, UTR Designer, EMOPEC,
and others. This evaluation was conducted on a meticulously curated dataset comprising 16,779 mRNA sequences with
quantified expression levels, as assembled by Reis and Salis [1].
quantified expression levels, as assembled by Reis and Salis.
<br><br>
The outcomes of our evaluation unequivocally establish that our tool has decisively surpassed the accuracy of the
foremost calculators worldwide, doing so by a substantial margin.
<br><br>
<a
href="https://gitlab.igem.org/2023/software-tools/iit-madras/-/blob/main/probiotic-model/kill_switch.sbproj">https://gitlab.igem.org/2023/software-tools/iit-madras/-/blob/main/probiotic-model/kill_switch.sbproj</a>
</p>
</div>
<div class="contri">
......@@ -74,22 +60,12 @@ endblock %}
Our model has the best performance among those compared, despite the inherent noise present in a FlowSeq experiment.
</p>
</div>
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/pred.png">
</div>
<br><br>
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/opti1.png">
</div>
<br><br>
<div class="contri">
<img src="https://static.igem.wiki/teams/4931/wiki/opti1.png">
</div>
<div class="hpText row">
<p>
<b>BioBrick parts: </b><br><br>
We have created the following BioBrick parts generated by our Synthopedia: RBS Predictor tool which have been
characterized by conducting fluorescence assays in wet lab.
<b>BioBrick parts </b><br><br>
We have created the following BioBrick parts generated by our RBS Optimization tool which have been
characterized by conducting fluorescence assays in the wet lab.
</p>
</div>
<table class="parts-tbl">
......@@ -127,5 +103,41 @@ endblock %}
<td><a href="http://parts.igem.org/Part:BBa_K4931016">BBa_K4931016</a></td>
</tr>
</table>
<div class="hpHeading">
<h1>Probiotic Treatment for Homocystinuria</h1>
</div>
<div class="hpText row">
<p>
<strong>Modelling engineered Lactococcus lactis for homocystinuria treatment</strong>
<br>
We developed a novel kinetic model for engineered Lactococcus lactis to be used as a probiotic for the treatment of
homocystinuria. Apart from the addition of the genes required to metabolize methionine to cysteine, the model
incorporated the MazEF toxin-antitoxin system as a kill-switch.</p>
</div>
<div class="hpHeading">
<h1>ChassiDex</h1>
</div>
<div class="hpText row">
<p>
Kushi Athreya, a third-year BSc. Biotechnology student of Shiv Nadar Institute of Imminence has collaborated with
IITM iGEM 2023 to continue the ChassiDex project. The ChassiDex project was started by Team iGEM IIT-Madras as an
online database of host organisms and related information. The resource also includes host specific software tools
and lists guidelines to help start with new host organisms. <br>
<br>
Kushi played a pivotal role in compiling data related to various Model and GRAS organisms, including animal models
(<i>Zebrafish, Drosophila, C. elegans</i> ), plant models (<i>Tobacco, Arabidopsis</i>), and other model organisms
(<i>Agrobacterium, Bacillus licheniformis, Trichoderma spp</i>) that we wished to include in the ChassiDex website.
This was followed by uploading the collated data onto a publically accessible GitHub platform as a part of the
ChassiDex project.<br>
<br>
The database can be accessed through the following link:<br><br>
<a href="https://chassidex.github.io/">https://chassidex.github.io/ </a> <br><br>
She has been very determined and optimistic, fostering collaboration throughout our exchange. This experience has
been very positive and enriching at both ends. We look forward to future collaborations.
</p>
</div>
<div class="contri" style="margin-bottom: 50px;">
<img src="https://static.igem.wiki/teams/4931/wiki/contri.png">
</div>
{% endblock %}
\ No newline at end of file
wiki/pages/description.html
View file @ 4af6db1e
......@@ -27,7 +27,7 @@
levels, providing a solid base of initial information for anyone using this chassis.</p>
<p style="font-size: 32px"><strong>Our RBS Predictor-Optimizer: Synthopedia!</strong></p>
<p>Our predictor-optimizer addresses the issue of fine tuning protein expression on a broader level, and contributes
to the groundwork to an in-silico solution to better control over expression dynamics. Harnessing machine learning
to the groundwork to an in silico solution to better control over expression dynamics. Harnessing machine learning
principles over a wider set of parameters, including both thermodynamic and biophysical data points, our team has
designed a machine learning model for protein expression, leveraging which, we are able to predict expression levels
for RBS sequences as well as optimize them for a particular expression level. The results of this model have been
......
wiki/pages/engineering.html
View file @ 4af6db1e
......@@ -16,7 +16,7 @@ engineering design cycle.{% endblock %}
<p><strong>WET LAB</strong></p>
<p>
In the lab, designing each minor experiment itself is an experience that takes one through the DBTL cycle. As
outlined in our Lab notebook, even staple molecular biology techniques like PCR and electroporation can prove to
outlined in our Lab Notebook, even staple molecular biology techniques like PCR and electroporation can prove to
be challenging when approaching it for the first time with new organisms and parts to test.
</p>
<p>
......@@ -95,9 +95,9 @@ engineering design cycle.{% endblock %}
<p>This thorough and systematic approach allowed us to gain a comprehensive understanding of the strengths and
weaknesses of each algorithm in the context of protein expression optimization. It also highlighted the
adaptability and robustness of the Genetic Algorithm in achieving the desired protein expression targets.</p>
<p>Our optimization algorithm was put to a final test by generating different RBS sequences for GFP expression in L.
lactis. This were then implemented in the wet lab. The results of this testing reflected a good differentiation
<p>Our optimization algorithm was put to a final test by generating different RBS sequences for GFP expression in <i>L.
lactis</i>. This were then implemented in the wet lab. The results of this testing reflected a good differentiation
between high and low expressing RBS variants. The results are described in detail under the 'Results' page.</p>
</div>
{% endblock %}
\ No newline at end of file
{% endblock %}
wiki/pages/home.html
View file @ 4af6db1e
......@@ -66,6 +66,6 @@ standardized genetic parts and systems in this beneficial chassis.{% endblock %}
</div>
<div style="margin-bottom: 50px;">
<img src="https://static.igem.wiki/teams/4931/wiki/application.svg" alt="Applications" class="img-fluid">
<img src="https://static.igem.wiki/teams/4931/wiki/applications.svg" alt="Applications" class="img-fluid">
</div>
{% endblock %}
\ No newline at end of file
{% endblock %}
wiki/pages/model.html
View file @ 4af6db1e
......@@ -27,26 +27,26 @@ endblock %}
</div>
<br>
<p style="margin-bottom: 0;">
To harness the capabilites of <i>L. lactis</i> for potential use as a probiotic treatment,we have developed modeled L.
lactis which is engineered as a probiotic treatment of homocystinuria. We hope to incorporate the RBS sequences
obtained and characterized through our current project in the model for optimizing it and wish to further
implement the genetic engineering in wet-lab for next year's project.
To harness the capabilites of <i>L. lactis</i> for potential use as a probiotic treatment, we have developed
a model for <i>L.
lactis</i> that has been engineered for the treatment of homocystinuria. We hope to incorporate the RBS sequences
obtained from our RBS optimization tool as a part of this model.
<br>
<br>
Homocystinuria is a rare genetic disorder characterized by the inability of the individual to metabolize
methionine. This causes an increased accumulation of homocysteine, an intermediate. This is known to cause
thrombotic events like strokes and heart attacks. We have attempted to engineer <i>L. Lactis</i> to convert
thrombotic events like strokes and heart attacks. We have attempted to engineer <i>L. lactis</i> to convert
methionine
to homocysteine. Being a GRAS organism makes <i>L. Lactis</i> an ideal chassis for developing probiotics.
to homocysteine. As it is a GRAS organism, <i>L. lactis</i> is an ideal chassis for developing probiotics.
<br>
<br>
However, <i>L. Lactis</i> does not possess the genes necessary to convert methionine to homocysteine and the
However, <i>L. lactis</i> does not possess the genes necessary to convert methionine to homocysteine and the
gene which
converts cystathionine to cysteine. We are aiming to engineer these genes into L .lactis and use an RBS sequence
converts cystathionine to cysteine. We aim to engineer these genes into <i>L. lactis</i> and use an RBS sequence
which can increase the expression of these genes.
<br>
<br>
<strong>Current Methionine metabolic capacities of <i>L. Lactis</i>:</strong>
<strong>Current methionine metabolic capacities of <i>L. lactis</i>:</strong>
<br>
<br>
Conversion of methionine to cysteine involves the following steps:
......@@ -61,18 +61,14 @@ endblock %}
<strong>1. Conversion of methionine to homocysteine</strong>
<br>
<br>
The image below shows the metabolic network of <i>L. Lactis</i> involved in the conversion of methionine to
The image below shows the metabolic network of <i>L. lactis</i> involved in the conversion of methionine to
homocysteine. The gene which converts an intermediate of methionine (S-adenosyl methionine) to
S-adenosyl-L-homocysteine needs to be engineered.
</p>
<br>
<img src="https://static.igem.wiki/teams/4931/wiki/modalimg12.png" alt="" style="width: 20%; height: 20%" />
<br>
<p>
<strong>2. Conversion of homocysteine to cystathione</strong>
<br>
Wild type <i>L. Lactis</i> has the ability to convert L-homocysteine to L-cystathionine. However, <i>L.
Lactis</i> also has
Wild type <i>L. lactis</i> has the ability to convert L-homocysteine to L-cystathionine. However, <i>L. lactis</i> also has
the ability to synthesize an enzyme cystathionine-gamma synthase. This enzyme converts L-cysathionine back to
L-homocysteine. We aim to knock out this gene.
</p>
......@@ -83,11 +79,9 @@ endblock %}
<strong>3. Conversion of L-cystathionine to L-cysteine</strong>
<br>
<br>
The enzyme which converts L-cystathionine to L-cysteine is natively absent in <i>L. Lactis</i> and must be
incorporated
into it. This enzyme however, is produced in yeast naturally, hence we propose engineering this enzyme into
<i>L.
lactis.</i> The image below shows the absence of enzyme in <i>L. Lactis</i> and presence of the same enzyme
The enzyme which converts L-cystathionine to L-cysteine is natively absent in <i>L. lactis</i> and must be
incorporated into it. This enzyme however, is produced in yeast naturally, hence we propose engineering this enzyme into
<i>L. lactis.</i> The image below shows the absence of enzyme in <i>L. lactis</i> and presence of the same enzyme
in S.
cerevisiae.
</p>
......@@ -98,14 +92,13 @@ endblock %}
<strong>4. Metabolism of L-cysteine</strong>
<br>
<br>
This occurs naturally in <i>L. Lactis</i>, however, we aim to use the RBS calculator to increase the expression
of the
carrier proteins which export L-cysteine from <i>L. Lactis</i> to the environment.
This occurs naturally in <i>L. lactis</i>. However, we plan to use the RBS Optimization tool to increase the expression
of the carrier proteins which export L-cysteine from <i>L. lactis</i> to the environment.
</p>
<br>
<p>
Based on the genes which need to be inserted and knocked out in <i>L. Lactis</i> (As mentioned above) for the
successful conversion of methionine to cysteine we have designed the genetic circuit.
Based on the genes which need to be inserted and knocked out in <i>L. lactis</i> for the
successful conversion of methionine to cysteine, we have designed a genetic circuit.
</p>
<br>
<img src="https://static.igem.wiki/teams/4931/wiki/modalimg15.png" alt="" />
......@@ -114,7 +107,7 @@ endblock %}
<h3>Model</h3>
</div>
<p>
To portray the application of our web tool- Synthopedia (RBS sequence generator), we aimed to model the
To portray the application of our web tool, we modelled the
transcription and translation of the inserted enzyme, and we aim to show that with the right Ribosome Binding
Sequence, we can increase the production of our enzyme of interest to the optimal rate.
<br>
......@@ -130,38 +123,39 @@ endblock %}
</ol>
</p>
<p>
On modeling this on SimBiology. We also plotted the production of protein, mRNA with respect to time. This is
the image obtained is as follows:
After modeling this on SimBiology, we plotted the production of protein, mRNA with respect to time.
</p>
<br>
<img src="https://static.igem.wiki/teams/4931/wiki/full-transcription-translation-1.svg" alt="">
<br>
<p>
Constants Considered:
<ul>
<li>Binding of RNAP to the promoter - [1]</li>
<li>Dissociation of RNAP-promoter complex to form mRNA - taken approximately</li>
<li>Binding of the mRNA to the Ribosome to form a mRNA-Ribosome complex - [2]</li>
<li>mRNA-ribosome complex dissociates to form the protein, mRNA and ribosome. - [2]</li>
</ul>
<ul>
<li>Binding of RNAP to the promoter - [1]</li>
<li>Dissociation of RNAP-promoter complex to form mRNA - taken approximately</li>
<li>Binding of the mRNA to the Ribosome to form a mRNA-Ribosome complex - [2]</li>
<li>mRNA-ribosome complex dissociates to form the protein, mRNA and ribosome. - [2]</li>
</ul>
</p>
<p>
Initial Concentrations Considered:
<ul>
<li>Concentration of promoter - 2.6*(e-9)</li>
<li>Concentration of RNAP - 1.9*e-5</li>
<li>Concentration of Ribosome - 4.4*e-5</li>
</ul>
<ul>
<li>Concentration of promoter - 2.6*(e-9)</li>
<li>Concentration of RNAP - 1.9*e-5</li>
<li>Concentration of Ribosome - 4.4*e-5</li>
</ul>
</p>
<div class="model-subheading2">
<h3>Future Directions</h3>
</div>
<p>
We aim to use constraint based modeling techniques to understand the optimal concentration of CGL enzyme that
We aim to use constraint-based modeling techniques to understand the optimal concentration of CGL enzyme that
needs to be produced. We aim to then use the RBS calculator to engineer the necessary RBS, to obtain optimal
protein production rate.
</p>
<h2 class="references"><strong>References</strong></h2>
<div class="model-subheading2">
<h3>References</h3>
</div>
<br />
<p class="references">
References:
......@@ -181,7 +175,7 @@ endblock %}
homocystinuria by determination of cystathionine-ß-synthase activity in plasma using LC-MS/MS. Clin Chim Acta.
2015;438:261–5
<br>
Kegg pathways: https://www.kegg.jp/pathway/map=lla00270&keyword=Methionine
KEGG Pathways: <a href="https://www.kegg.jp/pathway/map=lla00270&keyword=Methionine">https://www.kegg.jp/pathway/map=lla00270&keyword=Methionine</a>
</p>
<div class="model-subheading">
<h2>Kill Switch</h2>
......@@ -192,10 +186,10 @@ endblock %}
</div>
<br>
<p>
One of the core concerns in Synthetic biology is if Genetically Engineered Organisms (GEMs) pose a danger to
One of the core concerns in synthetic biology is if Genetically Engineered Organisms (GEOs) pose a danger to
their environment. Although precautions are taken, it is impossible to predict the interactions of a novel
organism in a system as complex as an ecosystem. We chose to address the need for bio-contaiment with a kill
switch, a genetic circuit designed to eliminate the GEMs in a controlled manner when certain conditions are met.
switch, a genetic circuit designed to eliminate the GEOs in a controlled manner when certain conditions are met.
The kill-switch designed here is activated in the low-glucose environment of the distal part of the human
digestive system.
</p>
......@@ -205,20 +199,20 @@ endblock %}
</div>
<br>
<p>
The kill switch works via the well-known mazEF Toxin-antitoxin (TA) system
The kill switch works via the well-known mazEF toxin-antitoxin (TA) system
<br>
<br>
<a href="https://2021.igem.org/Team:NYCU−Taipei/">https://2021.igem.org/Team:NYCU−Taipei/</a>
<br>
<br>
activated by the Carbohydrate response element binding protein (CHREBP) glucose sensing system. We used a
activated by the carbohydrate response element binding protein (CHREBP) glucose sensing system. We used a
glucose sensing system from
<br>
<br>
<a href="https://2019.igem.org/Team:NUDTCHINA/">https://2019.igem.org/Team:NUDTCHINA/</a>
<br>
<br>
The mazEF Toxin-antitoxin system is comprized of the toxin gene MazE and its antitoxin MazF. The antitoxin works
The mazEF toxin-antitoxin system is comprized of the toxin gene MazE and its antitoxin MazF. The antitoxin works
by forming a complex with the toxin that deactivates it. mazF inhibits translation thus halting protein
production in the cell[https://doi.org/10.1242/jcs.02619]. As the kill-switch is supposed to activate in the
deficit of glucose, the expression of mazE upwards regulated by the glucose sesnsing system in the abundance of
......@@ -286,7 +280,7 @@ endblock %}
but to keep it, and other metabolites at optimum levels. Achieving this level of finesse in modulating enzyme
activity while minimizing perturbations to other integral systems is a direct application of our RBS design
tools. Using an array of RBS sequences characterized by expression levels and the predictor optimizer tool, we
are able to easily add additional features like kill–switches, whose core toxin-antitoxin system requires fine
are able to easily add additional features like kill switches, whose core toxin-antitoxin system requires fine
control over expression activity.
</p>
</div>
......
wiki/pages/safety.html
View file @ 4af6db1e
......@@ -13,11 +13,11 @@
</div>
<div class="Content" style="max-height: 100%;">
<p>
Safety has always been our team's foremost concern, and we have taken comprehensive measures to ensure a secure working environment. Our project revolves around <i>Lactococcus lactis</i>, a well-known microorganism, and the strain we utilized, MC1636, is non-pathogenic, falling within the biosafety level 1 classification and is a Generally Regarded as Safe (GRAS) organism. We underwent rigorous training, guided by experts from Dr. Guhan Jayaraman's lab, to handle Lactococcus lactis effectively.
Safety has always been our team's foremost concern, and we have taken comprehensive measures to ensure a secure working environment. Our project revolves around <i>Lactococcus lactis</i>, a well-known microorganism, and the strain we utilized, MC1636, is non-pathogenic, falling within the biosafety level 1 classification and is a Generally Regarded as Safe (GRAS) organism. We underwent rigorous training, guided by experts from Dr. Guhan Jayaraman's lab, to handle <i>Lactococcus lactis</i> effectively.
<br><br>
<u>Our safety measures included</u> :
<u>Our safety measures included</u>:
<br><br>
<b>Laboratory Infrastructure</b> : We conducted our research in a biosafety level 1 laboratory, which is well-equipped with essential facilities, including:
<b>Laboratory Infrastructure</b>: We conducted our research in a biosafety level 1 laboratory, which is well-equipped with essential facilities, including:
<ul>
<li class="list-element">A biosafety cabinet</li>
<li class="list-element">A shaking incubator</li>
......@@ -31,9 +31,9 @@
</ul>
</p>
<p>
<b>Sterile Conditions</b> : We maintained a sterile work environment by performing all tasks within the confines of a biosafety cabinet. This ensured that accidental contamination did not occur, particularly important since our work could have repercussions on adjacent labs.
<b>Sterile Conditions</b>: We maintained a sterile work environment by performing all tasks within the confines of a biosafety cabinet. This ensured that accidental contamination did not occur, particularly important since our work could have repercussions on adjacent labs.
<br><br>
<b>Safety Protocols</b> : To minimize risks, we diligently followed safety protocols, including:
<b>Safety Protocols</b>: To minimize risks, we diligently followed safety protocols, including:
<ul>
<li class="list-element">Wearing gloves and masks during all experiments.</li>
<li class="list-element">Practicing thorough handwashing before leaving the laboratory.</li>
......@@ -42,7 +42,7 @@
</ul>
</p>
<p>
In summary, our commitment to safety in handling Lactococcus lactis is unwavering, with strict adherence to established protocols and stringent measures to maintain a secure work environment.
In summary, our commitment to safety in handling <i>Lactococcus lactis</i> is unwavering, with strict adherence to established protocols and stringent measures to maintain a secure work environment.
</p>
</div>
......