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static/style.css
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margin-bottom: 50px;
}
img.modalImg10 {
width: 70%;
}
@media screen and (max-width: 1080px) {
img.modalImg10 {
width: 80%;
}
}
h1 {
font-size: 36px;
/* Adjust the font size as needed */
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wiki/menu.html
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</a>
<div class="dropdown-menu bg-dark" aria-labelledby="awardsDropdown">
<a class="dropdown-item text-white" href="{{ url_for('pages', page = '/model') }}">Model</a>
<a class="dropdown-item text-white" href="{{ url_for('pages', page = 'software') }}">Software</a>
<a class="dropdown-item text-white" href="{{ url_for('pages', page = '/software') }}">Software</a>
<a class="dropdown-item text-white" href="{{ url_for('pages', page = '/education') }}">Education</a>
</div>
</li>
<li class="nav-item dark-mode-mobile">
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wiki/pages/contribution.html
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......@@ -10,33 +10,46 @@ endblock %}
<h1>CONTRIBUTIONS</h1>
</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>
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>
<br><br>
<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">
......@@ -47,22 +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">
......@@ -112,7 +115,7 @@ endblock %}
incorporated the MazEF toxin-antitoxin system as a kill-switch.</p>
</div>
<div class="hpHeading">
<h1>CHASSIDEX</h1>
<h1>ChassiDex</h1>
</div>
<div class="hpText row">
<p>
......@@ -121,9 +124,9 @@ endblock %}
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.
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>
......
wiki/pages/description.html
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......@@ -27,9 +27,9 @@
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 <i>in silico</i> 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
designed a machine learning model for protein expression prediction and optimization, 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
cross-tested against state of the art models.</p>
</div>
......
wiki/pages/education.html
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......@@ -11,11 +11,11 @@ with new communities by discussing public values and the science behind syntheti
</div>
<div class="hpText row">
<p class="col-lg-8">
Team iGEM IIT-Madras believes education is the strongest tool one can possess to combat the problems that the
Team iGEM IIT Madras believes education is the strongest tool one can possess to combat the problems that the
world is facing today. Throughout the course of our project, we conducted events targeting audiences from
university students and newly joined freshers to school students to teach them about basic synthetic biology
concepts and apply the knowledge they’ve accumulated to solve a crisis. Apart from this, we have continued the
Language Project started by Team iGEM IIT-Madras in 2018 by translating educational videos on our YouTube
Language Project started by Team iGEM IIT Madras in 2018 by translating educational videos on our YouTube
channel to break the barrier that language creates for information spread. We have also held hands-on sessions
for school students to create bio-art and teach them about bioethics to pique their interest in synthetic
biology and create the problem-solvers of tomorrow.
......@@ -94,6 +94,7 @@ with new communities by discussing public values and the science behind syntheti
the chance to have lunch amidst the greenery of the campus! The feedback we received was hugely positive. The
students got to have a fun and informative time. We plan to conduct more such visits, especially for schools in
underdeveloped regions.
<br><br>
<object data="https://static.igem.wiki/teams/4931/wiki/bioart-and-bioethics-school-session.pdf"
type="application/pdf" width="100%" height="850px">
<p>Unable to display PDF file. <a
......@@ -136,6 +137,7 @@ with new communities by discussing public values and the science behind syntheti
biology, participants received a comprehensive individual evaluation of their solutions, along with constructive
feedback to address any gaps in their reasoning or information. Needless to say, the event managed to ignite a
newfound interest in synthetic biology among many young undergraduates through an interactive game.
<br><br>
<object data="https://static.igem.wiki/teams/4931/wiki/synbio-auction.pdf" type="application/pdf" width="100%"
height="850px">
<p>Unable to display PDF file. <a
......@@ -164,14 +166,14 @@ with new communities by discussing public values and the science behind syntheti
Vice President for Biologics and Tumor Targeted Delivery, Ms. Anna Asberg, Vice President for R&D, IT, and Ms.
Hebe Midlemiss, Director, AI and Product Strategy, who spearheaded discussions during the offline event.
<br><br>
<strong>RESEARCH PITCH</strong><br>
<strong>RESEARCH PITCH</strong><br><br>
The event offered a platform for students to submit synopsis proposals on a range of pertinent topics,
including: <br>
1) Unleashing the Potential of Biologics through AI, ML, and Bioinformatics. <br>
2) Revolutionizing Drug Discovery through AI and In silico Methods. <br>
2) Revolutionizing Drug Discovery through AI and in <i>silico</i> Methods <br>
3) The Convergence of Synthetic Biology and Precision Medicine. <br>
4) In silico Approaches for Protein Engineering. <br>
<br><br>
4) In <i>silico</i> Approaches for Protein Engineering. <br>
<br>
Following a rigorous evaluation process by the AstraZeneca team, the top three submissions were chosen, and the
selected students had the privilege of presenting their research during the onground event in IIT Madras.
<br><br>
......@@ -179,14 +181,14 @@ with new communities by discussing public values and the science behind syntheti
event commenced with the students presenting their selected synopses, followed by an insightful Q&A session with
the judging panel, which included members of the AstraZeneca team.
<br><br>
<strong>PANEL DISCUSSION</strong> <br>
<strong>PANEL DISCUSSION</strong> <br> <br>
Subsequently, a panel discussion was held on the subject of unlocking the potential of biologics through AI, ML,
and Bioinformatics. This discussion was chaired by Dr. Puja Sapra and Ms. Anna Asberg, alongside Prof. Karthik
Raman and Prof. Meiyappan Lakshmanan from the Department of Biotechnology at IIT Madras, and students from the
departments of Computer Science and Biotechnology. It served as a platform for a rich exchange of insights into
the current applications of AI/ML in biology and the promising future prospects.
the current applications of AI/ML in biology and the promising future prospects. <br><br>
<strong>DEBATE</strong> - Does Science Drive Technology or is it the other way?
<br><br>
<br>
The event's highlight was an engaging debate session, moderated by Ms. Hebe Middlemiss, where participants,
including the AstraZeneca team, professors, and students, passionately debated whether science drives technology
or vice versa. Both sides presented compelling arguments, ultimately arriving at a consensus that science plays
......@@ -268,7 +270,7 @@ with new communities by discussing public values and the science behind syntheti
</div>
<div class="hpText row">
<p class="col-lg-8">
As part of an orientation program for aspiring IIT-M students, team iGEM had the opportunity to showcase the
As part of an orientation program for aspiring IITM students, team iGEM had the opportunity to showcase the
product of our yearlong efforts to a team of enthusiastic young minds, and introduce them to the world of
synthetic biology. As a team representing the cutting edge biological innovation, it was our privilege to
demonstrate to our peers and aspirants just what iGEM and more broadly, synthetic biology, entailed. Moreover,
......@@ -277,7 +279,7 @@ with new communities by discussing public values and the science behind syntheti
towards biology within an engineering framework was a hurdle all of us at iGEM IITM had to face at some point
within our campus lives. Therefore the opportunity to defend our work, and by extension display the sheer extent
and unimaginable capability of “bio”-tech enabled us to change quite a few minds on their perspective towards
biological engineering as a field. <br>
biological engineering as a field. <br><br>
<img src="https://static.igem.wiki/teams/4931/wiki/aspirants.jpeg" class="eduimgs synaut1">
<img src="https://static.igem.wiki/teams/4931/wiki/aspirants-1.jpeg" class="eduimgs synaut2">
</p>
......
wiki/pages/experiments.html
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......@@ -82,7 +82,7 @@
To bind DNA, apply the sample to the QIAquick column and centrifuge for 1 min or apply vacuum to the manifold
until all the samples have passed through the column. Discard flow-through and place the QIAquick column back into
the same tube. For sample volumes >800 μl, load and spin/apply vacuum again.</li>
<li class="list-element">If DNA will subsequently be used for sequencing, in vitro transcription, or microinjection,
<li class="list-element">If DNA will subsequently be used for sequencing, <i>in vitro</i> transcription, or microinjection,
add 500 μl of Buffer QG to the QIAquick column and centrifuge for 1 min or apply vacuum. Discard flow-through and
place the QIAquick column back into the same tube.</li>
<li class="list-element">To wash, add 750 μl of Buffer PE to the QIAquick column and centrifuge for 1 min or apply
......@@ -166,7 +166,5 @@
<li class="list-element">The colonies were picked for overnight cultures with appropriate antibiotics and used for
creating glycerol stocks.</li>
</ul>
<h2 class="references">References</h2>
{% endblock %}
\ No newline at end of file
wiki/pages/human-practices.html
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......@@ -13,7 +13,7 @@ the world. Consider how the world affects your work and how your work affects th
</div>
<div class="hpText row">
<p class="col-lg-8">
As team iGEM IIT-Madras, we value how our project impacts the research being conducted in labs worldwide as well
As team iGEM IIT Madras, we value how our project impacts the research being conducted in labs worldwide as well
as how we can apply it to various industrial domains including food and cosmetic technology, production of
recombinant proteins for therapeutic purposes and biomanufacturing. To ensure that our project for this year’s
iGEM is relevant for scientists and industries, we took initiatives to meet them and get their feedback as
......@@ -21,7 +21,7 @@ the world. Consider how the world affects your work and how your work affects th
Salis who worked on developing the popularly used RBS Calculator. Based on his inputs, we detailed our model
further. To ensure that our project can be scaled up, we had talks with various well known companies like Biocon
and incorporated their inputs. We are optimistic that our project will greatly help researchers and
industrialists in fine tuning gene expression and getting higher product yields.
industrialists in fine-tuning gene expression and getting higher product yields.
</p>
</div>
<div class="hpHeading">
......@@ -47,7 +47,7 @@ the world. Consider how the world affects your work and how your work affects th
species, may play a significant role in translation efficiency and
including their effect in the model could lead to more accurate
predictions. Additionally, Dr. de Lorenzo proposed the utilization of
an in vivo recombineering tool to expand the size and diversity of our
an <i>in vivo</i> recombineering tool to expand the size and diversity of our
ribosome binding site (RBS) library. This would enable us to diversify
our toolkit more effectively.
<br><br>
......@@ -60,7 +60,7 @@ the world. Consider how the world affects your work and how your work affects th
<img src="https://static.igem.wiki/teams/4931/wiki/a-meeting-with-professor-victor-de-lorenzo.png"
class="eduimgs synaut3">
</p>
</div>
......@@ -70,10 +70,10 @@ the world. Consider how the world affects your work and how your work affects th
<div class="hpText row">
<p class="col-lg-8">
Our team has the broad goal to develop a library of synthetic ribosome binding site (RBS) sequences for
fine-tuning protein expression in Lactococcus lactis. The project also included the creation of an in silico tool
to predict the expression level of a gene and optimize its RBS sequence to achieve a desired expression level.
fine-tuning protein expression in <i>Lactococcus lactis</i>. The project also included the creation of an in <i>silico</i> tool
to predict the expression level of a gene and optimise its RBS sequence to achieve a desired expression level.
Additionally, we wanted to explore applications of RBS engineering in the development of a probiotic treatment for
homocystinuria and in the modulation of the molecular weight of hyaluronic acid produced by recombinant L. lactis.
homocystinuria and in the modulation of the molecular weight of hyaluronic acid produced by recombinant <i>L. lactis</i>.
<br><br>
Seeking expert guidance, we reached out to Professor Howard Salis, a prominent figure in the field of synthetic
biology, and the creator of the widely used RBS Calculator. The correspondence between our team and Prof. Salis
......@@ -148,13 +148,13 @@ the world. Consider how the world affects your work and how your work affects th
<p class="col-lg-8">
Biocon, a leading and innovative biopharmaceutical company known for its development of affordable biosimilars,
generic formulations, and complex APIs, warmly received our project presentation. During our presentation, we
introduced them to our RBS calculator and optimizer software, providing insights into their development,
introduced them to our RBS calculator and optimiser software, providing insights into their development,
parameter choices, and advantages over existing standards. <br> <br>
We discussed the development process, emphasizing the precision and reliability of our RBS calculator and
optimizer software. We also explained the key parameters and algorithms used to fine-tune gene expression,
optimiser software. We also explained the key parameters and algorithms used to fine-tune gene expression,
highlighting how our software offers improved control and predictability in bioengineering. <br> <br>
Our presentation showcased how our software surpasses current industry standards, offering a more efficient and
streamlined approach to optimizing gene expression. This is particularly crucial in biopharmaceutical research
streamlined approach to optimising gene expression. This is particularly crucial in biopharmaceutical research
and production. <br><br>
We engaged in a discussion about the potential for advancing our project and collaborating with Biocon for
real-world applications, especially due to their research in the field of recombinant proteins like
......@@ -162,7 +162,7 @@ the world. Consider how the world affects your work and how your work affects th
Biocon's representatives expressed their enthusiasm for our project and its potential impact, and they offered
to sponsor further research and development efforts to support innovation in the biopharmaceutical industry.
<br><br>
Biocon also extended an invitation to IIT Madras for an industry visit to their Headquarters in Bangalore,
Biocon also extended an invitation to IIT Madras for an industry visit to their headquarters in Bangalore,
providing us with an opportunity to witness their research action and explore potential synergies in person.
<br><br>
Distinguished individuals from Biocon, including Dr. Nagaraj, Dr. Krishna Rao Kamisetty, Dr. Naga Sirisha
......@@ -174,11 +174,11 @@ the world. Consider how the world affects your work and how your work affects th
</p>
</div>
<div class="hpHeading">
<h1>Conversing with Hushhai</h1>
<h1>Conversing with hushh.ai</h1>
</div>
<div class="hpText row">
<p class="col-lg-8">
As part of our efforts in making the tech-bio intersection accessible, iGEM IIT-M reached out to Manish Sainani,
As part of our efforts in making the tech-bio intersection accessible, iGEM IITM reached out to Manish Sainani,
ex–Director of Product Management at Google and founder of hushh.ai. In an in-person session, our team laid out
our perspective of the synthetic biology landscape, and where we hoped our in-house tools would make a
significant impact. As a founder in the tech-AI sphere, Mr. Sainani’s expert opinion on the potential
......@@ -242,9 +242,9 @@ the world. Consider how the world affects your work and how your work affects th
Advantages and disadvantages of various recombinant protein expression systems were discussed. She emphasized on
the importance of choosing the right chassis based on speed, cost, yield, post-translational modifications,
process development and scalability regulation approval. After receiving this insightful knowledge from her, we
discussed with her the scope of using Lactococcus lactis as an expression system since it is a GRAS organism and
discussed with her the scope of using <i>Lactococcus lactis</i> as an expression system since it is a GRAS organism and
has always been used in food technology. She greatly appeciated our initiatives in expanding the genetic
engineering toolkit for L. lactis.
engineering toolkit for <i>L. lactis</i>.
<br><br>
Finally the session concluded with an insightful discussion on whether synthetic meat proteins can ever truly
replace meat-derived proteins considering the social significance of meat. Even though it may not be widely
......
wiki/pages/model.html
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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.
in <i>S. cerevisiae</i>.
</p>
<br>
<img src="https://static.igem.wiki/teams/4931/wiki/modalimg14.png" alt="" />
......@@ -94,9 +93,6 @@ endblock %}
<br>
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> for the
successful conversion of methionine to cysteine, we have designed a genetic circuit.
</p>
......@@ -108,13 +104,13 @@ endblock %}
</div>
<p>
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.
transcription and translation of the inserted enzyme, and we aim to show that with the right RBS,
we can increase the production of our enzyme of interest to the optimal rate.
<br>
We have modeled the transcription and translation as follows:
<ol>
<li>Binding of the RNA polymerase to the promoter to form a promoter-RNAP complex:
<br>RNAP + promoter -> promoter-RNAP
<br>RNAP + promoter → promoter-RNAP
</li>
<li>The promoter transcription complex dissociates to give back the promoter, RNA polymerase and the mRNA
sequence.</li>
......@@ -140,9 +136,9 @@ endblock %}
<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>
<li>Concentration of promoter - 2.6x10<sup>-9</sup></li>
<li>Concentration of RNAP - 1.9x10<sup>-5</sup></li>
<li>Concentration of ribosome - 4.4x10<sup>-5</sup></li>
</ul>
</p>
<div class="model-subheading2">
......@@ -158,8 +154,6 @@ endblock %}
</div>
<br />
<p class="references">
References:
<br>
[1] Újvári A, Martin C. Thermodynamic and Kinetic Measurements of Promoter Binding by T7 RNA Polymerase.
Biochemistry. 1996;35(46):14574-14582.
<br>
......@@ -190,7 +184,7 @@ endblock %}
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 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
The kill switch designed here is activated in the low-glucose environment of the distal part of the human
digestive system.
</p>
<br>
......@@ -200,21 +194,11 @@ endblock %}
<br>
<p>
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
glucose sensing system from
<br>
<br>
<a href="https://2019.igem.org/Team:NUDTCHINA/">https://2019.igem.org/Team:NUDTCHINA/</a>
<br>
<br>
glucose sensing system from the 2019 NUDT China iGEM team.
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
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
glucose. As is generally the case in TA systems [https://doi.org/10.4161%2Fmge.26219], The antitoxin is degraded
quickly by the clPAP protease if it’s production ceases. The expression of the toxin, MazF is by a
......@@ -255,9 +239,9 @@ endblock %}
<br>
<p>
We have assumed a 1000-fold difference in glucose levels in the stomach and dis-
tal digestive tract, following data from [https://doi.org/10.1152/ajpgi.1990.259.5.g822]. As seen in fig. 2, the
tal digestive tract, following data from [https://doi.org/10.1152/ajpgi.1990.259.5.g822]. As seen in figure 2, the
antitoxin mazE is greater in concentration than the toxin mazF in high glucose conditions. This corresponds to
the cell continuing to be alive. From fig.4, illustrating the low glucose condition, we can see that the
the cell continuing to be alive. From figure 4, illustrating the low glucose condition, we can see that the
concentration of mazF is greater than mazE, corresponding to cell death.
</p>
<br>
......@@ -278,8 +262,8 @@ endblock %}
living organism in all its complexity, with minimal loss of product function. In our approach to resolving
homocystinuria, it is to be noted that the approach is not to maximize the production of the required enzymes
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
activity while minimizing perturbations to other integral systems is a direct application of our prediction and optimization
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
control over expression activity.
</p>
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</button>
<div class="collapse-expand">
<div class="model-subheading2">
<h3>Expression Prediction and RBS Optimisation</h3>
<h3>Expression Prediction and RBS optimization</h3>
</div>
<div class="Content" style="max-height: 100%;">
<p><strong>June</strong></p>
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<td class="main-col">Week 3</td>
<td class="activities">
<ul>
<li>Defining the interaction between the rRNA and mRNA so as to have minimum hybridisation energy</li>
<li>Defining the interaction between the rRNA and mRNA so as to have minimum hybridization energy</li>
<li>Including the effect of spacing between the SD sequence and the start codon</li>
<li>Defining the interaction between the rRNA and the ribosomal S1 protein</li>
<li>Accounting for the transient regional folding of the mRNA that can hinder RBS accessibility</li>
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<ul>
<li>Downloading a dataset of 16779 RBS sequences characterised by FlowSeq from Reis and Salis (2020) and
testing the model on it</li>
<li>Reading up on different optimisation algorithms that can be used to "mutate" an RBS sequence in
<li>Reading up on different optimization algorithms that can be used to "mutate" an RBS sequence in
silico
in order to achieve a desired expression level</li>
</ul>
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<li>A partially working genetic algorithm was created, work is yet to be finished on this</li>
<li>The two working optimisers were used to generate RBS sequences with varying expression levels. These
sequences will be used in the wet lab using GFP-expressing Lactococcus lactis to see if the
optimisation
optimization
was successful.</li>
</ul>
</td>
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<td class="main-col">Week 2</td>
<td class="activities">
<ul>
<li>Attempted to combine different aspects to the optimization algorithms.</li>
<li>Attempted to combine different aspects to the Optimization algorithms.</li>
<li>Started work on adding new parameters to the model to improve accuracy of predictions. Literature
review on RNA folding kinetics and RNA degradation was initiated.</li>
</ul>
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<td class="activities">
<ul>
<li>Recruitment of the team completed</li>
<li>Installed and initialised MATLAB and SimBiology. Understood how to use SimBiology for solving ODEs
<li>Installed and initialized MATLAB and SimBiology. Understood how to use SimBiology for solving ODEs
and running mass kinetic models
</li>
</ul>
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<td class="main-col">Week 3</td>
<td class="activities">
<ul>
<li>Identified the genes that must be inserted in L.lactis to help it successfully convert cysteine to
<li>Identified the genes that must be inserted in <i>L. lactis</i> to help it successfully convert cysteine to
methionine and identified which gene must be knocked out.
</li>
<li>Initial transcription modelling process started off by modelling the binding of the RNA polymerase
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<ul>
<li>Completed transcription modelling by modelling the rate of transcription by the RNA polymerase.
</li>
<li>Started off with the translation modelling by modelling the binding of the Ribosome to the ribosome
<li>Started off with the translation modelling by modelling the binding of the Ribosome
to the RBS of the mRNA
</li>
</ul>
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<td class="main-col">Week 3</td>
<td class="activities">
<ul>
<li>Modelled the conversion of homocysteine to methionine by the engineered enzyme in L.lactis
<li>Modelled the conversion of homocysteine to methionine by the engineered enzyme in <i>L. lactis</i>
</li>
</ul>
</td>
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<td class="main-col">Week 4</td>
<td class="activities">
<ul>
<li>Compiled all the small processes modelled, we have written together to obtain the final model for
<li>Compiled all the small processes modelled to obtain the final model for
the system
</li>
</ul>
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</div>
<div class="Content" style="max-height: 100%;">
<p>
The aim of this wet lab project was to construct a library of RBS parts for L. lactis. With enough variation in the expression of each strain with a particular sequence, we can enable fine-tuning of genes for multiple metabolic engineering goals using this organism.
The aim of this wet lab project was to construct a library of RBS parts for <i>L. lactis</i>. With enough variation in the expression of each strain with a particular sequence, we can enable fine-tuning of genes for multiple metabolic engineering goals using this organism.
<br><br>
We also intended to test out computationally generated sequences based on our very own RBS model and understand if we are able to predict expression levels reliably.
We also intended to test out computationally generated sequences based on our very own predictor-optimizer model and understand if we are able to predict expression levels reliably.
<br><br>
What we learned from our results is that research is always a mixed bag. We win some and lose some. When we ‘lose’, it indicates that we need to go back to the drawing board, figure out what went wrong, and try again. This cycle of design-build-test-learn happens both at the small scale of day-to-day experiments and at the large scale of testing out new hypotheses. This is illustrated clearly in our lab notebook and our engineering success.
<br><br>
<b>Fluorescence Assay Results :</b>
<b>Fluorescence Assay Results</b>
<ul class="list">
<li class="list-element">Fluorescence readings are adjusted for OD and negative control values.</li>
<img src="https://static.igem.wiki/teams/4931/wiki/results.png" id="resultImg" alt="Results">
<li class="list-element">Looking at our results, we have been able to successfully characterize a library of 24 RBS sequences with a good variable range of expression.</li>
<li class="list-element">Most of the sequences that were computationally generated were quite low expressing compared to our positive control (PJ102 - natural RBS of L. lactis).</li>
<li class="list-element">Most of the sequences that were computationally generated were quite low expressing compared to our positive control (PJ102 - natural RBS of <i>L. lactis</i>).</li>
<li class="list-element">The strains generated through randomization of the native RBS have shown a large steady range of variation from low to medium to similar expression levels as the positive control.</li>
<li class="list-element">One surprise for us was the RBS15 from the computationally generated set that had twice the expression level of the control.</li>
<li class="list-element">Future work would include integrating the sequencing data from the randomized strains into the RBS model and attempting to predict the expression of the computationally generated sequences using the data from the randomized variants. Characterizing a larger randomized library including sequences that also show higher expression than that of the control will also give us higher tuning ability.</li>
<li class="list-element">This would be beneficial to attempt to correct the bias in the original dataset used to train the model and also train the model on L. lactis-specific data to ascertain if better predictions can be made on the computationally generated sequences.</li>
<li class="list-element">Our RBS model showed good performance on the test set which was similar to the dataset it was trained on. We suspect that due to the dearth of gram-positive bacteria data in the dataset, our model hasn't been able to generalize well to accurately predict expression in Lactococcus lactis.</li>
<li class="list-element">Future work would include integrating the sequencing data from the randomized strains into the predictor-optimizer model and attempting to predict the expression of the computationally generated sequences using the data from the randomized variants. Characterizing a larger randomized library including sequences that also show higher expression than that of the control will also give us higher tuning ability.</li>
<li class="list-element">This would be beneficial to attempt to correct the bias in the original dataset used to train the model and also train the model on <i>L. lactis</i>-specific data to ascertain if better predictions can be made on the computationally generated sequences.</li>
<li class="list-element">Our predictor-optimizer model showed good performance on the test set which was similar to the dataset it was trained on. We suspect that due to the dearth of gram-positive bacteria data in the dataset, our model hasn't been able to generalize well to accurately predict expression in <i>Lactococcus lactis</i>.</li>
<li class="list-element">This work can also be carried forward by future iGEM teams from our institute, setting a foundation for next year’s project.</li>
</ul>
</p>
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<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.
<br><br>
The RBS Rate Prediction Tool accepts the following input parameters: RBS Sequence, Coding Sequence, Temperature,
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>
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reduced.
</p>
<br>
<img src="https://static.igem.wiki/teams/4931/wiki/modalimg10.png" alt="" />
<img src="https://static.igem.wiki/teams/4931/wiki/modalimg27.png" alt="" class="modalImg10" />
<br>
<p >
<strong>Feature #8 - Gram stain</strong>
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<br />
<br />
[3] Vellanoweth, R. L., & Rabinowitz, J. C. (1992). The influence of
ribosome-binding-site elements on translational efficiency in Bacillus
subtilis and Escherichia coli in vivo. Molecular Microbiology, 6(9),
ribosome-binding-site elements on translational efficiency in <i>Bacillus
subtilis</i> and <i>Escherichia coli</i> <i>in vivo</i>. Molecular Microbiology, 6(9),
1105–1114. https://doi.org/10.1111/j.1365-2958.1992.tb01548.x
<br />
<br />
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