From 7bd325919f094a7a4fc3bfe61056e6d509be1838 Mon Sep 17 00:00:00 2001
From: Aadityanshu Abhinav <aaadityanshu@gmail.com>
Date: Wed, 2 Oct 2024 13:59:56 +0000
Subject: [PATCH] Update file software.html
---
wiki/pages/software.html | 1934 +++++++++++++++++++-------------------
1 file changed, 967 insertions(+), 967 deletions(-)
diff --git a/wiki/pages/software.html b/wiki/pages/software.html
index ca2e0e5..f45374b 100644
--- a/wiki/pages/software.html
+++ b/wiki/pages/software.html
@@ -1,1002 +1,1002 @@
{% extends "layout.html" %} {% block title %}Software{% endblock %} {% block
-lead %}Software in iGEM should make synthetic biology based on standard parts
-easier, faster, better or more accessible to our community.{% endblock %} {%
-block page_content %}
-
-<div id="root">
- <link
- href="{{ url_for('static', filename = 'Gotham_Thin.otf') }}"
- rel="stylesheet"
- />
- <link
- href="{{ url_for('static', filename = 'GlacialIndifference-Regular.otf') }}"
- rel="stylesheet"
- />
- <script type="text/x-mathjax-config">
- MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}});
- </script>
- <script
- type="text/javascript"
- src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.min.js"
- ></script>
- <style>
- * {
- margin: 0;
- padding: 0;
- box-sizing: border-box;
- }
-
- html {
- scroll-behavior: smooth;
- }
-
- .page {
- font-family: "glacial_indifference";
- /* font-weight: bold; */
-
- color: white;
- }
-
- @font-face {
- font-family: "Gotham-Thin";
- src: url("{{ url_for('static', filename = 'Gotham_Thin.otf') }}");
- font-weight: normal;
- font-style: normal;
- font-display: swap;
- }
-
- @font-face {
- font-family: glacial_indifference;
- font-style: normal;
- font-weight: 400;
- src: local("Glacial Indifference"),
- url("{{ url_for('static', filename = 'GlacialIndifference-Regular.otf') }}");
- }
- @font-face {
- font-family: glacial_indifference;
- font-style: normal;
- font-weight: bold;
- src: local("Glacial Indifference"),
- url("{{ url_for('static', filename = 'GlacialIndifference-Bold.otf') }}");
- }
-
- .page {
- max-width: 1000px;
- margin: 0 auto;
- }
-
- /* Mars Image */
- .mars-image {
- width: 150px;
- height: 150px;
- display: block;
- margin: 0 auto;
- }
-
- /* Page Title */
- .title h1 {
- font-size: 3em;
- font-weight: bold;
- text-align: center;
- margin-top: 0px;
- font-family: "gotham thin";
- }
-
- /* Layout for TOC and Content */
- .layout {
- display: flex;
- justify-content: space-between;
- align-items: flex-start;
- margin-top: -15%;
- }
-
- /* Table of Contents (Left side) */
- .toc {
- flex-basis: 20%;
- text-align: left;
- padding-right: 20px;
- border-right: 2px solid white;
- position: sticky;
- top: 20vw;
- margin-top: 15%;
- }
-
- /* Active TOC link */
- .toc ul li a.active {
- color: #ffdd00;
- /* Highlight color */
- font-size: 1em;
- /* Larger size to highlight */
- }
-
- .toc h2 {
- font-size: 1.5em;
- margin-bottom: 10px;
- }
-
- .toc ul {
- list-style-type: none;
- padding: 0;
- }
-
- .toc ul li {
- margin: 10px 0;
- }
-
- .toc ul li a {
- color: #f0d278;
- text-decoration: none;
- font-weight: bold;
- }
-
- .toc ul li a:hover {
- text-decoration: none;
- }
-
- /* Content Sections (Right side) */
- .content {
- flex-basis: 75%;
- padding-left: 20px;
- width: 80%;
- display: flex;
- flex-direction: column;
- justify-content: space-evenly;
- }
- .section h1 {
- color: #f0d278;
- font-weight: bold;
- margin: 8rem 0 2rem 0;
- text-align: center;
- font-size: 70px;
- }
-
- .section h2 {
- color: #f0d278;
- font-weight: bold;
- margin: -1rem 0 2rem 0;
- text-align: center;
- font-size: 30px;
- }
-
- .section p {
- font-size: 0.9em;
- text-align: justify;
- line-height: 1.6;
- }
-
- .section ul {
- font-size: 0.9em;
- }
-
- .mars_div {
- margin-top: 2rem;
- }
-
- .textukas {
- font-size: 450%;
- }
-
- @media screen and (max-width: 1024px) {
- .mars_div {
- margin-top: 3rem;
+ lead %}Software in iGEM should make synthetic biology based on standard parts
+ easier, faster, better or more accessible to our community.{% endblock %} {%
+ block page_content %}
+
+ <div id="root">
+ <link
+ href="{{ url_for('static', filename = 'Gotham_Thin.otf') }}"
+ rel="stylesheet"
+ />
+ <link
+ href="{{ url_for('static', filename = 'GlacialIndifference-Regular.otf') }}"
+ rel="stylesheet"
+ />
+ <script type="text/x-mathjax-config">
+ MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}});
+ </script>
+ <script
+ type="text/javascript"
+ src="{{ url_for('static', filename = 'latex.js') }}"
+ ></script>
+ <style>
+ * {
+ margin: 0;
+ padding: 0;
+ box-sizing: border-box;
+ }
+
+ html {
+ scroll-behavior: smooth;
}
- }
-
- @media screen and (max-width: 768px) {
+
.page {
- padding: 2rem 0 0 0;
+ font-family: "glacial_indifference";
+ /* font-weight: bold; */
+
+ color: white;
}
-
+
+ @font-face {
+ font-family: "Gotham-Thin";
+ src: url("{{ url_for('static', filename = 'Gotham_Thin.otf') }}");
+ font-weight: normal;
+ font-style: normal;
+ font-display: swap;
+ }
+
+ @font-face {
+ font-family: glacial_indifference;
+ font-style: normal;
+ font-weight: 400;
+ src: local("Glacial Indifference"),
+ url("{{ url_for('static', filename = 'GlacialIndifference-Regular.otf') }}");
+ }
+ @font-face {
+ font-family: glacial_indifference;
+ font-style: normal;
+ font-weight: bold;
+ src: local("Glacial Indifference"),
+ url("{{ url_for('static', filename = 'GlacialIndifference-Bold.otf') }}");
+ }
+
+ .page {
+ max-width: 1000px;
+ margin: 0 auto;
+ }
+
+ /* Mars Image */
+ .mars-image {
+ width: 150px;
+ height: 150px;
+ display: block;
+ margin: 0 auto;
+ }
+
+ /* Page Title */
+ .title h1 {
+ font-size: 3em;
+ font-weight: bold;
+ text-align: center;
+ margin-top: 0px;
+ font-family: "gotham thin";
+ }
+
+ /* Layout for TOC and Content */
.layout {
- flex-direction: column;
- align-items: center;
+ display: flex;
+ justify-content: space-between;
+ align-items: flex-start;
+ margin-top: -15%;
}
-
- /* Table of contents on top in mobile */
+
+ /* Table of Contents (Left side) */
.toc {
- flex-basis: 100%;
- text-align: center;
+ flex-basis: 20%;
+ text-align: left;
+ padding-right: 20px;
+ border-right: 2px solid white;
+ position: sticky;
+ top: 20vw;
+ margin-top: 15%;
+ }
+
+ /* Active TOC link */
+ .toc ul li a.active {
+ color: #ffdd00;
+ /* Highlight color */
+ font-size: 1em;
+ /* Larger size to highlight */
+ }
+
+ .toc h2 {
+ font-size: 1.5em;
+ margin-bottom: 10px;
+ }
+
+ .toc ul {
+ list-style-type: none;
padding: 0;
- border-right: none;
- position: static;
- text-align: center;
}
-
- /* Add a horizontal line separator between TOC and content */
- .toc::after {
- content: "";
- display: block;
- width: 100%;
- height: 1px;
- background-color: white;
- margin: 20px 0;
+
+ .toc ul li {
+ margin: 10px 0;
+ }
+
+ .toc ul li a {
+ color: #f0d278;
+ text-decoration: none;
+ font-weight: bold;
}
-
+
+ .toc ul li a:hover {
+ text-decoration: none;
+ }
+
+ /* Content Sections (Right side) */
.content {
- flex-basis: 100%;
- padding: 0;
+ flex-basis: 75%;
+ padding-left: 20px;
+ width: 80%;
+ display: flex;
+ flex-direction: column;
+ justify-content: space-evenly;
}
-
.section h1 {
- margin-top: 2rem;
- font-size: 45px;
+ color: #f0d278;
+ font-weight: bold;
+ margin: 8rem 0 2rem 0;
+ text-align: center;
+ font-size: 70px;
+ }
+
+ .section h2 {
+ color: #f0d278;
+ font-weight: bold;
+ margin: -1rem 0 2rem 0;
+ text-align: center;
+ font-size: 30px;
+ }
+
+ .section p {
+ font-size: 0.9em;
+ text-align: justify;
+ line-height: 1.6;
}
-
+
+ .section ul {
+ font-size: 0.9em;
+ }
+
.mars_div {
- margin-top: 5rem;
+ margin-top: 2rem;
}
-
+
.textukas {
- font-size: 250%;
+ font-size: 450%;
}
-
- .toc {
- display: none;
+
+ @media screen and (max-width: 1024px) {
+ .mars_div {
+ margin-top: 3rem;
+ }
}
- }
- </style>
- <div class="mars_div" style="position: relative; overflow: hidden">
- <img
- src="https://static.igem.wiki/teams/5155/mars.webp"
- style="padding: 10%; padding-top: 12%; padding-left: 0; padding-right: 0"
- />
- <div
- class="textukas"
- style="
- position: absolute;
- top: 52%;
- left: 50%;
- transform: translate(-50%, -50%);
- font-weight: 1000;
- color: rgb(224, 224, 210);
- z-index: 11;
- text-align: center;
- font-family: gotham-thin;
- text-shadow: rgba(255, 255, 255, 0.7) 0px 0px 15px;
- "
- >
- SOFTWARE
- </div>
- </div>
- <div class="page">
- <!-- Mars image and page title -->
-
- <div class="layout">
- <!-- Table of contents (on the left) -->
- <div class="toc">
- <!-- <h2>Table of Contents</h2> -->
- <ul>
- <li><a href="#section2">Inspiration</a></li>
- <li><a href="#section3">Pipeline</a></li>
- <li><a href="#section4">Web Tool</a></li>
- <li><a href="#section5">Results</a></li>
- </ul>
+
+ @media screen and (max-width: 768px) {
+ .page {
+ padding: 2rem 0 0 0;
+ }
+
+ .layout {
+ flex-direction: column;
+ align-items: center;
+ }
+
+ /* Table of contents on top in mobile */
+ .toc {
+ flex-basis: 100%;
+ text-align: center;
+ padding: 0;
+ border-right: none;
+ position: static;
+ text-align: center;
+ }
+
+ /* Add a horizontal line separator between TOC and content */
+ .toc::after {
+ content: "";
+ display: block;
+ width: 100%;
+ height: 1px;
+ background-color: white;
+ margin: 20px 0;
+ }
+
+ .content {
+ flex-basis: 100%;
+ padding: 0;
+ }
+
+ .section h1 {
+ margin-top: 2rem;
+ font-size: 45px;
+ }
+
+ .mars_div {
+ margin-top: 5rem;
+ }
+
+ .textukas {
+ font-size: 250%;
+ }
+
+ .toc {
+ display: none;
+ }
+ }
+ </style>
+ <div class="mars_div" style="position: relative; overflow: hidden">
+ <img
+ src="https://static.igem.wiki/teams/5155/mars.webp"
+ style="padding: 10%; padding-top: 12%; padding-left: 0; padding-right: 0"
+ />
+ <div
+ class="textukas"
+ style="
+ position: absolute;
+ top: 52%;
+ left: 50%;
+ transform: translate(-50%, -50%);
+ font-weight: 1000;
+ color: rgb(224, 224, 210);
+ z-index: 11;
+ text-align: center;
+ font-family: gotham-thin;
+ text-shadow: rgba(255, 255, 255, 0.7) 0px 0px 15px;
+ "
+ >
+ SOFTWARE
</div>
-
- <!-- Content Sections (on the right) -->
- <div class="content">
- <div id="section1" class="section">
- <p><br /></p>
- <p><br /></p>
- <p style="text-align: justify">
- Chassis selection is a critical early decision in any synthetic
- biology project, particularly when working in environments beyond
- Earth. As we push the boundaries of In Situ Resource Utilization
- (ISRU), the need to choose organisms that can efficiently harness
- local minerals and resources becomes paramount. Traditional
- laboratory organisms like <em>E. coli</em> dominate
- research due to their well-understood biology. However, their
- suitability for non-terrestrial environments often requires
- extensive genetic engineering to meet the demands of space-based
- resource constraints.
- </p>
- <p style="text-align: justify">
- To address this challenge, our team
- developed <strong>Astrolabe</strong>, a software tool designed
- to assist researchers in selecting the most appropriate chassis
- organism based on specific environmental resources and physiological
- conditions. By leveraging available resources and minimizing the
- engineering required, Astrolabe provides a ranked list of suitable
- organisms, offering a streamlined path to achieving desired
- biological outputs with maximum efficiency.
- </p>
- <p style="text-align: justify">
- Named after the ancient instrument that guided explorers across the
- seas, Astrolabe is set to guide the next generation of explorers in
- space. Just as the medieval astrolabe was a tool for navigation and
- discovery, our modern-day Astrolabe will aid scientists and
- engineers in navigating the complex landscape of biological resource
- utilization in space.
- </p>
- <img
- src="https://static.igem.wiki/teams/5155/description-7.webp"
- width="100%"
- />
- <p style="text-align: justify">
- Find the software hosted here: <a
- href="http://astrolabeiitm.duckdns.org/"
- ><u>http://astrolabeiitm.duckdns.org/</u></a
- >
- </p>
- </div>
-
- <div id="section2" class="section">
- <h1>Inspiration</h1>
-
- <p style="text-align: justify">
- During the early stages of ideation for our project, one of the most
- significant challenges we encountered was selecting suitable host
- organisms, particularly those capable of solubilizing silicates and
- utilizing them in extraterrestrial environments.
- </p>
- <p style="text-align: justify">
- This task was far from trivial, as identifying organisms with the
- ability to interact with and metabolize silicate-based minerals
- required an exhaustive review of both microbiology literature and
- resource availability data. After conducting an extensive literature
- review, we eventually identified a handful of organisms that could
- meet our specific use case. However, the process was both
- time-consuming and difficult, underscoring a broader issue within
- the field.
- </p>
- <p style="text-align: justify">
- In our search for a more efficient way to select organisms that
- could maximize resource utilization, we assumed that a software tool
- already existed to recommend chassis organisms based on given
- environmental resources. To our surprise, no such tool was
- available. This gap in existing SynBio tools sparked the idea to
- create a solution ourselves.
- </p>
- <p style="text-align: justify">
- We embarked on developing Astrolabe, a software designed to ease
- this burden by providing a ranked list of organisms tailored to
- specific environmental conditions and resource constraints,
- streamlining chassis selection and significantly reducing the time
- required for manual literature reviews and organism screening.
- </p>
+ </div>
+ <div class="page">
+ <!-- Mars image and page title -->
+
+ <div class="layout">
+ <!-- Table of contents (on the left) -->
+ <div class="toc">
+ <!-- <h2>Table of Contents</h2> -->
+ <ul>
+ <li><a href="#section2">Inspiration</a></li>
+ <li><a href="#section3">Pipeline</a></li>
+ <li><a href="#section4">Web Tool</a></li>
+ </ul>
</div>
-
- <div id="section3" class="section">
- <h1>Pipeline</h1>
-
- <h2
- style="
- text-align: justify;
- padding-left: 40%;
- font-family: glacial_indifference;
- "
- >
- <strong>Overview</strong>
- </h2>
- <p style="text-align: justify">
- Our software pipeline facilitates analyzing and identifying synbio
- hosts based on available resources and growth conditions as user
- inputs. The aim is to gather relevant resources from the user,
- perform pre-processing, search for metabolic data, find resource
- utilization pathways, and ultimately provide a ranked list of
- potential chassis that comprise these pathways.
- </p>
- <p style="text-align: justify">
- <strong>User Input and Preprocessing</strong>: The process starts
- with the user entering the common name of a molecule and selecting
- its corresponding ChEBI ID from a list. We’ve chosen the ChEBI
- standard for input as it provides a cataloging of a wide array of
- chemical formats and offers easy code integration via its API. The
- chosen ID can then be added to the Resources list. Also, the user
- can input query-related information, such as required physiological
- conditions (e.g., temperature, pH)
- </p>
- <p style="text-align: justify">
- <strong>Metabolite Search and Matching</strong>: Executing a search
- for specific reactions of these resource compounds and searching for
- metabolic pathways comprising these reactions in several databases,
- such as <a href="https://biocyc.org/"><u>BioCyc</u></a
- >, <a href="https://www.ebi.ac.uk/uniprot/index"
- ><u>UniProt</u></a
- > (Universal Protein Resource), <a
- href="https://www.genome.jp/kegg/"
- ><u>KEGG</u></a
- > (Kyoto Encyclopedia of Genes and Genomes) and <a
- href="https://www.brenda-enzymes.org/"
- ><u>BRENDA</u></a
- > (Braunschweig Enzyme Database). These metabolic pathways are
- then matched to potential hosts
- </p>
- <p style="text-align: justify">
- <strong>Scoring Function</strong>: The identified chassis are
- evaluated and ranked based on two key criteria: resourcefulness and
- survivability. Resourcefulness measures the utility of the organism
- as a chassis, determined by the number of pathways identified within
- it. Survivability assesses how well the organism's optimal
- growth conditions, such as temperature and oxygen requirements,
- align with the user's input. The physiological information is
- obtained from <a href="https://bacdive.dsmz.de/"
- ><u>BacDive</u></a
- > and <a href="https://mediadive.dsmz.de/"
- ><u>MediaDive</u></a
- >. The closer the match, the higher the ranking for survivability.
- </p>
- <p style="text-align: justify">
- <strong>Output</strong>: Using the scoring function, a ranked list
- of organisms that can be engineered for In Situ Resource Utilization
- (ISRU) is generated. The top organisms are selected based on their
- resource utilization pathways and adaptability to various
- conditions. The associated pathways and potential use cases are
- presented.
- </p>
-
- <img
- src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXflcW-Ppzyj9WYquWsIQS3YUY_j0kqPTONWogMMKZYggD1Odf-EmU2anObKNioP1_hMMdG030-Iu_U8XuSnxpURkrVrnu7DQU1m_ufG6alDj78nEniHA2Ha7J2q1WeBlBU_TfTSEBy60BwWXD_uQp-1VHDo?key=SLRk3Rd_eX-zGU4puFyaTg"
- width="100%"
- />
- <h2 style="padding-top: 10%; font-family: glacial_indifference">
- BioCyc
- </h2>
- <p style="text-align: justify">
- <strong>Overview of BioCyc</strong>
- </p>
- <p style="text-align: justify">
- BioCyc is a collection of Pathway/Genome Databases (PGDBs) for model
- eukaryotes and for thousands of microbes, plus software tools for
- exploring them. It contains curated data from 146,000 publications.
- The reason BioCyc was picked as one of the databases was due to the
- unique pathways and metabolites in its catalog, including some
- inorganics and pathways involved in critical biogeochemical cycles,
- absent in other databases.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong
- >Accessing BioCyc Using Python's Requests Package</strong
- >
- </p>
- <p style="text-align: justify">
- Python’s Requests package allows users to query web pages and
- collect the information on the page. First, we created a requests
- session, which allowed us to log in to BioCyc. This was necessary
- because BioCyc requires the user to log in before being able to
- access the database. After doing this, we queried various payloads
- corresponding to each step of the workflow.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong
- >Mapping ChEBI IDs to Pathways and Organism Filtering</strong
- >
- </p>
- <p style="text-align: justify">
- User-provided ChEBI IDs are converted to BioCyc IDs for
- compatibility with metabolic databases. The software then retrieves
- biochemical reactions involving the specified compounds, identifies
- associated metabolic pathways, and collects organisms documented to
- contain these pathways. To ensure relevance for synthetic biology
- applications, multicellular organisms under the taxonomic groups
- 'Metazoa' and 'Embryophyta' are filtered out,
- leaving only unicellular candidates. The remaining organisms are
- then passed through a scoring function, which ranks them based on
- their suitability as chassis organisms, optimizing for resource
- utilization efficiency and genetic tractability.
- </p>
-
- <img
- src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXd2VLFMNmKNJlEbUuPfk7gWTUsnzQiIDOmjMturxHSdAEII5QBdFdYng32d_Rp6khmf0KNkDBfSaG1Q7WEXQcX6EnoApoK7UD1RxhbMyvPf65lFi8fs_s-usfGZBCqbpRBFAKK_KFmfDiQKQMpTYigdACY?key=SLRk3Rd_eX-zGU4puFyaTg"
- width="100%"
- />
- <h2 style="padding-top: 10%; font-family: glacial_indifference">
- UniProt
- </h2>
- <p style="text-align: justify">
- <strong>Overview of UniProt</strong>
- </p>
- <p style="text-align: justify">
- UniProt is a comprehensive protein sequence and functional
- information database. It provides detailed annotations for proteins,
- including their functions, structures, and interactions. UniProt is
- a crucial resource for researchers in fields such as genomics,
- proteomics, and bioinformatics. It hosts a wealth of curated data
- from various sources, allowing users to explore relationships
- between proteins, their corresponding organisms, and biochemical
- pathways. The database is especially beneficial for studies
- involving synthetic biology and metabolic engineering due to its
- extensive protein functional information.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong
- >Accessing UniProt Using Python's Requests Package</strong
+
+ <!-- Content Sections (on the right) -->
+ <div class="content">
+ <div id="section1" class="section">
+ <p><br /></p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ Chassis selection is a critical early decision in any synthetic
+ biology project, particularly when working in environments beyond
+ Earth. As we push the boundaries of In Situ Resource Utilization
+ (ISRU), the need to choose organisms that can efficiently harness
+ local minerals and resources becomes paramount. Traditional
+ laboratory organisms like <em>E. coli</em> dominate
+ research due to their well-understood biology. However, their
+ suitability for non-terrestrial environments often requires
+ extensive genetic engineering to meet the demands of space-based
+ resource constraints.
+ </p>
+ <p style="text-align: justify">
+ To address this challenge, our team
+ developed <strong>Astrolabe</strong>, a software tool designed
+ to assist researchers in selecting the most appropriate chassis
+ organism based on specific environmental resources and physiological
+ conditions. By leveraging available resources and minimizing the
+ engineering required, Astrolabe provides a ranked list of suitable
+ organisms, offering a streamlined path to achieving desired
+ biological outputs with maximum efficiency.
+ </p>
+ <p style="text-align: justify">
+ Named after the ancient instrument that guided explorers across the
+ seas, Astrolabe is set to guide the next generation of explorers in
+ space. Just as the medieval astrolabe was a tool for navigation and
+ discovery, our modern-day Astrolabe will aid scientists and
+ engineers in navigating the complex landscape of biological resource
+ utilization in space.
+ </p>
+ <img
+ src="https://static.igem.wiki/teams/5155/description-7.webp"
+ width="100%"
+ />
+ <p style="text-align: justify">
+ Find the software hosted here: <a
+ href="http://astrolabeiitm.duckdns.org/"
+ ><u>http://astrolabeiitm.duckdns.org/</u></a
+ >
+ </p>
+ </div>
+
+ <div id="section2" class="section">
+ <h1>Inspiration</h1>
+
+ <p style="text-align: justify">
+ During the early stages of ideation for our project, one of the most
+ significant challenges we encountered was selecting suitable host
+ organisms, particularly those capable of solubilizing silicates and
+ utilizing them in extraterrestrial environments.
+ </p>
+ <p style="text-align: justify">
+ This task was far from trivial, as identifying organisms with the
+ ability to interact with and metabolize silicate-based minerals
+ required an exhaustive review of both microbiology literature and
+ resource availability data. After conducting an extensive literature
+ review, we eventually identified a handful of organisms that could
+ meet our specific use case. However, the process was both
+ time-consuming and difficult, underscoring a broader issue within
+ the field.
+ </p>
+ <p style="text-align: justify">
+ In our search for a more efficient way to select organisms that
+ could maximize resource utilization, we assumed that a software tool
+ already existed to recommend chassis organisms based on given
+ environmental resources. To our surprise, no such tool was
+ available. This gap in existing SynBio tools sparked the idea to
+ create a solution ourselves.
+ </p>
+ <p style="text-align: justify">
+ We embarked on developing Astrolabe, a software designed to ease
+ this burden by providing a ranked list of organisms tailored to
+ specific environmental conditions and resource constraints,
+ streamlining chassis selection and significantly reducing the time
+ required for manual literature reviews and organism screening.
+ </p>
+ </div>
+
+ <div id="section3" class="section">
+ <h1>Pipeline</h1>
+
+ <h2
+ style="
+ text-align: justify;
+ padding-left: 40%;
+ font-family: glacial_indifference;
+ "
>
- </p>
- <p style="text-align: justify">
- The Python Requests package enables users to easily query the
- UniProt REST API to retrieve protein-related information. The
- querying process begins by constructing a request URL that
- incorporates specific filters such as taxonomy and ChEBI
- identifiers. This step ensures that only relevant protein data is
- fetched from UniProt. The code provided utilizes a session with the
- UniProt API to access necessary protein data based on the given
- ChEBI IDs.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Mapping ChEBI IDs to UniProt Data</strong>
- </p>
- <p style="text-align: justify">
- The process of querying UniProt involves the following steps:
- </p>
- <ol style="padding-left: 10%">
- <li>
- <p style="text-align: justify">
- <strong>ChEBI ID Input</strong>: Users input ChEBI IDs as
- strings, which are then processed to extract the relevant part
- for querying.
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- <strong>UniProt Query Construction</strong>: A query URL is
- constructed that targets specific organisms (e.g., bacteria) and
- filters for proteins that have been reviewed and are associated
- with the specified ChEBI ID as a cofactor.
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- <strong>Data Retrieval</strong>: The constructed URL is used to
- send a GET request to the UniProt API. The resource list in
- ChEBI ID format is passed via the request to get the organism
- with the corresponding number of pathways containing the
- resource of interest in it. If the request is successful, the
- response data is returned in JSON format.
- </p>
- </li>
- </ol>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Filtering Organism Data</strong>
- </p>
- <p style="text-align: justify">
- Once the data is retrieved, it undergoes processing to filter out
- duplicates and irrelevant organisms:
- </p>
- <ol style="padding-left: 10%; font-size: 1em">
- <li>
- <p style="text-align: justify">
- <strong>Duplicate Filtering</strong>: The
- filter_duplicates function counts occurrences of each
- organism based on its scientific name, ensuring that each unique
- organism is represented only once in the results.
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- <strong>Relevance Filtering</strong>: The code further filters
- results based on the annotation score (indicating the likely
- accuracy of the data), retaining only those entries with a
- score greater than or equal to 3, and excluding any organisms
- classified as viruses.
- </p>
- </li>
- </ol>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Summary of Results</strong>
- </p>
-
- <p style="text-align: justify">
- The final output is a counter of unique organisms that meet the
- specified criteria, along with their respective counts. The results
- provide insights into the distribution of proteins associated with
- the input ChEBI IDs, enabling researchers to identify the most
- relevant organisms for further study.
- </p>
- <h2 style="padding-top: 10%; font-family: glacial_indifference">
- KEGG and BRENDA
- </h2>
- <p style="text-align: justify">
- <strong>Introduction</strong>
- </p>
- <p style="text-align: justify">
- KEGG (Kyoto Encyclopedia of Genes and Genomes) is a collection of
- databases dealing with genomes, biological pathways, and chemical
- substances. BRENDA (BRaunschweig ENzyme DAtabase) is the
- world's most comprehensive online database for functional,
- biochemical and molecular biological data on enzymes, metabolites
- and metabolic pathways. In the current architecture of the software,
- KEGG serves as the backbone for retrieving organismal data while
- BRENDA supplements enzyme data for deeper insights into metabolic
- pathways. KEGG and BRENDA are queried to retrieve enzymes and genes
- that act on the compound. Then, KEGG is queried to collect enzyme
- and gene information to identify organisms.
- </p>
-
- <p style="text-align: justify">
- KEGG structures its database as a collection of interconnected
- sub-databases (KEGG Compounds, KEGG Enzymes, KEGG Genes, KEGG
- Organisms, and KEGG BRITE). Navigating through these sub-databases
- was unintuitive due to the lack of cohesive documentation explaining
- how the sub-databases cross-reference each other. After some
- iterations, we settled on a compound → enzyme → gene
- → organism approach, which proved more efficient. Following
- this, we have implemented a filtering step to include only those
- taxa that are relevant to synthetic biology, including Ascomycetes,
- Basidiomycetes, Diatoms, Euglenozoa, Green algae, Red algae, along
- with bacteria.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Accessing and Sorting Data</strong>
- </p>
- <p style="text-align: justify">
- The KEGG subroutine required optimisations to overcome 2 major
- bottlenecks, namely query limits and the complexity of mapping
- enzymes to genes and genes to organisms:
- </p>
- <ul style="padding-left: 10%; font-size: 1em">
- <li>
- <p style="text-align: justify">
- <strong>Batching Queries</strong>: By sending requests in
- batches instead of individually, we reduced redundant queries
- and lowered overall processing time.
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- <strong>Multiprocessing</strong>: Multiprocessing is a computer
- paradigm in which two or more processors in a computer are
- simultaneously executing different portions of the same program.
- This technique allows for better utilization of available
- hardware and improves speed. We implemented Python’s
- multiprocessing library to run multiple independent queries in
- parallel. This was particularly effective since each query was
- isolated and could be processed independently. This optimization
- alone shaved around 30 seconds off the initial 4 minutes.
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- <strong>Local Searching</strong>: We discovered that KEGG uses a
- prefix in gene codes corresponding to organism codes. By
- downloading a local list of organism codes from KEGG, We were
- able to bypass querying the server for each gene, dramatically
- reducing the time spent on gene-to-organism mapping by
- eliminating network delays.
- </p>
- </li>
- </ul>
- <p style="text-align: justify">
- As not all compounds were documented in KEGG, we decided to
- complement the KEGG results by running the same compound queries
- through BRENDA, to see if additional enzymes could be identified.
- BRENDA uses the SOAP API architecture. SOAP is fundamentally
- different from the REST architecture used in KEGG, requiring an
- intermediary service provider to process requests, which added
- complexity. Due to improperly configured API endpoints and function
- prototypes, we were unable to sort the API calls in time. Thus, we
- resorted to using BRENDA’s CSV download functionality. We have
- constructed dynamic URLs to automate the download process of the EC
- enzyme information.<strong> </strong>Integration of BRENDA let
- us generate more comprehensive enzyme lists.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Graphical Overview</strong>
- </p>
-
- <img
- src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXfcyHpNBCaM5vwiZrQkUR7cRnWPaB7Gpc_so9kPBPbtgk7y6WV6221WYqhHy38e6ZQlpijyLoHLIiIAeg1lIOXirkdlqlpdNgklAL_9m72WI0BQJzd9g1OjBnaGw4su4Ow7R-s4n15rDoAzhmlJhWop0mor?key=SLRk3Rd_eX-zGU4puFyaTg"
- width="100%"
- />
- <h2 style="padding-top: 10%; font-family: glacial_indifference">
- Scoring Function
- </h2>
- <h4 style="text-align: justify">
- <strong>Parameters</strong>
- </h4>
- <p style="text-align: justify">
- The scoring function takes the output from the different databases
- executing Metabolic Search, integrates and sorts the output to give
- the user the benefit of the different information sources.
- </p>
- <p style="text-align: justify">
- The scoring function plays a crucial role in ensuring the user gets
- maximum utility from our software. We have identified the following
- factors to characterize and quantify the suitability of a potential
- chassis organism for the expressed use case.
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Resourcefulness Score</strong>
- </p>
- <p style="text-align: justify">
- This parameter quantifies the competence of the organism at
- utilizing the most number of metabolites of interest. The score is
- the sum total number of pathways/reactions/enzymes in an organism
- that contain the resources(s) of interest from all the 4 databases
- divided by the maximum number of pathways identified in a single
- organism across the databases amongst all the organisms. This
- normalizes the score and makes it simpler to scale using the
- weights. This information is supplied from the Uniprot, BioCyc, KEGG
- and BRENDA databases.
- </p>
- <p style="text-align: justify">
- $$ \text{Resourcefulness score} = \frac{\sum_{database}^{n=4}
- \text{no. of pathways/reactions/enzymes}}{\text{maximum no. of
- pathways}}$$
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Survivability Score</strong>
- </p>
- <p style="text-align: justify">
- This parameter quantifies the suitability of the organism to grow in
- the environment desired by the user. Although microorganisms on
- Earth cannot bear extraterrestrial conditions, considerations like
- this can help increase the feasibility of the proposed project by,
- for example, reducing energy expenditure in maintaining the required
- temperatures in a bioreactor. We have considered the overlap in
- Temperature and pH ranges between the user input and the
- organism’s preferred ranges (normalized by dividing with
- respective input ranges), supplied from the BacDive and MediaDive
- databases.
- </p>
- <p style="text-align: justify">
- $$ \text{Temperature score} =\frac{\text{length(input temperature
- range} \cap \text{organism temperature range)}}{\text{length(input
- temperature range)}}$$
- </p>
- <p style="text-align: justify">
- $$ \text{pH score} = \frac{\text{length(input pH range} \cap
- \text{organism pH range)}}{\text{length(input pH range)}}$$
- </p>
- <p style="text-align: justify">
- $$ \text{Survivability score} = \text{Temperature score} + \text{pH
- score}$$
- </p>
- <p><br /></p>
- <p style="text-align: justify">
- <strong>Combined Formula</strong>
- </p>
- <p style="text-align: justify">
- The Final score used to return an ordered list of organisms is a
- linear combination of the Resourcefulness and Survivability scores.
- Currently, the weights for both the scores have been set equal to
- ‘1’.
- </p>
- <p style="text-align: justify">
- $$ \text{Final score} = \text{w1} \cdot \text{Resourcefulness score}
- + \text{w2} \cdot \text{Survivability score}$$ $$ \text{where w1,
- w2} = 1 $$
- </p>
- <p style="text-align: justify">
- This can further be customized and fine-tuned as per the end
- user’s requirements, as this provides a trade-off between the
- constraints supplied by the user.
- </p>
- <p><br /></p>
- <h4 style="text-align: justify">
- <strong>BacDive and MediaDive</strong>
- </h4>
- <p style="text-align: justify">
- BacDive is a database that provides information about bacterial and
- archaeal diversity. BacDive’s well documented REST API still
- posed issues of inconsistent data formats that required extensive
- error handling to process the data. We get temperature data for an
- organism from BacDive from which the temperature ranges are
- calculated.
- </p>
- <p style="text-align: justify">
- MediaDive is a culture media database that was often referenced in
- BacDive entries. We have utilized this database to infer pH
- physiological data for a given organism from which the pH ranges are
- calculated.
- </p>
- <p><br /></p>
- <h4 style="text-align: justify">
- <strong>Graphical Overview </strong>
- </h4>
- <p style="text-align: justify">
+ <strong>Overview</strong>
+ </h2>
+ <p style="text-align: justify">
+ Our software pipeline facilitates analyzing and identifying synbio
+ hosts based on available resources and growth conditions as user
+ inputs. The aim is to gather relevant resources from the user,
+ perform pre-processing, search for metabolic data, find resource
+ utilization pathways, and ultimately provide a ranked list of
+ potential chassis that comprise these pathways.
+ </p>
+ <p style="text-align: justify">
+ <strong>User Input and Preprocessing</strong>: The process starts
+ with the user entering the common name of a molecule and selecting
+ its corresponding ChEBI ID from a list. We’ve chosen the ChEBI
+ standard for input as it provides a cataloging of a wide array of
+ chemical formats and offers easy code integration via its API. The
+ chosen ID can then be added to the Resources list. Also, the user
+ can input query-related information, such as required physiological
+ conditions (e.g., temperature, pH)
+ </p>
+ <p style="text-align: justify">
+ <strong>Metabolite Search and Matching</strong>: Executing a search
+ for specific reactions of these resource compounds and searching for
+ metabolic pathways comprising these reactions in several databases,
+ such as <a href="https://biocyc.org/"><u>BioCyc</u></a
+ >, <a href="https://www.ebi.ac.uk/uniprot/index"
+ ><u>UniProt</u></a
+ > (Universal Protein Resource), <a
+ href="https://www.genome.jp/kegg/"
+ ><u>KEGG</u></a
+ > (Kyoto Encyclopedia of Genes and Genomes) and <a
+ href="https://www.brenda-enzymes.org/"
+ ><u>BRENDA</u></a
+ > (Braunschweig Enzyme Database). These metabolic pathways are
+ then matched to potential hosts
+ </p>
+ <p style="text-align: justify">
+ <strong>Scoring Function</strong>: The identified chassis are
+ evaluated and ranked based on two key criteria: resourcefulness and
+ survivability. Resourcefulness measures the utility of the organism
+ as a chassis, determined by the number of pathways identified within
+ it. Survivability assesses how well the organism's optimal
+ growth conditions, such as temperature and oxygen requirements,
+ align with the user's input. The physiological information is
+ obtained from <a href="https://bacdive.dsmz.de/"
+ ><u>BacDive</u></a
+ > and <a href="https://mediadive.dsmz.de/"
+ ><u>MediaDive</u></a
+ >. The closer the match, the higher the ranking for survivability.
+ </p>
+ <p style="text-align: justify">
+ <strong>Output</strong>: Using the scoring function, a ranked list
+ of organisms that can be engineered for In Situ Resource Utilization
+ (ISRU) is generated. The top organisms are selected based on their
+ resource utilization pathways and adaptability to various
+ conditions. The associated pathways and potential use cases are
+ presented.
+ </p>
+
<img
- src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXcA8sILSwFJzQsfJH019JP1xfJrrngZsdcSJHC7kF-Fsh5TnJb87K4zKH6dqKu_HcVRVra8nGK99-6wSG7eQqkYjvzG1nMKXam5BVauO5xwyKapywH7BSSROp3F1GFSwFghhUNAgqUjiQwD8qzOXTkoW_G2?key=SLRk3Rd_eX-zGU4puFyaTg"
+ src="https://static.igem.wiki/teams/5155/software/10.webp"
width="100%"
/>
- </p>
- </div>
- <div id="section4" class="section">
- <h1>Web Tool</h1>
- <p style="text-align: justify">
- We have created a web tool that provides a simple and accessible way
- for users to run our software without needing to download or
- understand complex code. Releasing code is useful for developers,
- but for non-experts who are not used to setting up code and handling
- issues, it can be a challenge. By hosting the tool on a Django
- server with a user-friendly interface, we make it easy for anyone to
- use without technical setup. This ensures a wider audience can
- benefit from the tool, focusing on their research rather than
- technical details, and allows easy access from any device, improving
- usability and utility.
- </p>
- <p style="text-align: justify">
- We can confirm that this website-hosted form of our software is
- compatible with the following non-exhaustive list of browsers:
- </p>
- <p><br /></p>
- <ul style="padding-left: 20%; font-size: 1em">
- <li>
- <p style="text-align: justify">
- Google Chrome
-
- 129.0.6668.70
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- Safari on macOS (Laptops and Desktops)
- 18.0
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- Safari on iOS (iPhone, iPad and iPod)
- 18.0
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- Mozilla Firefox
-
-
-
- 130.0.1
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- Opera on Desktop
-
-
- 114.0.5282.21
- </p>
- </li>
- <li>
- <p style="text-align: justify">
- Opera on Android
-
-
- 76.2.4027.73374
- </p>
- </li>
- </ul>
- <p><br /></p>
- <p style="text-align: justify">
- For other or non-supported browsers, please refer to our command line interface (CLI) version
- which can be run locally on the user’s device.
- </p>
- </div>
- <div id="section5" class="section">
- <h1>Results</h1>
- <p
- >
- We present our software in two forms. One, as a <strong> standalone software</strong> working on the command line interface (CLI), to enable easy
- modifications and integrations with new code. Two, as part of
- a <strong> web application </strong> created using the Django framework that can be hosted
- locally to enable user-friendly access to the code, especially
- useful for an intended target audience without software or
- programming expertise. We have deployed this code at <a
- href="http://astrolabeiitm.duckdns.org/"
- style="text-decoration: none"
- >http://astrolabeiitm.duckdns.org/</a
- > to enable quick and easy access via the internet.
- </p>
-
- <p
-
- >
- <strong>Standalone</strong>
- </p>
- <p
-
- >
- The standalone software can be found <a
- href="https://gitlab.igem.org/2024/software-tools/iit-madras"
- style="text-decoration: none"
- >here</a
- >. This can be used by running main.py after installing the
- necessary dependencies listed in requirements.txt.
- </p>
- <p>
- The input interface for the standalone appears like this on the
- command line:
- </p>
- <img
- src="https://static.igem.wiki/teams/5155/software/1.png"
- width="100%"
- />
- </p>
- <p
+ <h2 style="padding-top: 10%; font-family: glacial_indifference">
+ BioCyc
+ </h2>
+ <p style="text-align: justify">
+ <strong>Overview of BioCyc</strong>
+ </p>
+ <p style="text-align: justify">
+ BioCyc is a collection of Pathway/Genome Databases (PGDBs) for model
+ eukaryotes and for thousands of microbes, plus software tools for
+ exploring them. It contains curated data from 146,000 publications.
+ The reason BioCyc was picked as one of the databases was due to the
+ unique pathways and metabolites in its catalog, including some
+ inorganics and pathways involved in critical biogeochemical cycles,
+ absent in other databases.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong
+ >Accessing BioCyc Using Python's Requests Package</strong
+ >
+ </p>
+ <p style="text-align: justify">
+ Python’s Requests package allows users to query web pages and
+ collect the information on the page. First, we created a requests
+ session, which allowed us to log in to BioCyc. This was necessary
+ because BioCyc requires the user to log in before being able to
+ access the database. After doing this, we queried various payloads
+ corresponding to each step of the workflow.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong
+ >Mapping ChEBI IDs to Pathways and Organism Filtering</strong
+ >
+ </p>
+ <p style="text-align: justify">
+ User-provided ChEBI IDs are converted to BioCyc IDs for
+ compatibility with metabolic databases. The software then retrieves
+ biochemical reactions involving the specified compounds, identifies
+ associated metabolic pathways, and collects organisms documented to
+ contain these pathways. To ensure relevance for synthetic biology
+ applications, multicellular organisms under the taxonomic groups
+ 'Metazoa' and 'Embryophyta' are filtered out,
+ leaving only unicellular candidates. The remaining organisms are
+ then passed through a scoring function, which ranks them based on
+ their suitability as chassis organisms, optimizing for resource
+ utilization efficiency and genetic tractability.
+ </p>
+
+ <img
+ src="https://static.igem.wiki/teams/5155/software/7.webp"
+ width="100%"
+ />
+ <h2 style="padding-top: 10%; font-family: glacial_indifference">
+ UniProt
+ </h2>
+ <p style="text-align: justify">
+ <strong>Overview of UniProt</strong>
+ </p>
+ <p style="text-align: justify">
+ UniProt is a comprehensive protein sequence and functional
+ information database. It provides detailed annotations for proteins,
+ including their functions, structures, and interactions. UniProt is
+ a crucial resource for researchers in fields such as genomics,
+ proteomics, and bioinformatics. It hosts a wealth of curated data
+ from various sources, allowing users to explore relationships
+ between proteins, their corresponding organisms, and biochemical
+ pathways. The database is especially beneficial for studies
+ involving synthetic biology and metabolic engineering due to its
+ extensive protein functional information.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong
+ >Accessing UniProt Using Python's Requests Package</strong
+ >
+ </p>
+ <p style="text-align: justify">
+ The Python Requests package enables users to easily query the
+ UniProt REST API to retrieve protein-related information. The
+ querying process begins by constructing a request URL that
+ incorporates specific filters such as taxonomy and ChEBI
+ identifiers. This step ensures that only relevant protein data is
+ fetched from UniProt. The code provided utilizes a session with the
+ UniProt API to access necessary protein data based on the given
+ ChEBI IDs.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Mapping ChEBI IDs to UniProt Data</strong>
+ </p>
+ <p style="text-align: justify">
+ The process of querying UniProt involves the following steps:
+ </p>
+ <ol style="padding-left: 10%">
+ <li>
+ <p style="text-align: justify">
+ <strong>ChEBI ID Input</strong>: Users input ChEBI IDs as
+ strings, which are then processed to extract the relevant part
+ for querying.
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ <strong>UniProt Query Construction</strong>: A query URL is
+ constructed that targets specific organisms (e.g., bacteria) and
+ filters for proteins that have been reviewed and are associated
+ with the specified ChEBI ID as a cofactor.
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ <strong>Data Retrieval</strong>: The constructed URL is used to
+ send a GET request to the UniProt API. The resource list in
+ ChEBI ID format is passed via the request to get the organism
+ with the corresponding number of pathways containing the
+ resource of interest in it. If the request is successful, the
+ response data is returned in JSON format.
+ </p>
+ </li>
+ </ol>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Filtering Organism Data</strong>
+ </p>
+ <p style="text-align: justify">
+ Once the data is retrieved, it undergoes processing to filter out
+ duplicates and irrelevant organisms:
+ </p>
+ <ol style="padding-left: 10%; font-size: 1em">
+ <li>
+ <p style="text-align: justify">
+ <strong>Duplicate Filtering</strong>: The
+ filter_duplicates function counts occurrences of each
+ organism based on its scientific name, ensuring that each unique
+ organism is represented only once in the results.
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ <strong>Relevance Filtering</strong>: The code further filters
+ results based on the annotation score (indicating the likely
+ accuracy of the data), retaining only those entries with a
+ score greater than or equal to 3, and excluding any organisms
+ classified as viruses.
+ </p>
+ </li>
+ </ol>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Summary of Results</strong>
+ </p>
+
+ <p style="text-align: justify">
+ The final output is a counter of unique organisms that meet the
+ specified criteria, along with their respective counts. The results
+ provide insights into the distribution of proteins associated with
+ the input ChEBI IDs, enabling researchers to identify the most
+ relevant organisms for further study.
+ </p>
+ <h2 style="padding-top: 10%; font-family: glacial_indifference">
+ KEGG and BRENDA
+ </h2>
+ <p style="text-align: justify">
+ <strong>Introduction</strong>
+ </p>
+ <p style="text-align: justify">
+ KEGG (Kyoto Encyclopedia of Genes and Genomes) is a collection of
+ databases dealing with genomes, biological pathways, and chemical
+ substances. BRENDA (BRaunschweig ENzyme DAtabase) is the
+ world's most comprehensive online database for functional,
+ biochemical and molecular biological data on enzymes, metabolites
+ and metabolic pathways. In the current architecture of the software,
+ KEGG serves as the backbone for retrieving organismal data while
+ BRENDA supplements enzyme data for deeper insights into metabolic
+ pathways. KEGG and BRENDA are queried to retrieve enzymes and genes
+ that act on the compound. Then, KEGG is queried to collect enzyme
+ and gene information to identify organisms.
+ </p>
+
+ <p style="text-align: justify">
+ KEGG structures its database as a collection of interconnected
+ sub-databases (KEGG Compounds, KEGG Enzymes, KEGG Genes, KEGG
+ Organisms, and KEGG BRITE). Navigating through these sub-databases
+ was unintuitive due to the lack of cohesive documentation explaining
+ how the sub-databases cross-reference each other. After some
+ iterations, we settled on a compound → enzyme → gene
+ → organism approach, which proved more efficient. Following
+ this, we have implemented a filtering step to include only those
+ taxa that are relevant to synthetic biology, including Ascomycetes,
+ Basidiomycetes, Diatoms, Euglenozoa, Green algae, Red algae, along
+ with bacteria.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Accessing and Sorting Data</strong>
+ </p>
+ <p style="text-align: justify">
+ The KEGG subroutine required optimisations to overcome 2 major
+ bottlenecks, namely query limits and the complexity of mapping
+ enzymes to genes and genes to organisms:
+ </p>
+ <ul style="padding-left: 10%; font-size: 1em">
+ <li>
+ <p style="text-align: justify">
+ <strong>Batching Queries</strong>: By sending requests in
+ batches instead of individually, we reduced redundant queries
+ and lowered overall processing time.
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ <strong>Multiprocessing</strong>: Multiprocessing is a computer
+ paradigm in which two or more processors in a computer are
+ simultaneously executing different portions of the same program.
+ This technique allows for better utilization of available
+ hardware and improves speed. We implemented Python’s
+ multiprocessing library to run multiple independent queries in
+ parallel. This was particularly effective since each query was
+ isolated and could be processed independently. This optimization
+ alone shaved around 30 seconds off the initial 4 minutes.
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ <strong>Local Searching</strong>: We discovered that KEGG uses a
+ prefix in gene codes corresponding to organism codes. By
+ downloading a local list of organism codes from KEGG, We were
+ able to bypass querying the server for each gene, dramatically
+ reducing the time spent on gene-to-organism mapping by
+ eliminating network delays.
+ </p>
+ </li>
+ </ul>
+ <p style="text-align: justify">
+ As not all compounds were documented in KEGG, we decided to
+ complement the KEGG results by running the same compound queries
+ through BRENDA, to see if additional enzymes could be identified.
+ BRENDA uses the SOAP API architecture. SOAP is fundamentally
+ different from the REST architecture used in KEGG, requiring an
+ intermediary service provider to process requests, which added
+ complexity. Due to improperly configured API endpoints and function
+ prototypes, we were unable to sort the API calls in time. Thus, we
+ resorted to using BRENDA’s CSV download functionality. We have
+ constructed dynamic URLs to automate the download process of the EC
+ enzyme information.<strong> </strong>Integration of BRENDA let
+ us generate more comprehensive enzyme lists.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Graphical Overview</strong>
+ </p>
+
+ <img
+ src="https://static.igem.wiki/teams/5155/software/8.webp"
+ width="100%"
+ />
+ <h2 style="padding-top: 10%; font-family: glacial_indifference">
+ Scoring Function
+ </h2>
+ <h4 style="text-align: justify">
+ <strong>Parameters</strong>
+ </h4>
+ <p style="text-align: justify">
+ The scoring function takes the output from the different databases
+ executing Metabolic Search, integrates and sorts the output to give
+ the user the benefit of the different information sources.
+ </p>
+ <p style="text-align: justify">
+ The scoring function plays a crucial role in ensuring the user gets
+ maximum utility from our software. We have identified the following
+ factors to characterize and quantify the suitability of a potential
+ chassis organism for the expressed use case.
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Resourcefulness Score</strong>
+ </p>
+ <p style="text-align: justify">
+ This parameter quantifies the competence of the organism at
+ utilizing the most number of metabolites of interest. The score is
+ the sum total number of pathways/reactions/enzymes in an organism
+ that contain the resources(s) of interest from all the 4 databases
+ divided by the maximum number of pathways identified in a single
+ organism across the databases amongst all the organisms. This
+ normalizes the score and makes it simpler to scale using the
+ weights. This information is supplied from the Uniprot, BioCyc, KEGG
+ and BRENDA databases.
+ </p>
+ <p style="text-align: justify">
+ $$ \text{Resourcefulness score} = \frac{\sum_{database}^{n=4}
+ \text{no. of pathways/reactions/enzymes}}{\text{maximum no. of
+ pathways}}$$
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Survivability Score</strong>
+ </p>
+ <p style="text-align: justify">
+ This parameter quantifies the suitability of the organism to grow in
+ the environment desired by the user. Although microorganisms on
+ Earth cannot bear extraterrestrial conditions, considerations like
+ this can help increase the feasibility of the proposed project by,
+ for example, reducing energy expenditure in maintaining the required
+ temperatures in a bioreactor. We have considered the overlap in
+ Temperature and pH ranges between the user input and the
+ organism’s preferred ranges (normalized by dividing with
+ respective input ranges), supplied from the BacDive and MediaDive
+ databases.
+ </p>
+ <p style="text-align: justify">
+ $$ \text{Temperature score} =\frac{\text{length(input temperature
+ range} \cap \text{organism temperature range)}}{\text{length(input
+ temperature range)}}$$
+ </p>
+ <p style="text-align: justify">
+ $$ \text{pH score} = \frac{\text{length(input pH range} \cap
+ \text{organism pH range)}}{\text{length(input pH range)}}$$
+ </p>
+ <p style="text-align: justify">
+ $$ \text{Survivability score} = \text{Temperature score} + \text{pH
+ score}$$
+ </p>
+ <p><br /></p>
+ <p style="text-align: justify">
+ <strong>Combined Formula</strong>
+ </p>
+ <p style="text-align: justify">
+ The Final score used to return an ordered list of organisms is a
+ linear combination of the Resourcefulness and Survivability scores.
+ Currently, the weights for both the scores have been set equal to
+ ‘1’.
+ </p>
+ <p style="text-align: justify">
+ $$ \text{Final score} = \text{w1} \cdot \text{Resourcefulness score}
+ + \text{w2} \cdot \text{Survivability score}$$ $$ \text{where w1,
+ w2} = 1 $$
+ </p>
+ <p style="text-align: justify">
+ This can further be customized and fine-tuned as per the end
+ user’s requirements, as this provides a trade-off between the
+ constraints supplied by the user.
+ </p>
+ <p><br /></p>
+ <h4 style="text-align: justify">
+ <strong>BacDive and MediaDive</strong>
+ </h4>
+ <p style="text-align: justify">
+ BacDive is a database that provides information about bacterial and
+ archaeal diversity. BacDive’s well documented REST API still
+ posed issues of inconsistent data formats that required extensive
+ error handling to process the data. We get temperature data for an
+ organism from BacDive from which the temperature ranges are
+ calculated.
+ </p>
+ <p style="text-align: justify">
+ MediaDive is a culture media database that was often referenced in
+ BacDive entries. We have utilized this database to infer pH
+ physiological data for a given organism from which the pH ranges are
+ calculated.
+ </p>
+ <p><br /></p>
+ <h4 style="text-align: justify">
+ <strong>Graphical Overview </strong>
+ </h4>
+ <p style="text-align: justify">
+ <img
+ src="https://static.igem.wiki/teams/5155/software/9.webp"
+ width="100%"
+ />
+ </p>
+ </div>
+ <div id="section4" class="section">
+ <h1>Web Tool</h1>
+ <p style="text-align: justify">
+ We have created a web tool that provides a simple and accessible way
+ for users to run our software without needing to download or
+ understand complex code. Releasing code is useful for developers,
+ but for non-experts who are not used to setting up code and handling
+ issues, it can be a challenge. By hosting the tool on a Django
+ server with a user-friendly interface, we make it easy for anyone to
+ use without technical setup. This ensures a wider audience can
+ benefit from the tool, focusing on their research rather than
+ technical details, and allows easy access from any device, improving
+ usability and utility.
+ </p>
+ <p style="text-align: justify">
+ We can confirm that this website-hosted form of our software is
+ compatible with the following non-exhaustive list of browsers:
+ </p>
+ <p><br /></p>
+ <ul style="padding-left: 20%; font-size: 1em">
+ <li>
+ <p style="text-align: justify">
+ Google Chrome
+
+ 129.0.6668.70
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ Safari on macOS (Laptops and Desktops)
+ 18.0
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ Safari on iOS (iPhone, iPad and iPod)
+ 18.0
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ Mozilla Firefox
+
+
+
+ 130.0.1
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ Opera on Desktop
+
+
+ 114.0.5282.21
+ </p>
+ </li>
+ <li>
+ <p style="text-align: justify">
+ Opera on Android
+
+
+ 76.2.4027.73374
+ </p>
+ </li>
+ </ul>
+ <p><br /></p>
+ <p style="text-align: justify">
+ For other or non-supported browsers, please refer to our CLI version
+ which can be run locally on the user’s device.
+ </p>
+ </div>
+ <div id="section5" class="section">
+ <h1>Results</h1>
+ <p
+ >
+ We present our software in two forms. One, as a standalone software working on the command line interface (CLI), to enable easy
+ modifications and integrations with new code. Two, as part of
+ a web application created using the Django framework that can be hosted
+ locally to enable user-friendly access to the code, especially
+ useful for an intended target audience without software or
+ programming expertise. We have deployed this code at <a
+ href="http://astrolabeiitm.duckdns.org/"
+ style="text-decoration: none"
+ >http://astrolabeiitm.duckdns.org/</a
+ > to enable quick and easy access via the internet.
+ </p>
- >
- The output is a Python dictionary whose keys are the organism
- names and values are tuples. Each tuple consists of 4 scores in
- this order: Final_score, Resourcefulness_score, Temperature_score,
- pH_score. The organisms are arranged in descending order of their
- Final_score value.
- </p>
-
+ <p
+
+ >
+ Standalone
+ </p>
+ <p
+
+ >
+ The standalone software can be found <a
+ href="https://gitlab.igem.org/2024/software-tools/iit-madras"
+ style="text-decoration: none"
+ >here</a
+ >. This can be used by running main.py after installing the
+ necessary dependencies listed in requirements.txt.
+ </p>
+ <p>
+ The input interface for the standalone appears like this on the
+ command line:
+ </p>
<img
- src="https://static.igem.wiki/teams/5155/software/2.png"
- width="100%"
- />
- </p>
-
- <p
+ src="https://static.igem.wiki/teams/5155/software/1.png"
+ width="100%"
+ />
+ </p>
+ <p
+
+ >
+ The output is a Python dictionary whose keys are the organism
+ names and values are tuples. Each tuple consists of 4 scores in
+ this order: Final_score, Resourcefulness_score, Temperature_score,
+ pH_score. The organisms are arranged in descending order of their
+ Final_score value.
+ </p>
- >
- Online Web Application
- </p>
- <p>
- We have deployed our software at <a
- href="http://astrolabeiitm.duckdns.org/"
- style="text-decoration: none"
- >http://astrolabeiitm.duckdns.org/</a
- > to enable quick and easy access via the internet.
- </p>
- <p
+ <img
+ src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdnCC9anxU6eIkGa0qUgOmkpwNyd28Z8RxWAb0HZLPP70SvLdy2AsClSPZOzz5OtIEnE6clikSd8jGFLPA0RKIhqH6Mn5krrSDprOC5Bgrn6ni_NazgvSqaw_51PJCHz52UKEzyIuKwh9cJURg_p3qriKw?key=SLRk3Rd_eX-zGU4puFyaTg"
+ width="100%"
+ />
+ </p>
- >
- Local Host Web Application
- </p>
- <p
+ <p
+
+ >
+ Online Web Application
+ </p>
+ <p>
+ We have deployed our software at <a
+ href="http://astrolabeiitm.duckdns.org/"
+ style="text-decoration: none"
+ >http://astrolabeiitm.duckdns.org/</a
+ > to enable quick and easy access via the internet.
+ </p>
+ <p
+
+ >
+ Local Host Web Application
+ </p>
+ <p
+
+ >
+ To use the Django web application locally, follow these
+ steps:
+ </p>
- >
- To use the Django web application locally, follow these
- steps:
- </p>
-
- <object class="pdf" data="https://static.igem.wiki/teams/5155/software/astrolabe-instructions.pdf" width="100%" height="500px"></object>
- </p>
+ <object class="pdf" data="https://static.igem.wiki/teams/5155/software/astrolabe-instructions.pdf" width="100%" height="500px"></object>
+ </p>
+ </div>
</div>
</div>
</div>
- </div>
-
- <script>
- document.addEventListener("DOMContentLoaded", function () {
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- </script>
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-{% endblock %}
+ </script>
+ </div>
+ {% endblock %}
+
\ No newline at end of file
--
GitLab