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--- a/wiki/pages/model.html
+++ b/wiki/pages/model.html
@@ -88,6 +88,15 @@
overflow-x: auto;
/* å…许水平滚动 */
}
+ .col-lg-12 a {
+ color: #fa8072;
+ text-decoration: none;
+ transition: color 0.3s ease;
+ }
+ .col-lg-12 a:hover {
+ color: #ff6347;
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</head>
@@ -138,7 +147,7 @@
</div>
<div class="image-container">
<img src="https://static.igem.wiki/teams/5187/wiki-model-fig/introduction.png" alt="introduction_figure"
- class="shadowed-image">
+ class="shadowed-image" style="width: 80%; max-width: 800px;">
</div>
<p style="text-align: center; font-size: 0.9em; margin-top: 10px;">fig 1 General Description of Model</p>
</div>
@@ -148,7 +157,7 @@
<h2 id="topic1">
<h2>Compartment Model for Muscone Inhalation</h2>
<hr>
- <h3>Model Description</h3>
+ <h3>1.Model Description</h3>
<p>The main focus of our project is the use of muscone as a signaling molecule to activate engineered
yeast in the gut for therapeutic purposes. Therefore, it is crucial to provide a quantitative
description and computational support for the diffusion of muscone in the body. This model describes
@@ -188,7 +197,7 @@
<li><strong>Compartment 4</strong> (Target Intestine, \(I\)): \(Q_I(t)\) represents the amount of
muscone in the intestine(\(\text{mg}\)).</li>
<p></p>
- <h3>Initial Settings and Assumptions</h3>
+ <h3>2. Initial Settings and Assumptions</h3>
<p>At \(t=0\), the amount of muscone in all compartments is \(0\).</p>
<p>Assuming that the total amount of inhaled muscone is \(Q_{\text{inhale}}\) (\(\text{mg}\)), which is
assumed to be \(100\text{mg}\). Only \(0.5\%\) of muscone enters the systemic circulation through
@@ -196,7 +205,7 @@
synthesize lactate, we only consider the metabolism and excretion of muscone in the systemic
circulation. We only focus on the short-term process of muscone appearing in the intestine from
scratch, and the subsequent process of reaching a certain concentration can be ignored.</p>
- <h3>Model Equations</h3>
+ <h3>3. Model Equations</h3>
<h4>Inhalation Equation for Muscone</h4>
@@ -323,7 +332,7 @@
\text{min}^{-1} \)
</p>
- <h3>System of Equations:</h3>
+ <h3>4. System of Equations:</h3>
<p>In summary, we can write a system of ordinary differential equations and import it into MATLAB for
simulation:</p>
@@ -469,7 +478,7 @@ end</div>
prepare the three-dimensional molecular model of the research object. In this study, our goal is
to simulate the interaction between muscone and the olfactory receptor Or5an6 (MOR215-1).</li>
</ul>
- <h4>1. Constructing the Three-Dimensional Structures of Muscone and the Receptor:</h4>
+ <h4>Constructing the Three-Dimensional Structures of Muscone and the Receptor:</h4>
<ul>
<li><strong>Muscone</strong>:</li>
@@ -578,7 +587,7 @@ ALQRCKNKCFSQCHC</div>
</div>
<p style="text-align: center; font-size: 0.9em; margin-top: 10px;">fig 6 Protein structure of MOR215-1
</p>
- <h4>2. System Preparation:</h4>
+ <h4>System Preparation:</h4>
<ul>
<li>To study how muscone binds to the receptor, molecular docking tools such as AutoDock and Vina
are used to determine potential binding conformations and obtain docking data:
@@ -676,7 +685,7 @@ out=muscure.pdbqt</div>
</li>
</ul>
<h3>2. Force field parameterization</h3>
- <h4>1. Select Force Field:</h4>
+ <h4>Select Force Field:</h4>
<ul>
<li>To perform molecular dynamics simulations, it is necessary to choose an appropriate molecular
force field to describe the interactions between molecules within the system. CHARMM36 was
@@ -684,7 +693,7 @@ out=muscure.pdbqt</div>
due to the absence of direct parameters for musk ketone in existing force fields, custom
parameters need to be generated to supplement it.</li>
</ul>
- <h4>2. Generate Force Field Parameters:</h4>
+ <h4>Generate Force Field Parameters:</h4>
<ul>
<li>Use Avogadro to convert to <code>.mol2</code> format, adjust file information, and then use the
software <a href="https://cgenff.com/">CGenFF</a> to generate its CHARMM36 force field parameter
@@ -694,7 +703,7 @@ out=muscure.pdbqt</div>
</ul>
<pre><code>perl sort_mol2_bonds.pl MUS.mol2 MUS_fix.mol2</code></pre>
<h3>3. Preprocessing</h3>
- <h4>1. Build the system:</h4>
+ <h4>Build the system:</h4>
<ul>
<li>Generate the topology file <code>MOR_processed.gro</code> for the receptor using GROMACS's
<code>pdb2gmx</code> command.
@@ -706,7 +715,7 @@ out=muscure.pdbqt</div>
<pre><code>python cgenff_charmm2gmx_py3_nx2.py MUS MUS_fix.mol2 MUS.str charmm36-jul2022.ff</code></pre>
</ul>
- <h4>2. Merge the system:</h4>
+ <h4>Merge the system:</h4>
<ul>
<li>Prepare the complete solvent system required for simulations using the <code>editconf</code> and
<code>solvate</code> commands, merging the topology files of muscone <code>mus.gro</code> and
@@ -729,7 +738,7 @@ SOL 31227
CL 9</code></pre>
</ul>
- <h4>3. Energy minimization:</h4>
+ <h4>Energy minimization:</h4>
<ul>
<li>Perform energy minimization on the overall system to eliminate unreasonable conflicts in the
initial geometry. Achieve rapid convergence of energy through the gradient descent algorithm and
@@ -756,7 +765,7 @@ dit xvg_show -f potential.xvg</code></pre>
Minimization</p>
</ul>
<h3>4. Molecular Dynamics Simulation</h3>
- <h4>1. System Equilibration:</h4>
+ <h4>System Equilibration:</h4>
<ul>
<li>To achieve thermal and mechanical equilibrium of the system, simulations are conducted in two
stages: NVT (constant temperature) and NPT (constant pressure) equilibration. The system
@@ -793,7 +802,7 @@ dit xvg_show -f temperature.xvg</code></pre>
</div>
<p style="text-align: center; font-size: 0.9em; margin-top: 10px;">fig 11 Curve of the density over time
</p>
- <h4>2. Production Simulation:</h4>
+ <h4>Production Simulation:</h4>
<ul>
<li>Under the conditions of equilibrium, a long-term production simulation is conducted. This
simulation observes the time evolution characteristics of the dynamic interactions between
@@ -828,7 +837,7 @@ gmx mdrun -deffnm md_0_10</code></pre>
PyMOL.</li>
</ul>
- <h4>1. Trajectory Analysis:</h4>
+ <h4>Trajectory Analysis:</h4>
<p>To gain deeper insights into the interactions between muscone and the receptor, visualization tools
are used to make the simulation process intuitive, identifying key interaction sites and structural
@@ -882,7 +891,7 @@ gmx trjconv -s md_0_10.tpr -f md_0_10_fit.xtc -o traj.pdb -dt 10
</div>
<p style="text-align: center; font-size: 0.9em; margin-top: 10px;">fig 15 Trajectory Analysis
</p>
- <h4>2. RMSD (Root Mean Square Deviation) Analysis</h4>
+ <h4>RMSD (Root Mean Square Deviation) Analysis</h4>
<ul>
<li>RMSD provides a fundamental metric for measuring structural deviation during the simulation
@@ -921,7 +930,7 @@ xmgrace rmsd_mus.xvg</code></pre>
</div>
<p style="text-align: center; font-size: 0.9em; margin-top: 10px;">fig 16 RMSD Analysis
</p>
- <h4>3. Radius of Gyration (Rg) Calculation</h4>
+ <h4>Radius of Gyration (Rg) Calculation</h4>
<ul>
<li>The radius of gyration (Rg) is used to assess the compactness of a protein and is an important
@@ -946,7 +955,7 @@ xmgrace gyrate.xvg</code></pre>
</div>
<p style="text-align: center; font-size: 0.9em; margin-top: 10px;">fig 17 Radius of Gyration Calculation
</p>
- <h4>4. Protein-Ligand Interaction Energy</h4>
+ <h4>Protein-Ligand Interaction Energy</h4>
<ul>
<li>By calculating the Coulomb and Lennard-Jones interaction energies within the system, the binding
@@ -989,7 +998,7 @@ dit xvg_show -f interaction_energy.xvg</code></pre>
<h2 id="topic3">
<h2>Ordinary Differential Equation of the signal transduction of the yeast MAPK pathway</h2>
<hr>
- <h3>Model Description</h3>
+ <h3>1. Model Description</h3>
<p>In our project, we express the muscone receptor (GPCR) on the yeast cell membrane. After a
certain concentration of muscone diffuses into the intestine and binds to the receptor, it
activates the receptor, which in turn activates the G protein. The G protein dissociates into α and
@@ -1025,7 +1034,7 @@ dit xvg_show -f interaction_energy.xvg</code></pre>
<img src="https://static.igem.wiki/teams/5187/wiki-model-fig/mapk.png" alt="MAPK Pathway"
class="shadowed-image" style="width: 50%; max-width: 500px;">
</div>
- <h3>Basic Assumptions</h3>
+ <h3>2. Basic Assumptions</h3>
<ol>
<li>Since the model only simulates the signal transduction shortly after muscone activation, it
does not consider protein synthesis and degradation, assuming that the concentrations of each
@@ -1036,7 +1045,7 @@ dit xvg_show -f interaction_energy.xvg</code></pre>
factors.</li>
</ol>
- <h3>Model Equations</h3>
+ <h3>3. Model Equations</h3>
<h4>Activation of muscone Receptor</h4>
<strong>Reactions</strong>:
<div>
@@ -1848,9 +1857,9 @@ hold off;</div>
<div class="row mt-4">
<div class="col-lg-12">
<h2 id="topic4">
- <h2>lactate Absorption Model</h2>
+ <h2>Lactate Absorption Model</h2>
<hr>
- <h3>Model Description</h3>
+ <h3>1. Model Description</h3>
<p>
Our project alleviates IBD symptoms by secreting lactate in the intestine to weaken
autoimmunity, but it may face two aspects of doubt: first, why can't lactate or lactate
@@ -1862,7 +1871,7 @@ hold off;</div>
regulate treatment time and prevent acidosis.
</p>
- <h3>Basic Assumptions</h3>
+ <h3>2. Basic Assumptions</h3>
<ol>
<li>Only the absorption process of lactate is described, without considering other effects of
lactate on the human body.</li>
@@ -1872,7 +1881,7 @@ hold off;</div>
secrete a total amount of lactate \(a\) within time \(t_0\), secreting \(\frac{a}{n}\) of
lactate in the time interval \(\frac{t_0}{n}\).</li>
</ol>
- <h3>Model Equation</h3>
+ <h3>3. Model Equation</h3>
<p>
According to Fick's law :
</p>