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<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>David Wooten</title>
<link>https://djwooten.github.io/</link>
<atom:link href="https://djwooten.github.io/index.xml" rel="self" type="application/rss+xml" />
<description>David Wooten</description>
<generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><copyright>© 2018 - 2020 David Wooten</copyright><lastBuildDate>Thu, 01 Oct 2020 00:00:00 +0000</lastBuildDate>
<image>
<url>https://djwooten.github.io/images/logo_hufcc7f18ea081c5a5c1d6a1ea8035b9ba_249_300x300_fit_lanczos_2.png</url>
<title>David Wooten</title>
<link>https://djwooten.github.io/</link>
</image>
<item>
<title>synergy - A Python library for calculating, analyzing, and visualizing drug combination synergy</title>
<link>https://djwooten.github.io/publication/wooten-2020-synergy/</link>
<pubDate>Thu, 01 Oct 2020 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/wooten-2020-synergy/</guid>
<description></description>
</item>
<item>
<title>synergy - Python Package</title>
<link>https://djwooten.github.io/software/synergy/</link>
<pubDate>Thu, 23 Apr 2020 12:00:07 -0400</pubDate>
<guid>https://djwooten.github.io/software/synergy/</guid>
<description><h1 id="synergy">synergy</h1>
<p>A python package to calculate, analyze, and visualize drug combination synergy and antagonism. Currently supports multiple models of synergy, including MuSyC, Bliss, Loewe, Combination Index, ZIP, Zimmer, BRAID, Schindler, and HSA.</p>
<h2 id="installation">Installation</h2>
<p>Using PIP
<code>pip install synergy</code></p>
<p>Using conda
<code>not yet</code></p>
<p>Using git</p>
<pre><code>git clone git@github.com:djwooten/synergy.git
cd synergy
pip install -e .
</code></pre>
<h2 id="example-usage">Example Usage</h2>
<h3 id="generate-synthetic-data-to-fit">Generate synthetic data to fit</h3>
<pre><code class="language-python">from synergy.combination import MuSyC
from synergy.utils.dose_tools import grid
</code></pre>
<p>Initialize a model. I will use the <code>MuSyC</code> synergy model to generate data, but it could be done using <code>Zimmer</code> or <code>BRAID</code> as well.</p>
<pre><code class="language-python">E0, E1, E2, E3 = 1, 0.7, 0.4, 0.
h1, h2 = 2.3, 0.8
C1, C2 = 1e-2, 1e-1
alpha12, alpha21 = 3.2, 1.1
gamma12, gamma21 = 2.5, 0.8
truemodel = MuSyC(E0=E0, E1=E1, E2=E2, E3=E3, h1=h1, h2=h2, C1=C1, C2=C2, \
alpha12=alpha12, alpha21=alpha21, gamma12=gamma12, \
gamma21=gamma21)
</code></pre>
<p>Display the model&rsquo;s parameters</p>
<pre><code class="language-python">print(truemodel)
</code></pre>
<pre><code>MuSyC(E0=1.00, E1=0.70, E2=0.40, E3=0.00, h1=2.30, h2=0.80, C1=1.00e-02, C2=1.00e-01, oalpha12=3.20, oalpha21=1.10, beta=0.67, gamma12=2.50, gamma21=0.80)
</code></pre>
<p>Evaluate the model at doses d1=C1, d2=C2 (a combination of the EC50 of each drug)</p>
<pre><code class="language-python">print(truemodel.E(C1, C2))
</code></pre>
<pre><code>0.3665489890285983
</code></pre>
<p>Generate a dose sampling grid to make &ldquo;measurements&rdquo; at. Drug 1 will be sampled at 8 doses, logarithmically spaced from C1/100 to C1*100. Drug 2 will be likewise sampled around C2. (8 doses of Drug 1) X (8 doses of Drug 2) = 64 total measurements.</p>
<pre><code class="language-python">d1, d2 = grid(C1/1e2, C1*1e2, C2/1e2, C2*1e2, 8, 8)
print(d1.shape, d2.shape)
</code></pre>
<pre><code>(64,) (64,)
</code></pre>
<p>Evaluate the model at those 64 dose combinations</p>
<pre><code class="language-python">E = truemodel.E(d1, d2)
print(E.shape)
</code></pre>
<pre><code>(64,)
</code></pre>
<p>Add noise to get imperfect data</p>
<pre><code class="language-python">import numpy as np
E_noisy = E * (1+0.1*(2*np.random.rand(len(E))-1))
print(E_noisy.shape)
</code></pre>
<pre><code>(64,)
</code></pre>
<h3 id="fit-synergy-model-to-data">Fit synergy model to data</h3>
<p>Create a new synergy model to fit using the synthetic data. Here I use <code>MuSyC</code>, which is the same model we used to generate the synthetic data. <code>bootstrap_iterations</code> are used to get confidence intervals.</p>
<pre><code class="language-python">model = MuSyC()
model.fit(d1, d2, E_noisy, bootstrap_iterations=100)
print(model)
</code></pre>
<pre><code>MuSyC(E0=0.93, E1=0.68, E2=0.42, E3=0.00, h1=1.86, h2=1.12, C1=9.64e-03, C2=1.24e-01, alpha12=3.75, alpha21=1.08, beta=0.81, gamma12=2.01, gamma21=0.98)
</code></pre>
<p>This prints the best fit and lower and upper bound confidence intervals (defaults to 95%) for each parameter.</p>
<pre><code class="language-python">print(model.get_parameters())
</code></pre>
<pre><code>{'E0': (0.93, [ 0.91223345 0.96329156]),
'E1': (0.68, [ 0.64643766 0.70749396]),
'E2': (0.42, [ 0.39022822 0.44990642]),
'E3': (0.00, [-0.02507603 0.02363363]),
'h1': (1.86, [ 1.26005438 2.73713318]),
'h2': (1.12, [ 0.93018994 1.43865508]),
'C1': (9.64e-03, [ 0.00760803 0.01384544]),
'C2': (1.24e-01, [ 0.10018859 0.15263104]),
'alpha12': (3.75, [ 2.85988609 4.6230902 ]),
'alpha21': (1.08, [ 0.73239517 1.79969918]),
'beta': (0.81, [ 0.69827786 0.95770258]),
'gamma12': (2.01, [ 1.44083572 2.76863031]),
'gamma21': (0.98, [ 0.56548907 1.83905139])}
</code></pre>
<h3 id="visualize">Visualize</h3>
<pre><code class="language-python">from matplotlib import pyplot as plt
from synergy.utils import plots
fig = plt.figure(figsize=(12,6))
ax = fig.add_subplot(131)
truemodel.plot_colormap(d1, d2, xlabel=&quot;Drug1&quot;, ylabel=&quot;Drug2&quot;, title=&quot;True model&quot;, ax=ax, vmin=0, vmax=1)
ax = fig.add_subplot(132)
plots.plot_colormap(d1, d2, E_noisy, ax=ax, title=&quot;Noisy Data&quot;, cmap=&quot;viridis&quot;, xlabel=&quot;Drug1&quot;, ylabel=&quot;Drug2&quot;, vmin=0, vmax=1)
ax = fig.add_subplot(133)
model.plot_colormap(d1, d2, xlabel=&quot;Drug1&quot;, ylabel=&quot;Drug2&quot;, title=&quot;Fit model&quot;, ax=ax, vmin=0, vmax=1)
plt.tight_layout()
</code></pre>
<p><img src="output_9_0.png" alt="png"></p>
<pre><code class="language-python">
</code></pre>
</description>
</item>
<item>
<title>Charting the Fragmented Landscape of Drug Synergy</title>
<link>https://djwooten.github.io/publication/meyer-2020/</link>
<pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/meyer-2020/</guid>
<description></description>
</item>
<item>
<title>Data Driven Mechanistic Modeling</title>
<link>https://djwooten.github.io/project/data-driven/</link>
<pubDate>Mon, 11 Nov 2019 20:36:25 -0500</pubDate>
<guid>https://djwooten.github.io/project/data-driven/</guid>
<description></description>
</item>
<item>
<title>Multiscale Cell / Micro- environment Dynamics</title>
<link>https://djwooten.github.io/project/multiscale/</link>
<pubDate>Mon, 11 Nov 2019 13:42:12 -0400</pubDate>
<guid>https://djwooten.github.io/project/multiscale/</guid>
<description></description>
</item>
<item>
<title>Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers</title>
<link>https://djwooten.github.io/publication/wooten-506402/</link>
<pubDate>Thu, 31 Oct 2019 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/wooten-506402/</guid>
<description></description>
</item>
<item>
<title>Cancer Control</title>
<link>https://djwooten.github.io/project/cancer-control/</link>
<pubDate>Tue, 03 Sep 2019 13:42:12 -0400</pubDate>
<guid>https://djwooten.github.io/project/cancer-control/</guid>
<description><p>In response to treatment, cancer cells can react in complex, difficult to predict ways. Here we consider this through a lens of <strong>treatment-induced driving forces pushing cancer cells around an epigenetic landscape</strong>. This force may cause new phenotypes to emerge, old phenotypes to disapear, and/or the balance between pre-existing phenotypes to shift. The overall goal of this project is to identify treatments that can epigenetically <strong>reprogram drug-resistant cancer cells toward drug-sensitive states</strong>.</p>
<ul>
<li>Identify regulatory and epigenetic factors stabilizing drug-resistant or quiescent phenotypes</li>
<li>Identify treatments that can target those key regulators to de-stabilize undesirable states</li>
<li>Validate drug combinations in which one agent reprograms cells toward the sensitive state, and the other agent kills cells in sensitive state</li>
</ul>
<p>To achieve these ends, we employ a mixture of bioinformatic analyses and mechanistic modeling.</p>
</description>
</item>
<item>
<title>Drug Synergy</title>
<link>https://djwooten.github.io/project/synergy/</link>
<pubDate>Tue, 03 Sep 2019 12:00:07 -0400</pubDate>
<guid>https://djwooten.github.io/project/synergy/</guid>
<description><p>Many diseases require combinations of multiple drugs for effective treatment. I am interested in identifying, quantifying, and understanding synergistic or antagonistic interactions between drugs in combinations.</p>
<p>A web application for calculating MuSyC synergy parameters for combinations is now available!
<a href="https://musyc.lolab.xyz/" title="Synergy web application" target="_blank" rel="noopener">https://musyc.lolab.xyz/</a></p>
</description>
</item>
<item>
<title>A Consensus Framework Unifies Multi-Drug Synergy Metrics</title>
<link>https://djwooten.github.io/publication/wooten-683433/</link>
<pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/wooten-683433/</guid>
<description></description>
</item>
<item>
<title>Quantifying Drug Combination Synergy along Potency and Efficacy Axes</title>
<link>https://djwooten.github.io/publication/meyer-2019/</link>
<pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/meyer-2019/</guid>
<description></description>
</item>
<item>
<title>Ventricular-Subventricular Zone Contact by Glioblastoma is Not Associated with Molecular Signatures in Bulk Tumor Data</title>
<link>https://djwooten.github.io/publication/mistry-2019/</link>
<pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/mistry-2019/</guid>
<description></description>
</item>
<item>
<title>Mathematical models of cell phenotype regulation and reprogramming: Make cancer cells sensitive again!</title>
<link>https://djwooten.github.io/publication/wooten-2017/</link>
<pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/wooten-2017/</guid>
<description></description>
</item>
<item>
<title>Novel hybrid phenotype revealed in small cell lung cancer by a transcription factor network model that can explain tumor heterogeneity</title>
<link>https://djwooten.github.io/publication/udyavar-2017/</link>
<pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
<guid>https://djwooten.github.io/publication/udyavar-2017/</guid>
<description></description>
</item>
</channel>
</rss>