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<!--
slidedeck: A modification of the Google IO 2012 HTML5 slide template
URL: https://github.com/rmcgibbo/slidedeck
Based on https://github.com/francescolaffi/elastic-google-io-slides, and
ultimately:
Google IO 2012 HTML5 Slide Template
Authors: Eric Bidelman <[email protected]>
Luke Mahe <[email protected]>
URL: https://code.google.com/p/io-2012-slides
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<!DOCTYPE html>
<html>
<head>
<title> Engineering a full python stack for biophysical computation</title>
<meta charset="utf-8">
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<body style="opacity: 0">
<slides class="layout-widescreen">
<slide class="title-slide segue nobackground">
<hgroup class="auto-fadein">
<h1> Engineering a full python stack for biophysical computation</h1>
<h2></h2>
<p> Kyle A. Beauchamp, Patrick Grinaway, Choderalab@MSKCC<br/> Slides here: http://tinyurl.com/n4vq9aj</p>
</hgroup>
</slide>
<slide >
<hgroup>
<h2>Moore's Law</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=550 src=figures/moore_law.png />
</center></p>
<footer class="source">
http://en.wikipedia.org/wiki/Moore%27s_law
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Does Moore's law apply to medicine?</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=450 src=figures/drugs.jpg />
</center></p>
<footer class="source">
http://seniorhousingforum.net/
</footer></article>
</slide>
<slide >
<hgroup>
<h2>New Drug Approvals: Stagnant</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=450 src=figures/new_mol_ent.jpg />
</center></p>
<footer class="source">
http://www.forbes.com/sites/matthewherper/2011/06/27/the-decline-of-pharmaceutical-researchmeasured-in-new-drugs-and-dollars/
</footer></article>
</slide>
<slide >
<hgroup>
<h2>erooM's Law for R&D Efficiency</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=450 src=figures/eroom.png />
</center></p>
<footer class="source">
Scannell, 2012
</footer></article>
</slide>
<slide >
<hgroup>
<h2>A crisis in drug discovery</h2>
<h3>Producing a drug costs $2B and 15 years, with 95% fail rate</h3>
</hgroup>
<article ><p><center>
<img height=450 src=figures/cost_structure.jpg />
</center></p>
<footer class="source">
Paul, 2010
</footer></article>
</slide>
<slide >
<hgroup>
<h2>How can computers help design drugs?</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=475 src=figures/cruise.png />
</center></p>
<footer class="source">
Figure credit: @jchodera, Tom Cruise
</footer></article>
</slide>
<slide class="segue dark nobackground" >
<!-- <aside class="gdbar"><img src="images/google_developers_icon_128.png"></aside> -->
<hgroup class="auto-fadein">
<h2>A brief introduction to biophysics</h2>
<h3></h3>
</hgroup>
</slide>
<slide >
<hgroup>
<h2>The cell as a bag of protein machines</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=475 src=figures/ecoli.gif />
</center></p>
<footer class="source">
http://mgl.scripps.edu/people/goodsell/illustration/public/ecoli-icon.gif
</footer></article>
</slide>
<slide >
<hgroup>
<h2>How do drugs work?</h2>
<h3>By binding and controlling misbehaving proteins</h3>
</hgroup>
<article ><p><center>
<img height=415 src=figures/2HW0-covalent.png />
</center></p>
<footer class="source">
PDB Code: 2HWO. Figure generated by @sonyahanson
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Challenges in molecular medicine</h2>
<h3></h3>
</hgroup>
<article ><ul>
<li>Can we link nanoscale biophysics with human disease?</li>
<li>Can we rationally engineer protein-drug binding?</li>
</ul></article>
</slide>
<slide >
<hgroup>
<h2>Our Toolbox: Molecular Dynamics</h2>
<h3></h3>
</hgroup>
<article ><ul>
<li>Physics-based simulations of biomolecules</li>
<li>Numerically integrate (classical) equations of motion</li>
<li>Protein, water, salts, drugs</li>
</ul>
<div>
<video id="sampleMovie" class="center" src="movies/shaw-dasatanib-2.mov" loop=\"true\ autoPlay=\"true\ width="512" height="384"></video>
</div>
<footer class="source">
Shan et al: J. Am. Chem. Soc. (2011). <br>
http://deshawresearch.com/
</footer></article>
</slide>
<slide class="segue dark nobackground" >
<!-- <aside class="gdbar"><img src="images/google_developers_icon_128.png"></aside> -->
<hgroup class="auto-fadein">
<h2>Software for Biophysics</h2>
<h3></h3>
</hgroup>
</slide>
<slide >
<hgroup>
<h2>How do we reach biological timescales?</h2>
<h3>Challenge: 10^5 atoms, 10^12 iterations.</h3>
</hgroup>
<article ><p><center>
<img height=450 src=figures/protein_timescales.jpg />
</center></p>
<footer class="source">
Church, 2011.
</footer></article>
</slide>
<slide >
<hgroup>
<h2>OpenMM</h2>
<h3>GPU accelerated molecular dynamics</h3>
</hgroup>
<article ><ul>
<li>Extensible C++ library with Python wrappers</li>
<li>Hardware backends for CUDA, OpenCL, CPU</li>
<li><mathjax>$>100$</mathjax> nanoseconds (<mathjax>$10^{-7}$</mathjax> s) per day on a GTX Titan</li>
</ul>
<p><center>
<img height=300 src=figures/openmm.png />
</center></p>
<footer class="source">
openmm.org <br>
Eastman et al, 2012.
</footer></article>
</slide>
<slide >
<hgroup>
<h2>OpenMM Powers Folding@Home</h2>
<h3></h3>
</hgroup>
<article ><ul>
<li>Largest distributed computing project</li>
<li>100,000 CPUs, 10,000 GPUs, 40 petaflops!</li>
<li>Hundreds of microseconds per day aggregate simulation</li>
<li>~100 research papers on folding, misfolding, signalling</li>
</ul>
<p><center>
<img height=300 src=figures/folding-icon.png />
</center></p>
<footer class="source">
http://folding.stanford.edu/ <br>
Gromacs also powers Folding@Home: http://gromacs.org/
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Trajectory munging with MDTraj</h2>
<h3>Read, write, and analyze trajectories with only a few lines of Python.</h3>
</hgroup>
<article ><ul>
<li>Multitude of formats (PDB, DCD, XTC, HDF, CDF, mol2)</li>
<li>Geometric trajectory analysis (distances, angles, RMSD)</li>
<li>Numpy / SSE kernels enable Folding@Home scale analysis</li>
</ul>
<p><center>
<img height=200 src=figures/mdtraj_logo-small.png />
</center></p>
<footer class="source">
mdtraj.org <br>
McGibbon et al, 2014
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Trajectory munging with MDTraj</h2>
<h3>Lightweight Pythonic API</h3>
</hgroup>
<article ><pre class="prettyprint" data-lang="python">
import mdtraj as md
trajectory = md.load("./trajectory.h5")
indices, phi = md.compute_phi(trajectory)
</pre>
<p><center>
<img height=300 src=figures/phi.png />
</center></p>
<footer class="source">
mdtraj.org <br>
McGibbon et al, 2014
</footer></article>
</slide>
<slide >
<hgroup>
<h2>MDTraj IPython Notebook</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<img height=525 src=figures/mdtraj_notebook.png />
</center></p>
<footer class="source">
mdtraj.org <br>
McGibbon et al, 2014
</footer></article>
</slide>
<slide >
<hgroup>
<h2>MSMBuilder</h2>
<h3>Finding meaning in massive simulation datasets</h3>
</hgroup>
<article ><p><center>
<img height=300 src=figures/msmbuilder.png />
</center></p>
<footer class="source">
msmbuilder.org <br>
https://github.com/msmbuilder/msmbuilder
</footer></article>
</slide>
<slide >
<hgroup>
<h2>MSMBuilder</h2>
<h3>Markov State Models of Conformational Dynamics</h3>
</hgroup>
<article ><p><center>
<img height=400 src=figures/NTL9_network.jpg />
</center></p>
<footer class="source">
Voelz, Bowman, Beauchamp, Pande. J. Am. Chem. Soc., 2010
</footer></article>
</slide>
<slide >
<hgroup>
<h2>MSMBuilder</h2>
<h3>MSMBuilder: An sklearn-compatible framework for conformation dynamics</h3>
</hgroup>
<article ><pre class="prettyprint" data-lang="python">
# To install, `conda install -c https://conda.binstar.org/omnia msmbuilder`
import mdtraj as md
from msmbuilder import example_datasets, cluster, markovstatemodel
from sklearn.pipeline import make_pipeline
dataset = example_datasets.alanine_dipeptide.fetch_alanine_dipeptide() # From Figshare!
trajectories = dataset["trajectories"] # List of MDTraj Trajectory Objects
clusterer = cluster.KCenters(n_clusters=10, metric="rmsd")
msm = markovstatemodel.MarkovStateModel()
pipeline = make_pipeline(clusterer, msm)
pipeline.fit(trajectories)
</pre>
<footer class="source">
msmbuilder.org <br>
https://github.com/msmbuilder/msmbuilder
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Yank</h2>
<h3>Fast, accurate alchemical ligand binding simulations</h3>
</hgroup>
<article ><p><center>
<div>
<video id="sampleMovie" class="center" src="movies/alch.mov" loop=\"true\ autoPlay=\"true\ width="512" height="384"></video>
</div>
</center></p>
<footer class="source">
https://github.com/choderalab/yank <br>
http://alchemistry.org/
</footer></article>
</slide>
<slide class="segue dark nobackground" >
<!-- <aside class="gdbar"><img src="images/google_developers_icon_128.png"></aside> -->
<hgroup class="auto-fadein">
<h2>Python Packaging Blues</h2>
<h3></h3>
</hgroup>
</slide>
<slide >
<hgroup>
<h2>Building scientific software is hard!</h2>
<h3></h3>
</hgroup>
<article ><pre>
<font color="red">User: I couldn't really install the mdtraj module on my computer [...]
User: I tried easy_install and other things and that didn't work for me.</font>
</pre></article>
</slide>
<slide >
<hgroup>
<h2>Building scientific software is hard!</h2>
<h3>2008: I was compiling BLAS / Numpy / Scipy by hand...</h3>
</hgroup>
<article ><p><center>
<img height=520 src=figures/dependencies0.png />
</center></p>
<footer class="source">
Red means hard to install.
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Building scientific software is hard!</h2>
<h3>2010: Switched to Enthought python</h3>
</hgroup>
<article ><p><center>
<img height=520 src=figures/dependencies1.png />
</center></p>
<footer class="source">
Red means hard to install.
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Building scientific software is hard!</h2>
<h3>Present: Conda</h3>
</hgroup>
<article ><p><center>
<img height=520 src=figures/dependencies2.png />
</center></p>
<footer class="source">
Red means hard to install.
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Avoiding glibc Hell</h2>
<h3></h3>
</hgroup>
<article ><pre class="prettyprint" data-lang="bash">
-bash-4.1$ parmchk2
~/opt/bin/parmchk2_pvt: /lib64/libc.so.6: version `GLIBC_2.14' not found
</pre>
<ul>
<li>Problem: users insist on old Linux versions</li>
<li>Solution: build all recipes on a Centos 6.6 VM </li>
</ul>
<footer class="source">
https://github.com/omnia-md/virtual-machines/
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Facile package sharing</h2>
<h3></h3>
</hgroup>
<article ><pre>
<font color="red">User: I couldn't really install the mdtraj module on my computer [...]
User: I tried easy_install and other things and that didn't work for me.</font>
<font color="blue">Me: Installing mdtraj should be a one line command:
Me: `conda install -c https://conda.binstar.org/omnia mdtraj`</font>
<font color="red">User: Success!</font>
</pre></article>
</slide>
<slide >
<hgroup>
<h2>A full stack for biophysical computation</h2>
<h3>Simulation, Munging, Analysis, Visualization</h3>
</hgroup>
<article ><pre class="prettyprint" data-lang="bash">
conda install -c https://conda.binstar.org/omnia/channel/test omnia
</pre>
<ul>
<li>OpenMM</li>
<li>MDTraj</li>
<li>MSMBuilder</li>
<li>Yank</li>
<li>EMMA<mathjax>$^1$</mathjax></li>
</ul>
<footer class="source">
1: Senne, Noe. J. Chem. Theor. Comp. 2012
</footer></article>
</slide>
<slide class="segue dark nobackground" >
<!-- <aside class="gdbar"><img src="images/google_developers_icon_128.png"></aside> -->
<hgroup class="auto-fadein">
<h2>Automating Biophysics</h2>
<h3></h3>
</hgroup>
</slide>
<slide >
<hgroup>
<h2>Models are made to be broken</h2>
<h3>How can we falsify and refine computer based models?</h3>
</hgroup>
<article ><ul>
<li>Chemistry and biophysics are labor-intensive</li>
<li>Thousands of parameters = thousands of measurements</li>
<li>Reproducibilty and scalability</li>
</ul></article>
</slide>
<slide >
<hgroup>
<h2>Can experiments be easy as Py(thon)?</h2>
<h3></h3>
</hgroup>
<article ><pre class="prettyprint" data-lang="python">
from itctools.procedures import ITCExperiment
from itctools.materials import Solvent
from itctools.labware import Labware
# [...]
water = Solvent('water', density=0.9970479 * grams / milliliter)
source_plate = Labware(RackLabel='SourcePlate', RackType='5x3 Vial Holder')
experiment = ITCExperiment()
</pre>
<footer class="source">
Work by @jhprinz and @bas-rustenburg
<br>
https://github.com/choderalab/robots <t> https://github.com/choderalab/itctools
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Robots!</h2>
<h3></h3>
</hgroup>
<article ><p><center>
<video width="960" height="540" controls>
<source src="movies/robot.mp4" type="video/mp4">
</video>
</center> </p></article>
</slide>
<slide >
<hgroup>
<h2>Biophysical modeling should be:</h2>
<h3></h3>
</hgroup>
<article ><ul>
<li>Reproducible</li>
<li>Automatable</li>
<li>Accessible</li>
<li>Tested</li>
<li>Useful</li>
</ul></article>
</slide>
<slide >
<hgroup>
<h2>People</h2>
<h3></h3>
</hgroup>
<article ><ul>
<li>John Chodera + ChoderaLab (MSKCC)</li>
<li>Robert McGibbon (Stanford)</li>
<li>Peter Eastman (Stanford)</li>
<li>Vijay Pande + PandeLab (Stanford)</li>
<li>Daniel Parton (MSKCC)</li>
<li>Yutong Zhao (Stanford, Folding@Home)</li>
<li>Joy Ku (Stanford)</li>
<li>Jason Swails (Rutgers)</li>
<li>Justin MacCallum (U. Calgary)</li>
</ul>
<footer class="source">
Jan-Hendrik Prinz, Bas Rustenburg, Sonya Hanson
Greg Bowman, Christian Schwantes, TJ Lane, Vince Voelz, Imran Haque, Matthew Harrigan, Carlos Hernandez, Bharath Ramsundar, Lee-Ping Wang
Frank Noe, Martin Scherer, Xuhui Huang, Sergio Bacallado, Mark Friedrichs
</footer></article>
</slide>
<slide >
<hgroup>
<h2>Questions?</h2>
<h3></h3>
</hgroup>
<article ><pre class="prettyprint" data-lang="bash">
conda config --add channels http://conda.binstar.org/omnia/
conda install -c http://conda.binstar.org/omnia/channel/omnia1_beta1 omnia
</pre>
<p>omnia.md</p>
<p>openmm.org</p>
<p>mdtraj.org</p>
<p>github.com/msmbuilder/msmbuilder</p>
<p>github.com/choderalab/yank/</p></article>
</slide>
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