Interview with C. Titus Brown - Computational biologist and open access champion

Good question.  Short answer: apparently somewhere along the way I
became a biologist, but with a heavy dose of “computational scientist”
in there.

The longer answer?  Well, it’s a really long answer…

My first research was on Avida, a bottom-up model for evolution that
Chris Adami, Charles Ofria and I wrote together at Caltech in 1993:
http://en.wikipedia.org/wiki/Avida.  (Fun fact: Chris, Charles and I
are now all faculty at Michigan State!  Chris and I have offices one
door apart, and Charles has an office one floor down.)  Avida got me
very interested in biology, but not in the undergrad “memorize stuff”
biology — more in research.  This was computational science: using
simple models to study biological phenomena.

While continuing evolution research, I did my undergrad in pure math at Reed
College, which was pretty intense; I worked in the Software Development
lab there, which connected me to a bunch of reasonably well known hackers
including Keith Packard, Mark Galassi, and Nelson Minar.

I also took a year off and worked on Earthshine:

http://en.wikipedia.org/wiki/Planetshine#Earthshine

and then rebooted the project as an RA in 1997, the summer after
graduation.  This was mostly data analysis, although it included a
fair amount of hanging off of telescopes adjusting things as the
freezing winter wind howled through the Big Bear Solar Observatory’s
observing room, aka “data acquisition”.

After Reed, I applied to a bunch of grad schools, including Princeton
and Caltech in bio, UW in Math, and UT Austin and Ohio State in
physics.  I ended up at Caltech, where I switched over to
developmental biology and (eventually) regulatory genomics and genome
biology in Eric Davidson’s lab.  My work there included quite a bit
of wet bench biology, which is not something many people associate with me,
but was nonetheless something I did!

Genomics was really starting to hit the fan in the early 2000s, and I
was appalled by how biologists were handling the data — as one
example, we had about $500k worth of sequences sitting on a shared
Windows server, with no metadata or anything — just the filenames.
As another example, I watched a postdoc manually BLAST a few thousand
ESTs against the NCBI nr database; he sat there and did them three by
three, having figured out that he could concatenate three sequences
together and then manually deconvolve the results.  As probably the
most computationally experienced person in the lab, I quickly got
involved in data analysis and Web site stuff, and ended up writing
some comparative sequence analysis software that was mildly popular
for a while.

As part of the sequence analysis Web site I wrote, I became aware that
maintaining software was a really hard problem.  So, towards the end
of my 9 year stint in grad school, I spent a few years getting into
testing, both Web testing and more generally automated software
testing.  This led to perhaps my most used piece of software, twill, a
scripting language for Web testing.  It also ended up being one of the
things that got me elected into the Python Software Foundation,
because I was doing everything in Python (which is a really great
language, incidentally).

I also did some microbial genome analysis (which led to my first
completely reproducible paper (Brown and Callan, 2004;
http://www.ncbi.nlm.nih.gov/pubmed/14983022) and collaborated with the
Orphan lab on some metagenomics:
http://www.ncbi.nlm.nih.gov/pubmed?term=18467493.  This led to a
fascination with the biological “dark matter” in nature that is the
subject of some of my current work on metagenomics.

I landed my faculty position at MSU right out of grad school, because
bioinformatics is sexy and CS departments are OK with hiring grad
students as faculty.  However, I deferred for two years to do a
postdoc in Marianne Bronner-Fraser’s lab because I wanted to switch to
the chick as a model organism, and so I ended up arriving at MSU in
2009.  I had planned to focus a lot on development gene regulatory
networks, but 2009 was when Illumina sequencing hit, and as one of the
few people around who wasn’t visibly frightened by the term “gigabyte”
I got inextricably involved in a lot of different sequence analysis
projects.  These all converged on assembly, and, well, that seems to
be what I work on now :).

The two strongest threads that run through my research are these:

1. “better science through superior software” — so much of science
depends on computational inference these days, and so little of the
underlying software is “good”.  Scientists really suck at software
development (for both good and bad reasons) and I worry that a lot of
our current science is on a really shaky foundation.  This is one
reason I’m invested in Software Carpentry
(http://software-carpentry.org), a training program that Greg Wilson
has been developing — he and I agree that science is our best hope
for a positive future, and good software skills are going to be
essential for a lot of that science.  More generally I hope to turn
good software development into a competitive advantage for my lab
and my students.

2. “better hypothesis generation is needed” — biologists, in
particular, tend to leap towards the first testable hypothesis they
find.  This is a cultural thing stemming (I think) from a lot of
really bad interactions with theory: the way physicists and
mathematicians think about the world simply doesn’t fit with the Rube
Goldberg-esque features of biology (see
http://ivory.idyll.org/blog/is-discovery-science-really-bogus.html).

So getting back to the question, uh, yeah, I think I’m a computational
scientist who is working on biology?  And if I need to write a little
(or a lot) of software to solve my problems, I’ll do that, and I’ll
try to do it with some attention to good software development
practice — not just out of ethical concern for correctness, but
because it makes our research move faster.

One thing I’m definitely not is a statistician.  I have friends who
are statisticians, though, and they seem like perfectly nice people.

Ever since Mark Galassi introduced me to open source, I thought it
made sense.  So I’ve been an open source-nik since … 1988?

From there it’s just a short step to thinking that open science makes
a lot of sense, too.  When you’re a grad student or a postdoc, you
don’t get to make those decisions, though; it took until I was a PI
for me to start thinking about how to do it.  I’m still conflicted
about how open to be, but I’ve come to the conclusion that posting
preprints is obvious
(http://ivory.idyll.org/blog/blog-practicing-open-science.html).

The “radical” aspect that you’re referring to is probably my posting
of grants (http://ivory.idyll.org/blog/grants-posted.html).  There are
two reasons I ended up posting all of my single-PI grants.  Both have
their genesis in this past summer, when I spent about 5 months writing
6 different grants — 4 of which were written entirely by me.  Ugh.

First, I was really miserable one day and joked on Twitter that “all
this grant writing is really cutting into my blogging” — a mocking
reference to the fact that grant writing (to get $$) is considered
academically worthwhile, while blogging (which communicates with the
public and is objectively quite valuable) counts for naught with my
employer.  Jonathan Eisen responded by suggesting that I post all of
the grants and I thought, what a great idea!

Second, I’m sure it’s escaped most people (hah!), but grant funding
rates are in the toilet — I spent all summer writing grants while
expecting most of them to be rejected.  That’s just flat-out
depressing!  So it behooves me to figure out how to make them serve
multiple duties.  One way to do that is to attract collaborators;
another is to serve as google bait for my lab; a third is to provide
my grad students with well-laid-out PhD projects.  A fourth duty they
serve (and I swear this was unintentional) is to point out to people
that this is MY turf and I’m already solving these problems, so maybe
they should go play in less occupied territory.  I know, very passive
aggressive…

So I posted the grants, and unknowingly joined a really awesome cadre
of folk who had already done the same
(http://jabberwocky.weecology.org/2012/08/10/a-list-of-publicly-available-grant-proposals-in-the-biological-sciences/).
Most feedback I’ve gotten has been from grad students and undergrads
who really appreciate the chance to look at grants; some people told
me that they’d been refused the chance to look at grants from their
own PIs!

At the end of the day, I’d be lucky to be relevant enough that people
want to steal my grants or my software (which, by the way, is under a
BSD license — free for the taking, no “theft” required…).  My
observation over the years is that most people will do just about
anything to avoid using other people’s software.

I wish I knew!  There’s clearly a tradition of secrecy in biology;
just look at the Cold Spring Harbor rules re tweeting and blogging
(http://meetings.cshl.edu/report.html) - this is a conference, for
chrissakes, where you go to present and communicate!  I think it’s
self-destructive and leads to an insider culture where only those who
attend meetings and chat informally get to be members of the club,
which frankly slows down research. Given the societal and medical
challenges we face, this seems like a really bad way to continue doing
research.

One of the things I’m proudest of is our effort on the cephalopod
genome consortium’s white paper,
http://ivory.idyll.org/blog/cephseq-cephalopod-genomics.html, where a
group of bioinformaticians at the meeting pushed really hard to walk
the line between secrecy and openness.  I came away from that effort
thinking two things: first, that biologists were erring on the side of
risk aversity; and second, that genome database folk were smoking
crack when they pushed for complete openness of data.  (I have a blog
post on that last statement coming up at some point.)

The bottom line is that the incentives in academic biology are aligned
against openness.  In particular, you are often rewarded for the first
observation, not for the most useful one; if your data is used to do
cool stuff, you don’t get much if any credit; and it’s all about
first/last authorship and who is PI on the grants.  All too often this
means that people sit on their data endlessly.

This is getting particularly bad with next-gen data sets, because
anyone can generate them but most people have no idea how to analyze
their data, and so they just sit on it forever…

One of my favorite quotes is: “Making predictions is hard, especially
when they’re about the future.” I attribute it to Niels Bohr.

It’ll take a bunch of big, important scientists to lead the way. We
need key members of each subcommunity of biology to decide to do it on
a regular basis. (At this point I will take the obligatory cheap shot
and point out that Jonathan Eisen, noted open access fan, doesn’t post
his stuff to preprint servers very often.  What’s up with that?)  It’s
going to be a long road.

“Ohmigod what if someone steals them?”

Nobody has come up with a really convincing model for why posting
grants is a bad thing.  They’re just worried that it might be.  I
get the vague concerns about theft, but I have a hard time figuring
out exactly how it would work out well for the thief — reputation is
a big deal in science, and gossip would inevitably happen.  And at
least in bioinformatics I’m aiming to be well enough known that
straight up ripping me off would be suicidal.  Plus, if reviewers
do/did google searches on key concepts then my grants would pop up,
right?  I just don’t see it being a path to fame and glory for anyone.

Revisiting the passive-aggressive nature of my grant posting, I’d like
to point out that most of my grants depend on preliminary results from
our own algorithms.  So even if they want to compete on my turf, it’ll
be on a foundation I laid.  I’m fine with that — more citations for
me, either way :).

More optimistically, I really hope that people read my grants and then
find new (and better!) ways of solving the problems posed in them.  My
goal is to enable better science, not to hunker down in a tenured job
and engage in irrelevant science; if someone else can use my grants as
a positive or negative signpost to make progress, then broadly
speaking, my job is done.

Or, to look at it another way: I don’t have a good model for either
the possible risks OR the possible rewards of posting the grants, and
my inclinations are towards openness, so I thought I’d see what
happens.

Render them irrelevant by becoming senior researchers who supplant them
when they retire.  It’s the academic tradition, after all!  And it’s
really the only way within the current academic system, which — for
better or for worse — isn’t going anywhere.

Honestly, we need fewer people yammering on about open access and more
people simply doing awesome science and submitting it to OA journals.
Conveniently, many of the high impact journals are shooting themselves
in the foot and encouraging this by rejecting good science that then
ends up in an OA journal; that wonderful ecology oped on PLoS One
citation rates shows this well
(http://library.queensu.ca/ojs/index.php/IEE/article/view/4351).

For courses, no — in my opinion 80% of what any good researcher
learns is self-motivated and often self-taught, and so it’s almost
silly to pretend that any particular course or set of skills is
sufficient or even useful enough to warrant a whole course.  I’m not a
big fan of our current undergrad educational system

For skills?  You need critical thinking coupled with an awareness that
a lot of smart people have worked in science, and odds are that there
are useful tricks and approaches that you can use.  So talk to other
people, a lot!  My lab has a mix of biologists, computer scientists,
graph theorists, bioinformaticians, and physicists; more labs should
be like that.

Good programming skills are going to serve you well no matter what, of
course.  But I know plenty of good programmers who aren’t very
knowledgeable about biology, and who run into problems doing actual
science.  So it’s not a panacea.

How does replicable or reproducible research fit into your interests?

I’ve wasted so much time reproducing other people’s work that when
the opportunity came up to put down a marker, I took it.

http://ivory.idyll.org/blog/replication-i.html

The digital normalization paper shouldn’t have been particularly
radical; that it is tells you all you need to know about replication
in computational biology.

This is actually something I first did a long time ago, with what was
perhaps my favorite pre-faculty-job paper: if you look at the methods
for Brown & Callan (2004) you’ll find a downloadable package that
contains all of the source code for the paper itself and the analysis
scripts.  But back then I didn’t blog :).

Lack of reproducibility and openness in methods has serious
consequences — how much of cancer research has been useless, for
example?  See this horrific report</span> <span><</span><a href="http://online.wsj.com/article/SB10001424052970203764804577059841672541590.html" target="_blank"><a href="http://online.wsj.com/article/SB10001424052970203764804577059841672541590.html" target="_blank">http://online.wsj.com/article/SB10001424052970203764804577059841672541590.html</a></a><span>>__.)
Again, the incentives are all wrong: you get grant money for
publishing, not for being useful.  The two are not necessarily the
same…

Do you have a family, and how do you balance work life and home life?

Why, thank you for asking!  I do have a family — my wife, Tracy Teal,
is a bioinformatician and microbial ecologist, and we have two
wonderful daughters, Amarie (4) and Jessie (1).  It’s not easy being a
junior professor and a parent at the same time, and I keep on trying
to figure out how to balance the needs of travel with the need to be a
parent (hint: I’m not good at it).  I’m increasingly leaning towards
blogging as being a good way to have an impact while being around
more; we’ll see how that goes.