What does stomach flu have in common with an anti-government hacker?
What can Google’s server system teach us about how the brain handles
sleep deprivation? And how can a classic game theory quandary explain
is exploring such questions by striving
to uncover algorithms in nature—strategies of how molecules, cells and
organisms solve computational problems without a central commander.
Discovering shared principles can help advance the fields of both
computer science and biology, leading to improved computing algorithms
and a better understanding of large, distributed biological systems.
While computer science and biology have had a long history of collabora-
tion, computer scientists working in biology have typically been limited to
analyzing troves of experimental information (e.g., imaging or sequencing
data) to uncover patterns. Now, computing experts are doing more than
just mining information, says Navlakha. Advances in “big data” and jumps
in technology in the last few years have triggered this shift.
“There’s been a resurgence of computer science interfacing with biolo-
gy because now we can really manipulate biological systems in ways we
couldn’t before,” says Navlakha, who began his research in computing
and graph theory but moved to exploring biological networks at the
encouragement of his advisor at the University of Maryland, College Park.
“We can get into finer details of biological processes instead of staying at
a broad, abstract level.”
Armed with new algorithmic and computational technologies, Navlakha,
who recently joined the Salk Institute, has already begun to explore
potential collaborations that cover the spectrum of biological questions.
Like Saghatelian, Navlakha’s expertise can apply to virtually all areas of
biology, from protein interactions to disease outcomes, plant growth to
Groundbreaking work by Salk Assistant Professor
example, suggests that killing a pathogen with antibiotics might not be
the most efficient way to treat an infection. Rather, she predicts that
developing therapeutics aimed at the collateral damage done to an
organism (rather than the pathogen itself) would lead to new infectious
disease treatments that pathogens will not evolve resistance to.
In conversations with Ayres, Navlakha saw parallels in how governments
or companies handle security breaches by hackers. When faced with a
digital invasion, organizations must decide if they will use their resources
to aggressively go after the hackers (pathogens) or focus on stabilizing
their system and minimizing collateral damage. The two researchers
began to collaborate to develop computational analyses that encompass
the principles of both host-pathogen interactions and hacker defense.
“My lab has the expertise to experimentally test our predictions at the level
of a single individual and within model populations, but it will be important
to predict within an epidemiological context how our approaches to treating
infectious diseases will impact the emergence and spread of resistant
pathogens,” says Ayres. “In our collaboration with the Navlakha lab, we will
be able to execute such computational analyses.”
Navlakha adds, “We’re interested in seeing if there are analogies in the way
tradeoffs are made in host-pathogen interactions that might be similar in
network engineering and security.” Finding such parallels could also help
both fields develop efficient ways to deal with cyber or biological invasions.
“The ability to participate in interdisciplinary collaborations with such
ease is the beauty of Salk,” says Ayres. “It is only through such
collaborations that science can be pushed into new and unexpected
directions, which ultimately leads to the most exciting discoveries.”
In addition to this work, Navlakha plans to connect with neuroscientists to
explore intriguing parallels between the brain and computers. When you
do a search on Google, a central commander selects one of thousands of
servers with a low activity load to take on that request. By evenly distrib-
uting requests for work, the system is able to quickly provide accurate
answers to users. The brain also does its own “load balancing” (called
homeostasis) to generate responses in a timely manner. The brain, however,
does all of this without a central commander.
between biology and computing
From left: Ullas Pedmale and Saket Navlakhawww.salk.edu
Inside Salk 04 | 15