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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

plant growth?

Assistant Professor

Saket Navlakha

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

brain development.

Groundbreaking work by Salk Assistant Professor

Janelle Ayres

, for

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 Navlakha

Inside Salk 04 | 15