First it was chess.
Then it was Jeopardy.
Now computers are at
it again, but this time they are trying to automate the scientific process
team of scientists at Vanderbilt University, Cornell University,
and CFD Research Corporation Inc., has taken a major step toward this goal by demonstrating
that a computer can analyze raw experimental data from a biological system and
derive the basic mathematical equations that describe the way the system
operates. According to the researchers, it is one of the most complex
scientific modeling problems that a computer has solved completely from scratch.
The paper that describes
this accomplishment is published in the journal Physical Biology and
is currently available online. The work was a collaboration between John P.
Wikswo, the Gordon A. Cain University Professor at Vanderbilt, Michael Schmidt
and Hod Lipson at the Creative Machines Lab at Cornell University and
Jerry Jenkins and Ravishankar Vallabhajosyula at CFDRC in Huntsville, Ala.
The “brains” of the
system, which Wikswo has christened the Automated Biology Explorer (ABE), is a
unique piece of software called Eureqa developed at Cornell and released in
2009. Schmidt and Lipson originally created Eureqa to design robots without
going through the normal trial and error stage that is both slow and expensive.
After it succeeded, they realized it could also be applied to solving science
One of Eureqa’s initial
achievements was identifying the basic laws of motion by analyzing the motion
of a double pendulum. What took Sir Isaac Newton years to discover, Eureqa did
in a few hours when running on a personal computer.
In 2006, Wikswo heard Lipson lecture about his
research. “I had a ‘eureka moment’ of my own when I realized the system Hod had
developed could be used to solve biological problems and even control them,”
Wikswo says. So he started talking to Lipson immediately after the lecture and
they began a collaboration to adapt Eureqa to analyze biological problems.
“Biology is the area where the gap between theory and data is growing the
most rapidly,” says Lipson. “So it is the area in greatest need of automation.”
The biological system
that the researchers used to test ABE is glycolysis, the primary process that
produces energy in a living cell. Specifically, they focused on the manner in
which yeast cells control fluctuations in the chemical compounds produced by the
The researchers chose
this specific system, called glycolytic oscillations, to perform a virtual test
of the software because it is one of the most extensively studied biological
control systems. Jenkins and Vallabhajosyula used one of the process’ detailed
mathematical models to generate a data set corresponding to the measurements a
scientist would make under various conditions. To increase the realism of the
test, the researchers salted the data with a 10% random error. When they fed
the data into Eureqa, it derived a series of equations that were nearly
identical to the known equations.
amazing is that it produced these equations a priori,” says
Vallabhajosyula. “The only thing the software knew in advance was addition,
subtraction, multiplication, and division.”
The ability to generate mathematical equations
from scratch is what sets ABE apart from Adam, the robot scientist developed by
Ross King and his colleagues at the University
of Wales at Aberystwyth.
Adam runs yeast genetics experiments and made international headlines two years
ago by making a novel scientific discovery without direct human input. King fed
Adam with a model of yeast metabolism and a database of genes and proteins
involved in metabolism in other species. He also linked the computer to a remote-controlled
genetics laboratory. This allowed the computer to generate hypotheses, then
design and conduct actual experiments to test them.
“It’s a classic
paper,” Wikswo says.
In order to give ABE
the ability to run experiments like Adam, Wikswo’s group is currently developing
“laboratory-on-a-chip” technology that can be controlled by Eureqa. This will
allow ABE to design and perform a wide variety of basic biology experiments.
Their initial effort is focused on developing a microfluidics device that can
test cell metabolism.
“Generally, the way
that scientists design experiments is to vary one factor at a time while
keeping the other factors constant, but, in many cases, the most effective way
to test a biological system may be to tweak a large number of different factors
at the same time and see what happens. ABE will let us do that,” Wikswo says.
“Biology is more complex than astronomy or physics
or chemistry,” maintains Wikswo, a physicist who has spent his career studying
biological systems. “In fact, it may be too complex for the human brain to
This complexity stems
from the fact that biological processes range in size from the dimensions of an
atom to those of a whale and in time from a billionth of a second to billions
of seconds. Biological processes also have a tremendous dynamic range: for example,
the human eye can detect a star at night that is one billionth as bright as
objects viewed on a sunny day.
Then there is the
matter of sheer numbers. A cell expresses between 10,000 to 15,000 proteins at
any one time. Proteins perform all the basic tasks in the cell, including
producing energy, maintaining cell structures, regulating these processes, and
serving as signals to other cells. At any one time there can be anywhere from
three to 10 million copies of a given protein in the cell.
According to Wikswo,
the crowning source of complication is that processes at all these different
scales interact with one another: “These multi-scale interactions produce
emergent phenomena, including life and consciousness.”
Looked at from a
mathematical point of view, to create an accurate model of a single mammalian
cell may require generating and then solving somewhere between 100,000 to one
Balanced against this
complexity is the capability of the human brain. The biophysicist cites
research that has found that the human brain can only process seven pieces of
data at a time and quotes a 1938 assessment of brain research by Emerson Pugh:
“If the human brain were so simple that we could understand it, we would be so
simple that we couldn’t.”
That is where robot scientists like ABE and Adam
come in, Wikswo argues. They have the potential for both generating and
analyzing the tremendous amounts of data required to really understand how
biological systems work and predict how they will react to different
“We set out to work with robots, but our path took us, through many twists and
turns, to automating science,” says Lipson, associate director of the Creative
His starting point was an attempt to breed robot
control systems using an approach modeled on natural selection, instead of
having a programmer code in all the steps. Individual programming had largely
broken down as robots became more complex because the robots didn’t perform
correctly without extensive and time-consuming debugging.
Lipson used a
procedure called genetic programming for the breeding process. It involves
starting with the basic components of a robot, randomly combining them in
millions of different configurations and then testing how well they perform by
a specific criterion, such as how fast they can move. The designs that work the
best are then randomly combined and tested. These steps are repeated until it
produces a design that is acceptable. However, this process also proved to be
So Lipson combined
the breeding and the debugging processes in an approach he calls co-evolution.
He started with a crude simulator, used it to design a robot, tested the design,
and studied how it failed. He used this information to improve the simulator so
that it could predict the failure. Then he used the improved simulator to
design another robot, tested the design, watched how it failed, and improved
the simulator once again. Repeating these steps of co-evolving simulators and
robots produced increasingly competent designs, he found.
After proving that
co-evolution works for robot design, Lipson realized that it could be
generalized to solve other problems. Specifically, he adapted it for the
mathematical process of curve fitting, more generally called symbolic
regression. This involves deriving equations that can describe various data
package, which he and student Michael Schmidt named Eureqa, proved to be
extremely successful. As the word got around, he began getting requests for copies
of the program and decided to make it into a citizen science project, available
for anyone to download on the Internet.
“Today, it has more
than 20,000 users. People are using it to solve problems in a wide variety of
areas including traffic, business and neighborhood problems,” Lipson says. He
and his students tested it to see if they could predict the stock market, but
it didn’t work. “It may have worked for others, who aren’t talking about it,”
The software didn’t
work on the first biology program it was given either. Gurol Suel, a researcher
at the University of Texas Southwestern Medical Center, sent Lipson an
extensive data set from his studies of single cell dynamics and asked him to
run it through Eureqa. When Lipson and Schmidt did so and sent him back the
results, Suel informed them they didn’t make any sense. As they thought about
the problem, the researchers realized that they hadn’t given the software the
tools it needed.
“We had given it the
ability to add, subtract, multiply and divide and to calculate sines and
cosines. But sines and cosines weren’t relevant, while other factors that we
hadn’t included, such as time delays, were,” he explains. When they made this
adjustment, Eureqa derived a set of elegant equations that were simpler than
the ones Suel had derived, but Suel said that he didn’t know how to interpret
meaning of the equations that Eureqa generates can be a problem, Lipson
acknowledged: “We may have to create another program to do this.”
Wikswo isn’t as
concerned. He maintains that this approach will give scientists the ability to
control biological systems even if they can’t completely explain how they work,
and this capability can provide the basis for the development of significantly
improved drugs and other therapies.