Biomedical engineers at Duke University have devised a machine
learning approach to modeling the interactions between complex variables
in engineered bacteria that would otherwise be too cumbersome to
predict. Their algorithms are generalizable to many kinds of biological
In the new study, the researchers trained a neural network to
predict the circular patterns that would be created by a biological
circuit embedded into a bacterial culture. The system worked 30,000
times faster than the existing computational model.
To further improve accuracy, the team devised a method for
retraining the machine learning model multiple times to compare their
answers. Then they used it to solve a second biological system that is
computationally demanding in a different way, showing the algorithm can
work for disparate challenges.
The results appear online in the journal Nature Communications.
“This work was inspired by Google showing that neural networks could
learn to beat a human in the board game Go,” said Lingchong You,
professor of biomedical engineering at Duke.
“Even though the game has simple rules, there are far too many
possibilities for a computer to calculate the best next option
deterministically,” You said. “I wondered if such an approach could be
useful in coping with certain aspects of biological complexity
The challenge facing You and his postdoctoral associate Shangying
Wang was determining what set of parameters could produce a specific
pattern in a bacteria culture following an engineered gene circuit.
In previous work, You’s laboratory programmed bacteria to produce
proteins that, depending on the specifics of the culture’s growth,
interact with one another to form rings. By controlling variables such
as the size of the growth environment and the amount of nutrients
provided, the researchers found they could control the ring’s thickness,
how long it took to appear and other characteristics.
By changing any number of dozens of potential variables, the
researchers discovered they could do more, such as causing the formation
of two or even three rings. But because a single computer simulation
took five minutes, it became impractical to search any large design
space for a specific result.
For their study, the system consisted of 13 bacterial variables such
as the rates of growth, diffusion, protein degradation and cellular
movement. Just to calculate six values per parameter would take a single
computer more than 600 years. Running it on a parallel computer cluster
with hundreds of nodes might cut that run-time down to several months,
but machine learning can cut it down to hours.
“The model we use is slow because it has to take into account
intermediate steps in time at a small enough rate to be accurate,” said
You. “But we don’t always care about the intermediate steps. We just
want the end results for certain applications. And we can (go back to)
figure out the intermediate steps if we find the end results
To skip to the end results, Wang turned to a machine learning model
called a deep neural network that can effectively make predictions
orders of magnitude faster than the original model. The network takes
model variables as its input, initially assigns random weights and
biases, and spits out a prediction of what pattern the bacterial colony
will form, completely skipping the intermediate steps leading to the
While the initial result isn’t anywhere close to the correct answer,
the weights and biases can be tweaked each time as new training data
are fed into the network. Given a large enough “training” set, the
neural network will eventually learn to make accurate predictions almost
To handle the few instances where the machine learning gets it
wrong, You and Wang came up with a way to quickly check their work. For
each neural network, the learning process has an element of randomness.
In other words, it will never learn the same way twice, even if it’s
trained on the same set of answers.
The researchers trained four separate neural networks and compared
their answers for each instance. They found that when the trained neural
networks make similar predictions, these predictions were close to the
“We discovered we didn’t have to validate each answer with the
slower standard computational model,” said You. “We essentially used the
‘wisdom of the crowd’ instead.”
With the machine learning model trained and corroborated, the
researchers set out to use it to make new discoveries about their
biological circuit. In the initial 100,000 data simulations used to
train the neural network, only one produced a bacterial colony with
three rings. But with the speed of the neural network, You and Wang were
not only able to find many more triplets, but determine which variables
were crucial in producing them.
“The neural net was able to find patterns and interactions between
the variables that would have been otherwise impossible to uncover,”
As a finale to their study, You and Wang tried their approach on a
biologic system that operates randomly. Solving such systems requires a
computer model to repeat the same parameters many times to find the most
probable outcome. While this is a completely different reason for long
computational run times than their initial model, the researchers found
their approach still worked, showing it is generalizable to many
different complex biological systems.
The researchers are now trying to use their new approach on more
complex biological systems. Besides running it on computers with faster
GPUs, they’re trying to program the algorithm to be as efficient as
“We trained the neural network with 100,000 data sets, but that
might have been overkill,” said Wang. “We’re developing an algorithm
where the neural network can interact with simulations in real-time to
help speed things up.”
“Our first goal was a relatively simple system,” said You. “Now we
want to improve these neural network systems to provide a window into
the underlying dynamics of more complex biological circuits.”
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