Biomedical Engineers Apply Machine Learning to Biological Circuits
Biomedical engineers at Duke University have figured out a way to use machine learning in order to model interactions that take place between complex variables in engineered bacteria. Traditionally, this type of modeling has been to difficult to complete, but these new algorithms can be used within multiple different types of biological systems.
The new research was published in the journal Nature Communications on September 25.
The biomedical researchers looked at a biological circuit that was embedded into a bacterial culture, and they were able to predict circular patterns. This new way of modeling was extremely faster than traditional methods. Specifically, it was 30,000 times faster than the current computational model.
In order to be more accurate, the researchers then retrained the machine learning model multiple times. They compared the answers and used it on a second biological system. The second system was computationally different than the first, so the algorithm wasn’t limited to one set of problems.
Lingchong You is a professor of biomedical engineering at Duke.
“This work was inspired by Google showing that neural networks could learn to beat a human in the board game Go.” she said.
“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 confronting us.”
The study used 13 different bacterial variables including rates of growth, diffusion, protein degradation and cellular movement. A single computer would need at least 600 years to calculate six values per parameter, but the new machine learning system can complete it in 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 Lingchong 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 interesting.”
Postdoctoral associate Shangying Wang used a deep neural network that is able to make predictions much faster than the original model. The network uses model variables as the input, and it assigns random weights and biases. Then, it makes a prediction regarding the pattern that the bacterial colony will follow.
The first result isn’t correct, but the network slightly changes the weights and biases as it is given new training data. Once there has been enough training data, the predictions will become more accurate and stay that way.
There were four different neural networks that were trained, and their answers were compared. The researchers discovered that whenever the neural networks make similar predictions, they were close to the correct answer.
“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.”
After the machine learning model was sufficiently trained, the biomedical researchers used it on a biological circuit. There were 100,000 data simulations used to train the neural network. Out of all of those, only one produced a bacterial colony with three rings, but they were also able to identify certain variables that were important.
“The neural net was able to find patterns and interactions between the variables that would have been otherwise impossible to uncover,” said Wang.
To close out the study, the researchers tested it on a biological system that operates randomly. Traditionally, they would have to use a computer model that repeats certain parameters multiple times until it identifies the most probable outcome. The new system was able to do this as well, and it showed that it can be applied to various different complex biological systems.
The biomedical researchers have now turned to more complex biological systems, and they are working on developing the algorithm to become even more efficient.
“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.”