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27.04.19 Discovering gene circuits using machine-learning algorithms

last modified Jun 03, 2019 01:19 PM
Tom Hiscock in the Simons group applied a computational approach to generate algorithms describing natural gene circuits and for designing circuits in synthetic biology. The work is published in BMC Bioinformatics

Adapting machine-learning algorithms to design gene circuits

Hiscock TW (2019) BMC Bioinformatics 20(1): 214. DOI: 10.1186/s12859-019-2788-3.

 

 

 

Abstract from the paper

Background

Gene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turing’s model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform.

Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery.

Results

We use gradient-descent optimization algorithms from machine learning to rapidly screen and design gene circuits. With this approach, we found that we could rapidly design circuits capable of executing a range of different functions, including those that: (1) recapitulate important in vivo phenomena, such as oscillators, and (2) perform complex tasks for synthetic biology, such as counting noisy biological events.

Conclusions

Our computational pipeline will facilitate the systematic study of natural circuits in a range of contexts, and allow the automatic design of circuits for synthetic biology. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.

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Read more about research in the Simons lab.

Watch Ben Simons describe his research on YouTube.

 

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