Computer-assisted biology: Decoding noisy data to predict cell growth


Date:
July 9, 2021
Source:
Institute of Industrial Science, The University of Tokyo
Summary:
Researchers used artificial intelligence to obtain a more objective
understanding of cell growth and division without preconceived
assumptions. Using a deep-learning neural network, they were able
to more accurately model the complex processes that affect cell
size over time.

This work may lead to advances in microbiology and industrial
production of microorganisms.



FULL STORY ========================================================================== Scientists from The University of Tokyo Institute of Industrial Science
have designed a machine learning algorithm to predict the size of an
individual cell as it grows and divides. By using an artificial neural
network that does not impose the assumptions commonly employed in biology,
the computer was able to make more complex and accurate forecasts than previously possible. This work may help advance the field of quantitative biology as well as improve the industrial production of medications or fermented products.


==========================================================================
As in all of the natural sciences, biology has developed mathematical
models to help fit data and make predictions about the future. However,
because of the inherent complexities of living systems, many of these
equations rely on simplifying assumptions that do not always reflect
the actual underlying biological processes. Now, researchers at The
University of Tokyo Institute of Industrial Science have implemented
a machine learning algorithm that can use the measured size of single
cells over time to predict their future size.

Because the computer automatically recognizes patterns in the data,
it is not constrained like conventional methods.

"In biology, simple models are often used based on their capacity
to reproduce the measured data," first author Atsushi Kamimura
says. "However, the models may fail to capture what is really going
on because of human preconceptions,." The data for this latest
study were collected from either an Escherichia coli bacterium or a Schizosaccharomyces pombe yeast cell held in a microfluidic channel at
various temperatures. The plot of size over time looked like a "sawtooth"
as exponential growth was interrupted by division events. Human biologists usually use a "sizer" model, based on the absolute size of the cell,
or "adder" model, based on the increase in size since birth, to predict
when divisions will occur. The computer algorithm found support for the
"adder" principle, but as part of a complex web of biochemical reactions
and signaling.

"Our deep-learning neural network can effectively separate the history- dependent deterministic factors from the noise in given data," senior
author Tetsuya Kobayashi says.

This method can be extended to many other aspects of biology besides
predicting cell size. In the future, life science may be driven more
by objective artificial intelligence than human models. This may lead
to more efficient control of microorganisms we use to ferment products
and produce drugs.

========================================================================== Story Source: Materials provided by Institute_of_Industrial_Science,_The_University_of_Tokyo.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Atsushi Kamimura, Tetsuya J. Kobayashi. Representation and
inference of
size control laws by neural-network-aided point processes. Physical
Review Research, 2021; 3 (3) DOI: 10.1103/PhysRevResearch.3.033032 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210709104218.htm

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