Robotics and biology combined: AI to read or predict cell growth via noise data

A team of scientists coming from The University of Tokyo Institute of Industrial Science developed an artificial intelligence that allows them to further understand cell growth and its division without any bias.
By: HyperCharge Technology
RAMAT GAN, Israel - Aug. 31, 2021 - PRLog -- They designed a machine learning algorithm capable of decoding the complex processes that can affect cell as they experience growth by taking advantage of an artificial neural network. Their method allows them to bypass the current assumptions available in biology, and the accuracy of forecasts are higher than previous projects.

According to natural sciences, biology utilizes mathematical computations and predictions to determine the future outcome, but because living beings are more complex than others, a lot of the process were rather simplified assumptions that does not follow the whole biological process.

The goal of the researchers is to avoid such assumptions, that's why they developed the said machine learning algorithm. It was said to be able to predict the future size of a single cell by measuring its size and by recognizing patterns on data.

Their project's data came from experiments on Escherichia coli bacteria, or more commonly known as a Schizosaccharomyces pombe yeast cell, or yeast in short. The yeast cell was exposed to varying temperatures to determine other factors that can cause or trigger the cell to grow.

Based on this, the computer algorithm managed to successfully predict the final cell size before it manages to divide, as well as the length of time needed for it to reach the said size. Without the restriction of the conventional methods, the algorithm was able to successfully observe, measure and predict the duration and size of cell growth.

This study opened a lot of possibility and potential application as the core of biology is the cell. Since a project that allows us to further understand the cells was developed, the information and data can be used in other fields such as the possibility of controlling and breeding microorganisms to be used in the medicinal field. It can also be used to improve the quality and production of fertilizers for the agricultural industry.

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