In natural science, we have seen biology utilize mathematical models to process data and predict future outcomes. However, this is more difficult when dealing with the dynamics of living systems. Most of the data rely upon simplifying assumptions that do not result in the organic biology process that is true. Researchers at The University of Tokyo have created a way to deal with the complexity of living systems. They developed a machine-learning algorithm that uses the size of one cell over the course of time to make future predictions about their size. The computer can quickly identify patterns in the data set, unlike the boundaries of conventional methods. Recently, data was collected from either an Escherichia coli bacterium or a Schizosaccharomyces pombe yeast cell that was positioned in various temperatures in a microfluidic channel. What the researchers noticed was over time the size appeared to look like a "sawtooth" as growth was halted by division events. Normally, human biologists utilize a "sizer" model based on the exact size of the cell, the increase in size since birth, to anticipate when cellular division will happen. Fortunately, the algorithm discovered supporting efforts for the absolute size principle. Senior author, Tetsuya Kobayashi stated, "Our deep-learning neural network can effectively separate the history-dependent deterministic factors from the noise in given data." The team noted that this method could be utilized in other areas of biology and not just predicting cell size. The takeaway from this is that possibly soon life science can be more influenced by artificial intelligence than human models. In doing so, this will produce a greater control over microorganisms that are used to ferment products and produce drugs.
Source: https://www.sciencedaily.com/releases/2021/07/210709104218.htm
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