A team of researchers at the NHC, led by Dr Claudio Angione, explored how computational modelling techniques could be used to better predict and, more importantly, understand, how cell cultures react under different conditions.
Over the course of two years, the team devised a method to integrate and computationally describe both the genetic and metabolic activity of more than 1,000 different strains of Saccharomyces cerevisiae – a common “workhorse” yeast in biotechnology, baking and brewing. The researchers used machine learning to identify patterns in genetic and metabolic datasets, and demonstrate the ability to reveal unknown biological interactions.
Dr Angione’s research, which has been published in the Proceedings of the National Academy of Sciences (PNAS), showed that, not only can machine learning be used to interpret data and give a successful prediction of how cells might react under certain conditions, but also understand the chemical processes which led to this conclusion.
This could have major implication in fields such as medicine, for example to predict how a tumour or cancerous cells might grow. Equally in pharmacology, the ability to understand the different chemical reactions that take place is vital to identifying potential side-effects of a drug.
Using computational modelling is a hugely efficient way of predicting the biological functions and properties of living organisms, as much of the experimentation can be done digitally, drastically reducing the need for time in laboratories.
However, in the past, it has had its limitations. This research allows for a much more thorough understanding of what takes place within a cell, therefore enabling biological interpretation of the predictions of machine learning algorithms. This will, hopefully, make a huge impact in areas such as drug development and medicine
