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Cell cycle: new models to explain processes, thanks to data science

Cell cycle: new models to explain processes, thanks to data science

In the cell, key processes such as chromosome replication and segregation and cell division, can occur in a non-sequential order, differently from what suggested by the traditional model.

Specifically, between the occurrence of the chromosome-related processes and cell division a “time bubble” may occur in which the chromosome is ready to divide but the cell is still waiting.

This is what emerges from a study carried out by a group of Italian scientists working in Milan, Paris and Santa Fe, using data science methods. 

The study was conducted by researchers at the FIRC Institute of Molecular Oncology and at the Universities of Milan and Turin, in collaboration with ETH in Zurich, Sorbonne in Paris and the Santa Fe Institute in the United States. The results have appeared in the journal Science Advances. 

Making a comparison with production processes, the cell cycle process would be similar to the “just in time” supply chains: a process in which the arrival times of the different materials in the production line are coordinated with the moment in which they are to be used. The waiting time would allow the different processes to be realigned thus ensuring correct coordination. 

“The data emerging from the study suggests that the bubble is more of a functional stage rather than dead time, and that it is due to a process preparing for division that occurs concurrently to the chromosome-related process. In some cells – about half of them – it can be slower and therefore the cell has to wait for its completion”, the authors explained.

The researchers observed the coordination of chromosome cycle and cell division in the bacterium Escherichia coli. They then dynamically tracked the individual behaviours of thousands of cells, for the first time putting together different datasets, and have come to their conclusions by combining mathematical models with a sophisticated analysis of all observable correlations.

“This is a complex analysis”, said Marco Cosentino Lagomarsino, head of the laboratory of statistical physics of cells and genomes at IFOM and professor at the University of Milan, returned to Italy after spending a long period of time in Paris. 

“The cell cycle – like an assembly line – involves different completion times for each stage of production, but the relations between these stages are very intricate and it is very difficult to infer the production processes from the data. It is only thanks to data science methods that we managed to solve the problem”, added the researcher. 

Source IFOM
Publication date 12/14/2018
Tag Health