Scientists from The University of Manchester (UK), Aalto University (Finland) and the European Molecular Biology Laboratory Heidelberg (Germany) mentioned that the new method seems to detect targets of regulator genes.
The human genome appears to encompass commands for making all the cells in our body. An individual cell’s make up apparently depends on how these directives are interpreted. This is said to be regulated by gene regulatory mechanisms. Detecting these mechanisms may be rather vital to significantly enhance the comprehension of biological systems.
One imperative regulatory mechanism is claimed to be based on genes that is believed to energetically endorse or suppress the activity of other genes. The new study seemingly deals with the issue of detecting the targets these regulator genes affect.
The new technique is said to be based on cautious modelling of time series measurements of gene activity. It appears to merge an easy biochemical model of the cell with probabilistic modelling to address the issue of imperfect and unsure measurements.
Dr Magnus Rattray, a senior researcher at Manchester’s Faculty of Engineering and Physical Sciences, commented, “Combining biochemical and probabilistic modelling techniques as done here holds great promise for the future. Many systems we are looking at now are too complex for purely physical models and connecting to experimental data in a principled manner is essential.”
Dr Antti Honkela, his colleague at Aalto University School of Science and Technology remarked that a major contribution of their work is to show how data-driven machine learning techniques can be used to uncover physical models of cell regulation. This demonstrates how data-driven modelling can clearly benefit from the incorporation of physical modelling ideas.
The research was published in the early edition of PNAS.