New biological patterns have been discovered among different cellular processes due to medical researchers obtaining useful information through the assistance of big data in biology, making it easier to understand and predict internal activities in cells.
The scientists have relied heavily on big data to make new quantitative discoveries in biology, particularly in respect to the likes of genome, the microbiome, personalised medicine and disease modelling, EurekaAlert reports.
Up until now, scientists have been able to generate data about a cell’s or an organisim’s complete set of genes, proteins, RNA profiles, metabolites, etc. which goes by the term “omic” data. By applying “omic” data, scientists are now able to model complex biological interactions in order to get a more holistic view of the various cellular processes. However, what has appeared to be a challenge is analysing and understanding these large sets of data.
Bernhard Palsson, Galetti Professor of Bioengineering at the Jacobs School of Engineering at UC San Diego and senior author of the study said: “When doing big data analysis, it is important to know how all these different data types are related. Now we have a way of connecting multiple different data types to generate fundamental answers to biological questions,” said Bernhard Palsson, Galetti Professor of Bioengineering at the Jacobs School of Engineering at UC San Diego and senior author of the study”.
Elizabeth Brunk, a postdoctoral researcher in Palsson’s lab and a co-first author of the study explained that: “While all these data types are derived from the same cell, they represent processes occurring at very different scales. Our work is about getting multiple different data types synchronised so that we can understand the coordination of these processes and derive meaning from them”.
EurekaAlert explains that this study is a part of a larger effort to address a grand challenge posed by the National Institutes of Health called “Big Data to Knowledge”. The challenge includes translating large, complex biological data sets into information that can be understood based on fundamentals. In this study, researchers collected multiple “omic” data types, i.e. RNA sequences, ribosome profiles, protein data and metabolic data from E. coli grown in different growth environments. The team then integrated these different data types into next-generation genome-scale models of metabolism, which were then developed in Palsson’s lab.
The result to the research revealed that new regularities were discovered, which were biological consistencies throughout a change in environment. One of the regularities discovered showed that during a protein translation, ribosomes consistently pause at particular sites along a messenger RNA transcript, where these pause sites then dictate the three-dimensional structure of the protein, Reuters writes.
Palsson explained that: “Pause sites exist so that a protein has time to fold and form its overall shape, which is important for the protein to function correctly. This knowledge is useful for studying cancer biology. If a tumour has a genetic mutation that eliminates a pause site, translation will yield a protein that’s not folded correctly and malfunctions”.
This article was first found at: https://www.eurekalert.org/pub_releases/2016-10/uoc–nao102516.php