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Posts Tagged ‘computational biology’


Author and Reporter: Anamika Sarkar, Ph.D

Early in the month of September, Nature, published 30 research papers on the results found from the ambitious and one time felt risky project, named, ENCODE (Encyclopedia of DNA Elements). The results of ENCODE revealed that 80% of human genome is not “junk”, as thought before, rather act as  regulatory domains for further signaling events.

When human genome was first sequenced, more than a decade ago, scientists were surprised with the low ratio of coding regions transcribing genes to the number of bases in human DNA. Out of 3 billion bases in human DNA scientists found only 21,000 genes. This unexpected finding led to few basic questions:

  • Why do humans have so many base pairs?
  • How highly regulated complex behaviors of biochemical, cellular and physiological processes can be translated to regulation at genetic levels?

ENCODE project results unveil our limited knowledge about human genome until now. Their results open up new ways of thinking human DNA and its functional domains. It also brings in huge challenges for both experimental developments and data driven computational approaches for better understanding and applications of these new findings.

To gain insight from large scale data and identifying key players from a large pool of data, Bioinformatics approaches will  probably be the only way to move forward. This also means importance of developing new algorithms which will include the capability of including regulatory functions linking with gene regulation. Presently, most algorithms are targeted toward identifying genes and their connections in a linear fashion. However, regulatory domains and their functional activities might be non linear, something which will be revealed with many more experimental results in coming years.

The functional characteristics of human genome will also lead to better understanding of genetic differences between normal states and disease states. Moreover, with proper identification of functional characteristics of a particular gene regulation, drugs can be targeted with much more precision in future. However, to make success of such a complicated problem, it will require visionary design and execution of experiment and computational biology teams working together.

It is well recognized already that Bioinformatics approaches can hugely help in identifying key players in regulation of genes. However many times it is not easy to translate information at the genetic levels directly to cellular or physiological levels. Some of the main reasons are – a) the complex cross talks between proteins which lead to intracellular signaling events and b) highly non linear information sharing among receptors and ligands for extra cellular signaling processes.  To achieve efficient understanding of the functional characteristics of non-coding regions of DNA in context with regulation of genes, an effort should be given to map the functional network of gene regulation to signaling pathways of protein networks. This will require development of experimental as well as computational approaches to capture genetic as well as proteomics analysis together. Furthermore, for better understanding of cellular and physiological decisions,  mapping between regulations of genes and intracellular signaling pathways should be extended for dynamic analysis with time.

The extraordinary findings from ENCODE project pose many challenges in front for getting answers to many unknowns for next decade or so but also give solutions to some basic questions which have haunted scientific world for almost a decade.

Sources:

News and Views- ENCODE explained:  http://www.nature.com/nature/journal/v489/n7414/full/489052a.html

News and Analysis – ENCODE Project writes Eulogy for Junk DNA : http://www.sciencemag.org/content/337/6099/1159.summary?sid=835cf304-a61f-45d5-8d77-ad44b454e448

ENCODE Project (Nature Article): http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html

 

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Reported by: Dr. Venkat S. Karra, Ph.D.

“Comprehensive computer models of entire cells have the potential to advance our understanding of cellular function and, ultimately, to inform new approaches for the diagnosis and treatment of disease.” Not only does the model allow researchers to address questions that aren’t practical to examine otherwise, it represents a stepping-stone towards its use  in bioengineering and medicine.

A team led by Stanford bioengineering Professor Markus Covert used data from more than 900 scientific papers to account for every molecular interaction that takes place in the life cycle of Mycoplasma genitalium. Mycoplasma genitalium is a humble parasitic bacterium, known mainly for showing up uninvited in human urogenital and respiratory tracts. But the pathogen also has the distinction of containing the smallest genome of any free-living organism – only 525 genes, as opposed to the 4,288 of E. coli, a more traditional laboratory bacterium.

“This is potentially the new Human Genome Project,” Karr said who is a co-first author and Stanford biophysics graduate student. “It’s to understand biology generally.”

“It’s going to take a really large community effort to get close to a human model.”

This is a breakthrough effort for computational biology, the world’s first complete computer model of an organism. “This achievement demonstrates a transforming approach to answering questions about fundamental biological processes,” said James M. Anderson, director of the National Institutes of Health Division of Program Coordination, Planning, and Strategic Initiatives.

Study results were published by Stanford researchers in the journal Cell.

The research was partially funded by an NIH Director’s Pioneer Award from the National Institute of Health Common Fund.

Source:

http://www.dddmag.com/news/2012/07/first-complete-computer-model-bacteria?et_cid=2783229&et_rid=45527476&linkid=http%3a%2f%2fwww.dddmag.com%2fnews%2f2012%2f07%2ffirst-complete-computer-model-bacteria

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