The role of blogging in Biology – Biogging

Blogging is the “in-thing” for the present generation and will probably continue to be one of the most popular form of sharing knowledge, experience, information with a personalized approach. Blogposts can be just de facto or de jure, what one felt that day or what interesting one learnt, what one wants to do or how one should do. We can broadly categorize blogs into “Strategic” and “random” blogs. Here, I wish to discuss how strategic sharing of biological information, approaches, theories, beliefs and even speculations, are providing a niche for accelerated spread of knowledge. QUESTION: have biologists actually taken up the electronic sharing system seriously. ANSWER - “yes” and India is an active participant in biology related searches on Google. 

The tabular representation of google search trend for keyword “biology” shows India being at the second position (Trend 1). If this is HOT, let me now tell you what is HAPPENING. India holds the first place when it comes to google searches for “Bioinformatics” (Trend 2), with cities like Hyderabad, Chennai, Mumbai, Delhi etc occupying top seats on the global scale. Now if there is a demand for knowledge on Biology and bioinformatics, following the quote “Necessity is the mother of inventions”, resources will be created. There have been many instances where I have been benefited by blogs for example, I was facing issues during installation of double precision MPI-enabled version of Gromacs on Linux. I found a very comprehensive and “easy –to-understand” blog (which has now been converted to a website- www.somewhereville.com). There I amny blogs which I personally admire following. One of them being http://rosettadesigngroup.com/blog/ and http://practicalfragments.blogspot.com/. It is fun, educative and mind intriguing. There is one very important aspect of blogging, you SHARE knowledge – not only Impart. I personally feel this bidirectional traffic of knowledge and opinion is really exciting. The best part is you can communicate with people you hardly know and are geographically miles and miles away for you and there is a community following your discussions which minimizes the chances of channelization ofwrong knowledge. To wrap up - We are really fortunate of being able to write and share our views as free lancers. But this freedom of writing comes with responsibility to convey what is right. I really feel strategic blogging is crucial for Biology and its time to roll up your sleeves to participate in “Biogging”.

Choosing between academia and industry

A very common dilemma that we face is choosing between Academia and Industry. I myself have gone through the such confusions many a times and I understand that when you are an undergraduate how lucrative industry might appear ! So how to decide between the two. I read this very interesting and thought provoking article that appeared in PloS Computational Biology. The author ,David B. Searls has very neatly put forth the following 10 rules that we should consider while choosing the academia or the industry:
1.Assess your qualification
2.Assess your needs
3.Assess your desires
4.Assess your personality
5.Consider the alternatives
6.Consider the timing
7.Plan for the long term
8.Keep your options open
9.Be analytical
10.Be honest with yourself

I could very easily connect with these rules and specially the fact that there might be a stage where you may have completed you masters with a view of going to the industry all along, and never considering academia as an option even in dreams. This kind of approach can be lethal because of lack of “Practical” Bioinformatics. PhD and post-doctoral stages make you more practical , more educated (If you don't hate reading like I do) and definitely more robust. I personally feel these stages help you accepting challenges and even failures pretty well. The other important aspect is that the time of switching to industry is very crucial. The author discusses the recent trend of shrinking the R&D and mega-mergers of firms in industry and similar trends of cutting down of budgets in academia. These situations carry risks and opportunities both as the business cycle is bound to reverse and healthcare will be a major global priority. So one should wisely craft the short term and long term goals and ultimate objective one wishes to achieve professionally. Being ambitious is good and being open to broader ambitions is better. One should keep ones eyes open to new and evolving technologies as well as opportunities. When you land in a decision making situation make sure you analyze all the pros and cons with similar weights applied (honesty element is vital and tricky). Apply the scientific analytical methods you learned to analyze your decision and fate . I would quote what the author says : When you interview, don’t just impress,
but get impressions; record everything
down to your gut feelings.
To wrap it up, be calm, logical, analytical and optimistic because the career choice that you make is important but not irreversible. As the author says : “Don't let the decision process ruin what should be an exciting time for you”

Cheers

Reference : http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000388

Microsoft goes open : Thanks to Bioinformatics

Bioinformatics seemed to be jinxed in the beginning of my career but I am glad gradually multinationals are recognizing the power of prediction and insilico analysis. The latest to follow the trend are proprietary software giants Micosoft.

Microsoft Biology Foundation, or MFB, is a bioinformatics toolkit built atop the .NET framework. This toolkit is based on the concept of open development, code sharing, and cross-platform support.
Keeping with the spirit of this tradition, MFB will be released under the Microsoft Public License.

As claimed, the toolkit should be a super time saver for the users and promises to offer range of algorithms for manipulating DNA, RNA, and protein sequences; and a set of connectors to biological Web services such as NCBI BLAST.


Futuristic Goals of MFB

Extensibility
Language neutrality
Supporting best practices
Cross-platform and interoperability
Building community


Access

The first beta version of MBF is now available!

For More

http://research.microsoft.com/en-us/collaboration/tools/mbf.aspx

Fragment Based Drug Discovery

The jest of finding new drug is often eclipsed by unpredictable and uncontrollable aspects of drug designing. Basically, there are two approaches that are regularly practiced :

1. Focus on a small library representing scaffolds that are known to inhibit a class of targets.
   
2. Focus on small, simple molecules – Fragments screened at high concentration to find molecules that can be developed into drugs
   

These approaches require combination of expertise from computational chemists, structural biologists, organic chemists, biologists and biophysicists.

 

FBDD utilizes biophysical techniques to screen about 1000 small fragments which lie in range of 150-250 MW. Despite of the strong theoretical aspects, the implementation is a twisted tale of rigorous quantifying fragments because more complex molecules have greater probability of mismatches.

There are two major challenges of FBDD are as stated below:

1. Lack of specialized methods to detect fragment binding
   
2. Need of efficient optimization of fragment hits.
   

But despite all kind of stigma and apprehensions attached to this approach, today almost a dozen FBDD leads targeting different protein families in different disease areas have progressed towards clinical trials. As more leads take their course to clinical trials it will be possible to access the contribution of this approach to modern medicine.

 

For further reading on the topic please refer to:

• The rise of fragment-based drug discovery (Link)
  
• Fragonomics: fragment-based drug discovery (Link)

New Approach to conquer diseases :Intrinsically disordered proteins as potential drug targets

A new approach is being taken up by researchers where they are now focusing on intrinsically disordered proteins as potential drug targets. A couple of exciting work have sprung up in the recent past.

A whole proteome analysis has been carried out for Mycobacterium tuberculosis in our lab. The study revealed 13 potential drug targets which should be considered while prioritizing Anti-microbial drug targets.

One such suggested drug target - GlmU (Rv1018c) has been prioritized by Open Source Drug Discovery (OSDD) consortium (http://sysborgtb.osdd.net/bin/view/OpenProjectSpace/MycobacteriumTuberculosisGlmURv1018cDrugTarget). Glmu is a bifunctional protein comprising of an Uridyltransfer domain at the N-terminal and an acetyltransfer domain towards the C-terminal. The interesting aspect is that the C-terminal of this protein is intrinsically disordered i.e, in native form this region doesn't adopt a rigid secondary structure, but has the ability to attain such structure once interacts with a suitable biomolecule (protein,DNA or small molecule). Recently structure of this particular protein has been solved, with the limitation that the intrinsically disordered tail is missing out. Exploring and exploiting this region might add new dimensions to our understanding of both - intrinsically disordered region as well anti-microbial targets which tend to remain disordered.
For more information please visit : http://www.rsc.org/publishing/journals/MB/article.asp?doi=b905518p
or email me.
Opinions/criticism/comments are invited.

Swine Flu protein - Neuraminidase

The Developed nations have recently been under threat of Swine Flu which is caused by virus H1N1. Various sequencing projects were triggered as per sensitivity of the problem. NCBI provides a collated form of sequence data from various labs. An article which appeared in "Medical news today" highlights the fast paced breakthroughs that can be achieved using bioinformtics. Here is an excerpt from the article :

In the Biology Direct journal's May 20th issue, Sebastian Maurer-Stroh, Ph.D., and his team of scientists at the Bioinformatics Institute (BII), one of the research institutes at Singapore's Biopolis, also demonstrated the use of a computational 3-dimensional (3D) structural model of the protein, neuraminidase.

"Because we were working as a team, driven by the common goal to understand potential risks from this new virus, our group at BII was able to successfully complete this difficult analysis within such a short time," said Dr. Maurer-Stroh, BII principal investigator and first author of the paper.

BII's interactive 3D model is available at the following link: http://mendel.bii.a-star.edu.sg/SEQUENCES/H1N1/

With the 3D model, Dr. Maurer-Stroh and his team were able to map the regions of the protein that have mutated and determine whether drugs and vaccines that target specific areas of the protein were effective.

Among their findings:

* neuraminidase structure of the 2009 H1N1 influenza A virus has undergone extensive surface mutations compared to closely related strains such as the H5N1 avian flu virus or other H1N1 strains including the 1918 Spanish flu;

* neuraminidase of the 2009 H1N1 influenza A virus strain is more similar to the H5N1 avian flu than to the historic 1918 H1N1 strain (Spanish flu);

* current mutations of the virus have rendered previous flu vaccinations directed against neuraminidase less effective; and

* commercial drugs, namely Tamiflu® and Relenza®, are still effective in treating the current H1N1 virus.

With the Biology Direct journal paper, the Singapore scientists have become the first to demonstrate how bioinformatics and computational biology can contribute towards managing the H1N1 influenza A virus.

Russ Altman's View on whether Bioinformatics & Computational biology are same or not !

I spent the first 15 years of my professional life unwilling to recognize a difference between bioinformatics and computational biology. It was not because I didn’t think that there was or could be a difference, but because I thought the difference was not significant. I have changed my position on this. I now believe that they are quite different and worth distinguishing. For me,

Computational biology = the study of biology using computational techniques. The goal is to learn new biology, knowledge about living sytems. It is about science.

Bioinformatics = the creation of tools (algorithms, databases) that solve problems. The goal is to build useful tools that work on biological data. It is about engineering.

All this became important to me when I finally joined a bioengineering department, and I was forced to ask myself if I was a scientist or an engineer. I am both, and now am at peace.

When I build a method (usually as software, and with my staff, students, post-docs–I never unfortunately do it myself anymore), I am engaging in an engineering activity: I design it to have certain performance characteristics, I build it using best engineering practices, I validate that it performs as I intended, and I create it to solve not just a single problem, but a class of similar problems that all should be solvable with the software. I then write papers about the method, and these are engineering papers. This is bioinformatics.

When I use my method (or those of others) to answer a biological question, I am doing science. I am learning new biology. The criteria for success has little to do with the computational tools that I use, and is all about whether the new biology is true and has been validated appropriately and to the standards of evidence expected among the biological community. The papers that result report new biological knowledge and are science papers. This is computational biology.

As I look at my published work I have always tried to balance the publications in biological/medical journals and those in engineering/informatics journals. It is an aesthetic really, there is no reason why one should feel compelled to do this. However, it is useful to know when you are doing biology and when you are doing something else. I suppose someone can argue with the my use of the term “bioinformatics” as an engineering discipline. That’s fine–I’m open to a different term. But I would ask why bioinformatics isn’t good. I think computational biology is more solid–the ‘biology’ is clearly the noun and the ‘computational’ is clearly the adjective.


Russ Altman's blog : http://rbaltman.wordpress.com/