Does high-throughput synthetic practices have failed the drug discovery efforts by steering them toward greater unsaturation leading to more flat aromatic compounds those may not be better complement to the target proteins? Yes at least that's what Frank Lovering and others are suggesting. In a recent article published in Journal of Medicinal Chemistry Lovering et. al highlight lack of molecular complexity as key limitation of high-throughput parallel synthesis driven drug discovery efforts. In last 10 years due to ease of coupling reactions of flat aromatic moieties high-throughput parallel synthesis efforts have become quite popular contributing a lot to drug discovery efforts but they have also biased efforts at the bench side. By analyzing more than two million compounds retrieved from the GVK BIO database, authors suggest that the the molecular complexity and the presence of chiral centers are key determinants of success in the transition from discovery, through clinical testing, to approved drugs which means that current discovery efforts need to be directed towards diversity-oriented synthesis to produce architecturally complex candidate drug molecules. Molecules with higher complexity and the presence of stereo centers are more likely to succeed in these transitions. The authors chose two simple and interpretable measures of the molecular complexity: (i) carbon bond saturation as defined by fraction sp³ (Fsp³) where Fsp³ = (number of sp³ hybridized carbons/total carbon count) and (ii) existence of chiral centers. Use of saturation as a key descriptor for complexity means increase in complexity without increasing molecular weight significantly. Study also shows that saturation correlates well with solubility. Both molecular weight and solubility are key factor for drug-likeness noted in the Lipinski’s "Rule of 5". Current study addresses the role of molecular complexity and its connection to "Rule of 5". Findings will help to design molecules quite similar to natural products with better bioactive by exploring the complex areas of chemical space.

Notion of pre-competitive collaboration has been in under experiment steadily for quite sometime now. Notable examples are the Airbus consortium of European aircraft manufacturers, the Sematech consortium of US semiconductor manufacturers, banks working together to launch Visa and Mastercard, our recent moon lust and many more. But this was never a case for pharmaceutical industry until now which is now lowering industry firewalls to shift funding and focus from early- to late-stage projects by developing cooperation in the areas with little potential for differentiation most notably a shared informatics infrastructure through public–private partnerships. Pre-competitive collaboration in this process means that everyone will have same common pool of data and resources. Competition will be still there but for better ideas, for better models and to discover first.

Pre-competitive informatics initiatives
A very interesting opinion piece appeared in September issue of Nature Reviews Drug Discovery discussing the importance of pre-competitive informatics initiatives in drug discovery. Article suggest that many companies are already beginning to embrace this idea, and that for some companies

the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain.

This was a very timely review in the wake of several initiatives such a Innovative Medicines Initiative (IMI), EBI industry programme, Pistoia Alliance and many others. The idea of lowering industry firewalls caught more attention after the announcement of Sage Bionetworks, a non-profit medical research organization established this year with initiatives of Merck duo Eric Schadt and Stephen Friend. A similar kind of effort Open Source Drug Discovery(OSDD) was launched by CSIR, India earlier this year with a initial investment of US $38 million. OSDD consortium is trying to implement open source model for Drug Discovery and public-private partnership is one of the major focus of this initiative. Exciting isn't it? But wait there is a twist in story, there are no definitive answers for what type of data is pre-competitive and what is not? The definition of of pre-competitive is fluidic and it depends on several factors, one of them is whether data belongs to biology or chemistry. Article suggests that any data and tools used by biologists should be under consideration for pre-competitive sharing but those used by chemist should remain the competitive or proprietary (which is very much according to current trends). I could not find any rational reason behind this argument except the fact that there is overwhelming amount of public data in biology domain and day by day companies and institution are finding it hard to manage, integrate and use them for drug discovery. I will go further and suggest that much of these initiatives serves no benefits unless otherwise the data and tools belonging to chemistry domain is also considered as pre-competitive. Ironically much of the data and tools released by pharmaceutical companies under these initiatives are yet to proof their importance. For instance much hyped Life Science Grid released by Eli Lilly (which went open source in year 2008) failed to attract even an average user base. Lilly released only the biology side of the grid which includes a selected group of non proprietary plug-ins, including those for Gene Browser, NCBI Entrez, and Gene Ontology. Forgive me but there are already better tools for the biology in public domain. In my opinion unhindered access to data and tool is prerequisite for the success of the pre-competitive landscape which require more active contributions from the industry participants. Currently the systems is evolving and for now something is better than nothing.
Apart from the issues related to definition of pre-competitive boundaries there are several other bottlenecks, for instance the who will fund the long-term maintainability of such an infrastructure, those remain unresolved .

Universal application of high throughput omics technologies have enabled scientists to measure tens of thousands of data points in a single experiment. As a result of this scientific world has become deluged with data. This has greater implications the way science will be done in coming years. There is a general accord that science has turned more into a data management problem. Put the technical aspect of scientific data management aside, and ask can we depict useful and practically relevant conclusions from our past experiences in scientific data management, and what makes few data management strategies/efforts more popular than others? I am talking about appropriate philosophical foundations for scientific data and knowledge management. Although there is no universal agreement what is best way to manage heterogeneous scientific data which keeps evolving over the time, simplicity and abstraction have always appealed data pundits. Generally most of the scientific data management strategies have not conceived from the social perspective, they are always technology driven. You may call it by any name Big Data, Open Data, Linked Data or you can be in love with XML or RDF, but I am not impressed there will be any ultimate solution to this problem. So is there any social perspective for scientific data management at all? Well I guess so. Masanori Arita has recently published an interesting review article about what can metabolomics learn from genomics and proteomics? What I really liked about this article is the Masanori's analogy of omics data management with Gladwell states. Many of us will be aware about the Malcolm Gladwell highly praised book The Tipping Point: How Little Things Can Make a Big Difference, where based on sociological observation Gladwell describes the three rules of social epidemics: the law of the few, the stickiness factor, the power of context. Tipping points are nothing but the critical point at which the momentum for change becomes unstoppable or viral. From omics data management perspective lets just consider the first and foremost important, the law of the few which states that a small number of influential people called mavens (information specialists), salesmen (persuaders, charismatic), and connectors (truly extraordinary knack for connecting with people) play a crucial role in staging the tipping point. What Masanori really wanted to stage was the viral success for the metabolomics as a fundamental data-driven science like its counterparts, genomics and proteomics. But there is big massage for every other omics, Masanori draw three simple conclusions,

1. Mavens: large public databases with focus on information quantity
2. Salesmen: data appeal by simple formats/standards
3. Connectors: wiki-based community/knowledge portals
   

First point is large public databases with early focus on information quantity. Data must be readily and freely accessible through large, stable public repositories such as Genebank, SwissProts. Information quantity is no longer an issue in genomics and proteomics community, although they are suffering from several other issues such as high proportion of unannotated data (Out of 4000 bacterial genome projects only a handful are publicly well annotated) , high error rate (20 to 45% for high-throughput protein–protein interaction data) and low information content. On a rather small note stability need long term funding and I guess most of large public repositories are quite comfortable with that.
Second point is data appeal by simple formats/standards. Data format/standards played a important role in success and popularity of certain research areas. Masanori notes that

In biology, the readability of raw data affects popularity. In fact, metabolism, the primary research topic in metabolomics, is notorious for its incomprehensibility and many researchers stayed away from metabolic networks containing lengthy structural and stoichiometric information. The KEGG database gained popularity for its oversimplified representation of metabolic networks: each metabolite is represented as a node without structure, and each reaction as a binary relationship without stoichiometry. Although its oversimplification resulted in considerable misunderstandings , the KEGG database boosted the graph-oriented analysis of metabolic pathways, and consequently, it awakened the interest of the research community in metabolism. Many popular databases containing gene expression or protein–protein interaction data also use simple notations.

Third point is use of wikis as major platform for hosting the biological information. As matter of fact major biology databases are in the process of transferring to wiki-based sites and use of wiki as sites is getting momenta. Further

We, as scientists, should pay more attention to the evolution of web information because wiki embodies the quintessence of all sciences: the acquisition of knowledge through open discussion.

Openness is not only reason in favor of wiki over traditional databases. Situation is quite complicated for curated and annotated non-wiki databases where evolution of data remains intractable. Whether there was any updated in data base, and if yes why it was updated and was it discussed in appropriated forum before update, these issues remains gray area for non-wiki type sites. For instance, take the example of BioModels database which expose the systems biology models as releases, in each release there are few new models but it also includes old models which may or may not be modified after the previous release. In current BioModels implementation there is no clear mechanism to track the changes related to evolution of a given model. From a user perspective tracking of revisions and edits is really important. The other issue is whether or not curation projects have a backup mechanism in place. I am raising this issues because funding sources for biological databases are quite limited, which means sooner or later few or many projects will be out of fund (A recent example of this is the arabidopsis resource, TAIR). Rather than asking to funding agencies for sustainable model for biological databases funding, I would suggest that project manager should be asked to include the additional details in their project proposal such as how they will keep the data stream alive if funding run out at first place. I think there are several options to keep data stream alive for short lived projects, just dump the whole database in sourceforge or any other repository. Best option is make Wikipedia your new home. In all fairness I am not against long term funding of the database projects, but this should be a goal oriented merit based decision and even then very few will be succeed.
Not everything is well with wiki option also, like absence of incentives for participating in crowd sourcing efforts. But there is better chance and hope.

Students from iGEM Harward team are using the optical communication to create a physically distributed lac operon between a bacteria and an yeast cell which normally occur within the same cell. Idea is to use the principles of synthetic biology to decouple the single cell lac operon events such as de-repression and transcription into two different cells in order to create a spatially separation between these events. In this new system bacteria send optical signal to yeast that the operon has been de-repressed and in response the yeast complete the operon’s function and express beta-galactosidase.

In summary,

In this system, bacteria to communicate to yeast the presence of IPTG, which results in transcription of lacZ in the yeast cells. To permit bacteria to send an optical signal, we expressed in E. coli a red firefly luciferase under IPTG induction. To allow yeast to receive the signal, we used a two-hybrid-system based on the interaction between the red-light-sensitive Arabidopsis thaliana phytochrome PhyB and its interacting factor PIF3. Interaction between PhyB and PIF3 is induced by the red light from the bacteria, resulting in transcription of the lacZ gene.

Although optical communication between higher multicellular organisms is very common what you seeing here is the an optical communication system between a prokaryote and a eukaryote. By spatial seperation of the control and production units coupled with complementary features of bacteria and yeast makes this kind of setup more useful for the industrial applications such as large scale production of enzymes.

Mackenzie Cowell is one of the founders of DIYbio.org, an organization that aims to help make biology a worthwhile pursuit for citizen scientists, amateur biologists, and DIY biological engineers who value openness and safety. Mackenzie was recently interviewed by MAKE magazine for an ongoing series of video interviews with notable working scientists and technologists. These interviews were recorded at SciFoo, an un-conference on Science and Technology organized by O'Reilly Media along with Nature Publishing Group and Google. Mac is fascinated about emerging discipline of synthetic biology,

In the future there is a really neat opportunity for open source science to be driven by amateurs.

They Might Be Giants' album, Here Comes Science, is collection of thought provoking audio/video content for kids. A musical melody which covers a broad range of topics in science starting from why science is real and why it is so important? Here are two videos from the collection:

Science is real, the kick-off song which which explains how scientific beliefs formed with the process of rigorous testing.

Meet the Elements, an animated upbeat ode of elements and how they form our world.

"The Story of Synthia" explains -- in a cartoon -- how some scientists are attempting to create synthetic "life." By replacing the genome of a natural microbe with a human-made genome constructed from synthetic DNA, they hope to give birth to a new, synthetic species -- mycloplasma laboratorium.

Forget about the Internet scale, seriously that is not enough for Google. Google engineers are now talking about future scale which means company is preparing to manage as many as 10 million servers in the future. Google fellow Jeff Dean describes Google's Future scale as,

~10⁶ to 10⁷ machines, ~10¹³ directories,~10¹⁸ bytes of storage, spread at 100s to 1000s of locations around the world, ~10⁹ client machines

Woo! Jeff don't stop here, he further presents Google's MapReduce usage statistics over time, check out the incredible numbers of input data read (500+ petabyte) .

As I mentioned in my previous posts that for next few days we are going to cover various interesting activities related to iGEM 2009 and selected student projects will be featured on the Fisheye Perspective blog. Next in our list is project SynBioWave, the Google Wave extension for synthetic biology developed by a team of students from Albert Ludwigs University Freiburg. I must accept that team has done excellent job with wave and it is one of the best wave extensions for scientists out there (see a list of wave applications for the scientist at the end of the article). Concept is to develop a Google wave based collaborative environment for synthetic biology which will enable multiple distributed users to analyze and construct genetic parts in real time. Apart from Google Wave environment SynBioWave is using two other technologies: BioJava and Qooxdoo. In following image you can see the whole architecture of the SynBioWave.
In order to create a basic wave functionality for the different molecular biology tasks such as cloning, team is using BioJava library. BioJava enables the processing of biological data such as sequences and 3D structures, while Qooxdoo is used to to extend Wave's graphical user interfaces (GUI) with custom tool bars, buttons, forms, context-menus for SynBioWave application. Team has also introduced a qooxWave protocol for creating custom client side GUI inside a wave from a server side robot.
Sequence manipulation from Google Wave
So what type things you can do with SynBioWave? Development of SynBioWave is currently in early stage, but still you can to several things such as visualizing your sequence data, compare your sequence data or multiple sequence alignment. SynBioWave supports multiple views for visualize the sequence information in a intuitive way, not to mention the automated sequence coloring feature.

There is a simple view for short sequences typed or copied directly to the wave and a embedded gadget view for longer sequences and sequence comparisons like for example in multiple sequence alignments. Both views providing a clearly represented scaling and increase readability by automatically colorizing the sequences according to the sequence type. And in the end there is a circular view for displaying fully featured circular dna as needed for example in displaying vectors and plasmids.

BLAST from Google Wave
Further there is a BLAST robot which can BLAST the biological sequence information against available resources and further process the received Blast-hits.
and many more
There is another BioBrick robot which integrates different assembly-algorithms and enables the DAS communication with BioBrick database for genetic part import and upload in compliance with BioBrick standards. Further team is developing an Eclipse-plugin for SynBioWave-developers and several other robots.

So where will this lead us? Google Wave development for life scientists is now moving into next level. SynBioWave and other extensions (listed in the end) are making their way for the next generation of scientific engagement and collaboration.


List of wave applications for the scientists

If synthetic biology fulfills its promise it has potential to replace the world created by Darwinian evolution with one created by us. At least that's what Michael Specter's recent story published in September 28th issue of The New Yorker suggests. In last few months the synthetic biology has drawn increased media attention, and as a matter of fact the The New Yorker story is one of the most comprehensive collections about this very nascent field. Despite several major breakthroughs in last few years the public awareness about the synthetic biology is very poor. A recent groundbreaking survey of 1,001 U.S. adults conducted by Peter D. Hart Research Associates and the Project on Emerging Nanotechnologies has found that only 22 percent of Americans have heard of synthetic biology.
A further digging of the survey report hints that regardless of their awareness of synthetic biology, a strong majority of adults think that the risks will outweigh the benefits especially individuals who regularly attend religious services. I can also understand the feelings of those who believe in god because if synthetic biology truly succeeds everyone of us will be their own Darwins. Critics of synthetic biology generally raise the concern that scientist might be playing with God when they create new life forms, that's where public awareness really matters. If the public does not realize mankind can use synthetic biology to make new drugs or renewable energy, it will look like we are playing with god. No doubt there are serious concerns especially possibilities for deliberate abuse like bioterrorism risk which many suggest as extremely unlikely scenario. However to accomplish a dream of synthetic reality, we will also need an education system such as International Genetically Engineered Machine (iGEM) competition, an undergraduate synthetic biology contest run by the MIT, that will encourage skepticism and the study of science. What really worries me is lack of participation from New Zealand, like previous years this year again there is no team from New Zealand. Although last year two members of Auckland Bioengineering Institute, Dr Michael Cooling and James Lawson, were the mentors of gold medal winning team of the Newcastle University. CellML technology developed at the Auckland Bioengineering Institute played a major role in their endeavor which also suggests we have sufficient core expertise to participate and win iGEM contests. Yesterday I was surprised to learn that 12 year olds Indu Voruganti, Will Allen and 11 year olds Ahmad Rana, Ashley Kim had developed an innovative synthetic biological approach to cure the nasal allergy. I am sure we have equally talented undergrads, all they need is more awareness with a little guidance. This year 2 teams from Australia are participating in iGEM and we should hope to send at least one NZ team for next year's iGEM.

PS: Drop me an email or a comment if you might be interested to form a NZ team. I have few brainstorming ideas to start with!!!!

The International Genetically Engineered Machine (iGEM) 2009 Jamboree dates are coming closer and for next few days we are going to look around the wikis to learn more about what different teams are doing this year. iGEM 2009 is bigger than ever, in fact more than 110 teams competing this year's Jamboree falls on Halloween (October 31) weekend. The 2009 Brown iGEM team is taking an innovative synthetic biological approach to create self-regulating drug factory in the nose to treat the allergic rhinitis which is the most common type of nasal allergy with symptoms such as nasal congestion, itching, burning, sneezing, etc . Team has engineered a new effective strain of Staphylococcus epidermidis that can cure allergy without any side effect.

iGEM team worked to treat allergic rhinitis by engineering Staphylococcus epidermidis, a microbe endogenous to the human nasal flora, to secrete a recombinant histamine-binding protein in response to the elevated histamine concentrations of an allergic response. The engineered strain of S. epidermidis will function as a self-regulating drug factory in the nose, providing relief, without any negative side effects.

Yesterday Wellcome Trust announced the winners of the tenth Wellcome Image Awards, featuring 19 stunning images. These images are simply incredible, many of them are technically very difficult to achieve and require specific coloring skills. I have just selected a few remarkable images from the slide show. Click here for the full slide show of images along with different background information.

Compact bone

These circular structures are regions of compact bone from a human femur. Compact bone forms a hard outer shell around the spongy bone that makes up the marrow space in the centre.

This image is naturally striking. No colour has been added to the specimen, yet the vascular canals almost appear as though they are bleeding

In vitro fertilisation

This image shows sperm and an egg (or ovum) at the moment of conception by in vitro fertilisation (IVF). The egg is surrounded by protective cumulus cells around the outside surface, coloured yellow. The sperm need to penetrate the membrane surrounding the egg, called the zona pellucida, if successful fertilisation is to occur. Light micrograph.

Although this news is already hot in air I could not resist to post it again. According to reports Finland is the first country to make broadband access a legal right.

Starting next July, every person in Finland will have the right to a one-megabit broadband connection, says the Ministry of Transport and Communications. Finland is the world's first country to create laws guaranteeing broadband access.
The government had already decided to make a 100 Mb broadband connection a legal right by the end of 2015. On Wednesday, the Ministry announced the new goal as an intermediary step.

Can we expect something like this from John Key administration? Seriously speaking current broadband infrastructure in New Zealand really sucks, may be pigeon can be faster than broadband.