Specifically, Grecco et al. study the flow of information in the form of phosphate groups, a staple information tag that cells attach to their proteins to switch them on , off or give them special properties. Phosphate groups are attached to proteins by enzymes called Kinases, and removed by enzymes called Phosphatases. The fine balance between the kinase and phosphatase activities determines which proteins are phosphorylated and remain so for varying lengths of time.  A lot is known about which proteins get phosphorylated under what circumstances from tedious experiments over the last years that involve extracting the protoplasm into jumbled soup and then going about detecting how much phosphate is attached to which proteins. By replacing this step with a clever optical measurement, the authors opened the door to studying these interactions in living cells. Because the method is optical, and essentially works on a microscope (albeit not a very conventional one), the authors can not only tell which proteins get phosphorylated in response to a particular stimulus , but also where exactly in the cell these proteins are located.

To extract this information, the authors create a library of proteins containing phosphorylatable Tyrosine residues* , each tagged with one of the now famed fluorescent proteins. They adapted a technique called ‘reverse transfection’ to create a array of tiny spots of cells (a cellular array) , each expressing a different fluorescently-tagged protein. Then they added a stimulus – Epidermal Growth Factor, or EGF in short – an extracellular signal that sets off cellular signaling eventually leading to growth of a tissue. Then they added an antibody that would specifically recognize phosphorylated Tyrosines. The antibody itself was tagged with a particular fluorescent molecule. What they ended up with is cells, glowing with a fluorescent protein they had made it express, and an antibody that attached to this protein if its tyrosine were phosphorylated.

Now came the optical part. How does one find out if the antibody had bound to a protein? One of the most sensitive ways of detecting binding is to use a phenomenon called Foerster/Fluorescence Resonance Energy Transfer (FRET). Fluorescent molecules absorb light of a particular color, and electrons in them become ‘excited’, storing the energy for a brief instant. Eventually though, the electron gives off this energy as light again, but because some of the energy has been lost (no process is 100% efficient) , the light it gives is of a different color. Fluorescent molecules thus have an ‘absorption spectrum’ , and an ‘emission spectrum’. What happens , though, if there is another molecule sitting right next to this excited molecule that can absorb at exactly the same color that our excited molecule is about to emit? The laws of quantum physics say that , there is a chance that resonance will occur between these molecules and the excited molecule , instead of emitting light, will give away its energy to this other fluorescent molecule, that is perfectly suited to taking up this energy. This transfer can occur however , over only a very short distance , the so called Foerster radius – such that if it does occur, in almost all cases, it means that the two molecules are less than 20 angstroms from each other. In a solution of molecules, this is only likely to happen in any significant degree if the molecules are really bound to each other. In practice, it means the following, imagine a fluorophore that absorbs blue light and gives off green light. Another absorbs green light and gives off red light. If these molecules are so close as to be bound to each other, then shining blue light, will give off some green light, but also some red light. Some of the energy of the first fluorophore (the donor )has ‘leaked’ into the second one (the acceptor).

Left : A schematic representation of FRET-FLIM used for detecting Tyrosine Phosphorylation. Right : The high-throughput microcopy setup for measuring FLIM in biological samples.

FRET has been measured usually as the brightness of the signal. If energy is leaking between fluorophores, than the acceptor is brighter than it should be, and correspondingly the donor is dimmer than it should be. This method is not very precise in finding out how many of the donor-acceptor molecules are bound, though, since there are always unbound donors that confuse the signal. The authors use a different method to quantify FRET – they measure the average amount of time the donor fluorophores stay in their “excited” state – the fluorescence lifetime. If there is an acceptor bound and energy can leak, the donor molecules should remain in their excited state for a much shorter time – in other words their fluorescence lifetime will reduce. When performed on a microscope, the method is called Fluorescence Lifetime Imaging Microscopy (FLIM). The most useful part is that the average fluorescence lifetime reduces precisely in proportion to the amount of donors bound to acceptors, and FLIM can therefore tell us how much of a protein is phosporylated and thus bound to an antibody with an acceptor fluorophore.

The authors thus combined a high-throughput screening approach, with a sensitive optical method to detect protein phosphorylation, creating Cell Array – FLIM (CA-FLIM). They now had a way to see which of many proteins were phosphorylated, to what extent, and where in the cell, at the level of individual cells, when they added a stimulant. This is a level of detail in information that has been achieved in very few instances in biology. As with all cases of a data avalanche, however, the authors quickly found that biological variability was showing its effects – not all cells responded in the same way, and conventional methods of handling the data were incapable of resolving real phenomenon from the all-pervasive effects of this variability. So they had to develop a new way of analyzing this information, grouping cells into clusters based on how much of which protein was being phosphorylated. The mathematics of this new kind of ‘global analysis’ itself forms a significant proportion of the paper.

Finally, the authors compared their results with those obtained from the study of individual proteins done with tedious conventional methods. They found their data agreed with what other scientists had painstakingly discovered over years. Of course, CA-FLIM had revealed information of those proteins and many more in a matter of hours! What CA-FLIM added to the mix in many cases is information about the spatial aspects of this phosphorylation of proteins.

One point of critique here is that in order to detect the the phosphorylation of any protein, that protein needs to be expressed in its fluorescently tagged form. While this ectopic expression is relatively easy to do, it adds an extra population of that particular protein to what the cell itself is creating from its genome, known as the endogenous protein level. Since, the endogenous protein is not fluorescent, what happens to it remains in the dark. In all studies involving ectopic expression, the implicit assumption is that the fluorescent population behaves similarly to endogenous the non-fluorescent one , and all modifications occur to the same extent on both molecules. While this is more or less an reasonable assumption for many proteins , there is no way to be absolutely sure. Indeed, the assumption is in no way ironclad and several exceptions are known. In certain model organisms, such as yeast, it is possible to get around this problem by replacing the organism’s genes with fluorescently tagged versions of those same genes, but in mammalian cells, this is not yet technically possible.

CA-FLIM now adds to the growing list of methods that reduces the time required to detect cellular protein interactions by an order of magnitude. As technical challenges in implementation and automation are overcome, and CA-FLIM is expanded to other stimuli beyond the EGF used in this study, the multi-pronged nature of this approach should provide insights, or at least shine the light on interesting cellular phenomenon – increasing our knowledge of the basic unit of life further.

*Proteins can be phosphorylated on several amino acid residues , most commonly on Serine, Threonine and Tyrosine. Tyrosine phosphorylation seems more significant in the first steps of intracellular signaling and the authors focused on this particular kind.

NOTE : Nachiket Vartak , the author of this post , did not contribute to the study and development of CA-FLIM, but is affiliated to the same institution where the work was done. The author declares no conflict of interest.

Editor's Selection IconGrecco, H., Roda-Navarro, P., Girod, A., Hou, J., Frahm, T., Truxius, D., Pepperkok, R., Squire, A., & Bastiaens, P. (2010). In situ analysis of tyrosine phosphorylation networks by FLIM on cell arrays Nature Methods, 7 (6), 467-472 DOI: 10.1038/nmeth.1458

                          

Adding a little bit of spicy strangeness along those lines, is the phenomenon of ‘convergent evolution’. This is one of those not-so-rare cases when species that aren’t very similar or closely related , turn out to have a feature that is strikingly, almost awe-inspiringly, similar. Wikipedia has a pithy list of examples of convergent evolution, my favorite one being the case of the human eye, and the Octopus eye. See how similar* they look, even though Cephalopods(them) and Vertebrates(us) diverged in evolution millions of years ago, before any eyes of similar nature were invented by the ubiquitous tinkering of evolution.

There are many explanations for why convergent evolution might occur, but they are circumstantial at best, speculative at worst and usually not very convincing. If you are a biologist, you will know that finding out the truth in such cases is intensive enough to just suffice ourselves with Orgel’s Second rule “Evolution is cleverer than you are”, and leave it at that.

But it’s starting to turn out, that while evolution may be a clever tinkerer, there aren't too many ways to skin a cat. A couple of papers published in the last few weeks show that while evolution may create similar characteristics in unrelated organisms, it tends to use the same mechanism at the molecular level to do so. Chan et al report in Science, that sticklebacks in different isolated populations tend to lose a feature of their anatomy: the pelvic girdle, by the same molecular event – the loss of a genetic regulatory element called the Pitx1 enhancer. Now you may say that these are the same species, and not nearly as spectacular as the Octopus-Human comparison, but remember that these populations do not interbreed, and not all populations have sticklebacks lose their pelvic girdle. It is pretty astounding that three populations who lose their pelvic girdle do so in the same manner – by eliminating Pitx1.

More exciting was a paper published in PloS Genetics by Counterman et al. , who examine the genes that produce the color in Heliconius butterflies. In contrast to the earlier paper, Counterman et al. study populations of a species of butterfly called the Small Postman (H.erato), that is a Muellerian mimic of its poisonous relative , the Postman butterfly (H.melpomene). The idea is that birds know that the Postman butterfly is poisonous , and will not eat it. H.erato will try to mimic the appearance of the its toxic cousin, so that birds leave it alone in a case of mistaken identity.  Both H.erato and H.melpomene are poisonous, and have independently evolved similar appearances despite having diverged millions of years ago. The mimicry is a huge benefit to both species, since it ensures that predators have one thing firmly entrenched in their collective memory – if it looks like  Postman butterfly, do not eat it .

[The author of the paper has educated me about the nature of interaction between these two species. See comments for details].

                 Heliconicus melponeme                            Heliconicus erato

Of course, the various populations of H.erato are not all equal, some seem to be better make-up artists and look much more like their miasmatic relative. Counterman et al. perform a population genetic analysis of these, and find, to their surprise that all of them show a dependence on only one site (locus) in the genome of these butterflies.

By doing more intensive analysis on this locus, they are able to determine that in fact, it is only one gene – kinesin, that seems to make most of the difference between a good mimic , and a bad one. Once more, the appearance and nature of a rather complex trait, is shown to be linked to a rather small region of DNA. If you want to look like the Postman butterfly it seems, you need to mess with the Kinesin gene.

In the light of these findings, a hint of a molecular explanation for convergent evolution begins to emerge. As the authors of either paper suggest, there may be evolutionary hotspots in our genome for a given trait. Whenever natural selection pressures the species into changing the trait, it is this genomic hotspot that feels the brunt. At the molecular level, evolution seems to be much more constrained than the medley of phenotypic outcomes lead us to believe.

I wouldn’t be surprised if one day we found that the developmental genes or regulatory programs that make an octopus eye and a human eye are more akin than their tentacles and our hairy heads seem to suggest.

*Note : It is interesting that despite the compelling similarity, the eyes are not completely identical. The retina of the Octopus eye actually has the reverse order of cell layers , when compared to the Human eye. This actually makes the Octopus eye somewhat better, since its optic nerve does not have to penetrate the retina – thus, an Octopus eye has no blind spot! Morever, because its photoreceptors have no obstrcutive cell layer (unlike us), it probably has a higher sensitivity to light as well.

References

Chan, Y., Marks, M., Jones, F., Villarreal, G., Shapiro, M., Brady, S., Southwick, A., Absher, D., Grimwood, J., Schmutz, J., Myers, R., Petrov, D., Jonsson, B., Schluter, D., Bell, M., & Kingsley, D. (2009). Adaptive Evolution of Pelvic Reduction in Sticklebacks by Recurrent Deletion of a Pitx1 Enhancer Science, 327 (5963), 302-305 DOI: 10.1126/science.1182213

Counterman, B., Araujo-Perez, F., Hines, H., Baxter, S., Morrison, C., Lindstrom, D., Papa, R., Ferguson, L., Joron, M., ffrench-Constant, R., Smith, C., Nielsen, D., Chen, R., Jiggins, C., Reed, R., Halder, G., Mallet, J., & McMillan, W. (2010). Genomic Hotspots for Adaptation: The Population Genetics of Müllerian Mimicry in Heliconius erato PLoS Genetics, 6 (2) DOI: 10.1371/journal.pgen.1000796

                          

Sean Caroll proposes that science itself is an ‘empirical behavior practiced by humans’ . Of course, this means that science is plagued by at best disagreements within the community , and at worst , agitated controversies like the ‘religion’ issue.

In my personal opinion, the problem lies in the way science is projected to the public – it is projected like it is mathematics, like a conjoint twin of the blue-blooded mathematics, but destined to ‘get its hands dirty’. But then , consider this quip from (the) Einstein :

“As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.”

                                                                                                      Albert Einstein, "Geometry and Experience", January 27, 1921

You can imagine that this becomes a major peeve for science, which claims to be in the business of studying and revealing the workings of reality. I do understand that very important role that mathematics plays in making science tangible – but mathematics is a player of the abstract – numbers are ideas that are arranged in equations to reflect reality. In effect , calculus and arithmetic based mathematics is the ultimate simulation of reality.

Science , therefore, uses mathematics only as a tool. Yet, all too often it is dismissed by the populace as a dry, dispassionate subject- exacting, never completely applicable to reality, and working as it should only on a Professor’s blackboard. This view, whether valid or not, is more applicable to mathematics, and science suffers under the transferred epithet.

Science, I conclude then,  needs a new metaphor, and the only part of mathematics that lends an appropriate one is Probability theory.

Bear with me for a moment, and assume that reality (or more appropriately , reality as we understand it) is a collection of probability distributions on increasing orders of complexity and scale. Quite obviously, the most basic of these distributions are those of quantum mechanics – the Schroedinger equation and so on. At the other end of the scale , are the observed cosmic probability distributions – such as the chance of star formation, supernovae, spatial inflation (given the WMAP data) etc. Everything else, from ecology, sociology to music lies in between.

The job of science is to fill up the actual values that determine these distributions. It is apparent in this picture that nothing is impossible or possible – just very likely or unlikely. It is now left to the informed public to decide whether they are going to place their bets on what is a likely or something that has almost no chance of occurring.

This mental model of science provides two major advantages :

Firstly, it makes science overtly non-judgmental – as it should be. It doesn’t say walking on water , virgin births and resurrection from the dead is impossible – just something that isn’t very likely. In fact, it makes it just about as unlikely as thin air transforming into gold bullion. I said overtly earlier because in fact, it lumps together all the ‘unlikeliest , thereby accentuating the ridiculousness of belief in them. If you think walking on water is possible, then you must also believe that fish have developed civilization and are now (put your favorite mockery here) simply taking a break this millennium. Science as a probability function-finder is a way of enabling Stephen Jay Gould’s Magisteria, without causing science to lose it ‘power’ to say something about the world.

Secondly, it makes science more versatile and in many ways, a better representation of reality. Most theories in science, especially in biology, geology and cosmology provide ball-park figures, and rarely the precision of numbers available in physics. Considering a probabilistic spread of values provides a much better assessment of the theory’s power for explaining reality. Of course, even today science does use this premise, especially when applying statistical analysis. Statistics is a useful discipline and yet scientists sometimes think as of statistics is some sort of compromise.

“If your experiment needs statistics, you ought to have done a better experiment.”

                                                                                            Ernest Rutherford

The reason, I think, is that scientists have let the mathematicians mislead them with the false glorification of a single , elegant equation that explains everything. Murray Gell-Mann even proposes that the elegant beauty of an equation is in some ways an indication of its correctness. That may be so, but from what we have learnt from every other field of science, is that at least such elegance is NOT scalable. Any set of rules that describes a living cell with its ~100,000 thousand or so different types of molecules does not exhibit the elegance of Maxwell’s equations , or general relativity. It possesses a different kind of beauty – the chaotic charm of a pulsating ménage, a Rube Goldberg machine that has found a purpose – to ensure its existence using the the very thermodynamic laws that is fighting to stay one step ahead of.

Beauty at varied scales Left : Maxwell’s equations – a complete description of all electromagnetic phenomenon Right : a genomic map of the simplest known organism Mycobacterium genitalium, indicating its various functional genetic elements.

With that in mind, in an era when science branches out into unknown territory as a matter of routine, there is a chance to alter its image in the public eye.  It begins with scientists giving up the glorification of elegance, and acceptance of the messy uncertainties whose existence is the only certainty in the complex, dynamic world we find ourselves in.

Perhaps then, and ironically so, by relaxing its own expectations of the precision of reality, and by distancing itself in ideology from its quantitative associate, science will befriend the masses and find the credibility it has lacked throughout history.

                          

It seemed perplexing – science today is collaborative, everything depends on communication and sharing ideas , it seems perfect for scientists to share information fast, on a on on one basis if required or tweet to the community. Don’t get me wrong there are plenty of tech-twitter services that discuss the latest gadgetry to hit the markets, but none that discuss the fundamental subjects – the quartet now known as STEM – Science Technology Engineering and Mathematics. So I pondered on the reasons, using my own department as a small study group to try and ascertain why scientists don’t seem to use Twitter, despite the hype.

Here’s a list of reasons I came up with :

1. The Twitter Reputation : Some might say allusion to little bird sounds is cute, hip – but it also gives Twitter the image of something frivolous, not really useful , and part of the MySpace/Facebook category (collectively chided as being a narcissistic phenomenon a.k.a MyFace). Most scientists hold the impression that Twitter is a social network – a place where one wastes time, and real discussions happen only on mailing lists and forums.

2. The Social Activation Barrier : It isn’t just a stereotype. Scientists are substantially asocial – feeling more at home dwelling on the workings of their pet problem rather than interested in what other people are thinking about. Of course, scientists have a tinge of megalomania, and generally will assume that anyone who hasn’t earned credibility with brilliant ideas or research is not really worth listening to. This does not mean they are impolite to lay men, but they rarely take laymen seriously. It is not a reflection of personality , merely a result of the widespread examples of utter silliness among laymen (Intelligent design? ),  poor understanding of how the natural world works on the part of laymen and the stringent standards that scientists are used to apply to themselves and their peers (oh, is my megalomania showing here? ).

3. Email vs. Twitter : Scientists will talk of course to experts in their field , but the preferred method of communication is email. Scientific discussions tend to involve lot of fact-stating and elaboration of theories, and the 140 character limit is simply ill-suited for this purpose. Email is also private, let’s you communicate with people you know are interested and stays as a permanent record. Even if Twitter does some of those things, but it doesn’t top email when it comes to communication. So why change?

4. Privacy (read ‘Secrecy’) : The data and models a scientist generates and the insight they provide is the usually the culmination of a long arduous process. In most cases, it represents years of sacrifices, blood and sweat (of the researcher, metaphorically and his lab rats, literally). As such , the culture of Twitter , which is to openly pass out information is entirely antithetical to the culture of science. Scientists will protect their data from all eyes until it can be represented to the public through a legitimate medium. Only a peer-reviewed journal or a patent is a legitimate medium , by the way, Twitter is not. It is a vicious circle. Twitter is not a legitimate source because of Point 1-3…and Points 1-3 would be rectified if Twitter was considered a legitimate source.

5. Science Media : Speaking of legitimate publication media, the science publishing industry hasn’t quite taken to Twitter. Although some journals (Science , for example) offer podcasts and other “Web 2.0” methods of disbursing information, the core process of publishing science remains tied to the Print/Paper method. All other methods are , in the psychology of a typical scientist simply “Pop-Sci” offerings – the real technical stuff is in the ‘Paper’. Discussions and debates also proceed through a process of writing to the Corresponding Author over email or the Editor of the Journal , who then publishes it and so on. A painfully slow process , I might add, especially in this day and age.

Unfortunately, the above points are also applicable to other “Web 2.0 “ Science resources. Social Networks for scientists (see ResearchGate, Nature Networks) have received only lukewarm reception even when backed by a highly rated research journal – and the few who register are mostly graduate students. Serious scientists still don’t seem to care. I, personally, appreciate the enormous potential of services like Twitter to encourage dialogue between scientists, and to act as a fast information highway. To be fair, there are isolated cases of scientists actually using these resources ( OpenWetWare ). But until the culture of science undergoes a paradigm shift, I am afraid Twitter and its brethren will remain excluded from the scientific community, to be the subject of blog posts by individuals have who taken to it, and to be ignored by the institutions.

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