Tag Archives: evolution

Resource: Second Life Interactive Dendrogram Rezzer (SLIDR)

About half a year ago, I began experimenting with Second Life as a tool for virtual conferences (I should add that my experiences have since improved). However, I believe that imitating real life in a virtual world is not necessarily the best way to use the technology – it may be better to use virtual reality for doing the things that are difficult to do in the real world. A good example of this is Hiro’s Molecule Rezzer, which is one of the best known scientific tools in Second Life. It, and its much improved successor Orac, allows people to easily construct molecular models of small molecules in Second Life.

After speaking with several other researchers in Second Life, who like I are interested in evolution, I set out to build a similar tool for visualization of phylogenetic trees. The result is SLIDR (Second Life Interactive Dendrogram Rezzer), which based on a tree in Newick format constructs a dendrogram object. The first version of SLIDR can handle trees both with and without branch lengths; however, I have not yet implemented support for labels on internal nodes or for bootstrap values.

The picture below shows an example of a dendrogram that was automatically generated by SLIDR based on a Newick tree:

SLIDR closeup

There is a bit more to SLIDR than this, though. After the dendrogram has been built, it can be loaded with a photo and/or a sound for each of the leaf nodes. When click on a node, the corresponding sound will be played and the photo will be shown on the associated screen (the white box in front of which I stand):

SLIDR posing

I plan to work with collaborators in Second Life to construct dendrograms for evolution of bats (including their echolocation sounds and photos of the animals) and for the fully sequenced Drosophila genomes. Please do hesitate to contact me if you would like to use SLIDR on another project. I intend to make SLIDR available as open source software once I have implemented support for the full Newick format.

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Analysis: On the evolution of protein length and phosphorylation sites

It has been much too long since I have last written a blog post. Part of the reason has been that I have been busy moving back to Denmark, starting up a research group, and co-founding a company. More on that in other blog posts. The main reason, however, has been a lack of papers that inspired me to do the simple follow-up analyses that I usually blog about.

This has thankfully changed now. Pedro Beltrao and coworkers recently published an interesting paper in PLoS Biology on the evolution of regulation through protein phosphorylation. The paper presents several interesting analyses and comparisoins of phosphoproteomics data from three yeast species; the abstract summarizes the findings better than I can do:

Evolution of Phosphoregulation: Comparison of Phosphorylation Patterns across Yeast Species
The extent by which different cellular components generate phenotypic diversity is an ongoing debate in evolutionary biology that is yet to be addressed by quantitative comparative studies. We conducted an in vivo mass-spectrometry study of the phosphoproteomes of three yeast species (Saccharomyces cerevisiae, Candida albicans, and Schizosaccharomyces pombe) in order to quantify the evolutionary rate of change of phosphorylation. We estimate that kinase–substrate interactions change, at most, two orders of magnitude more slowly than transcription factor (TF)–promoter interactions. Our computational analysis linking kinases to putative substrates recapitulates known phosphoregulation events and provides putative evolutionary histories for the kinase regulation of protein complexes across 11 yeast species. To validate these trends, we used the E-MAP approach to analyze over 2,000 quantitative genetic interactions in S. cerevisiae and Sc. pombe, which demonstrated that protein kinases, and to a greater extent TFs, show lower than average conservation of genetic interactions. We propose therefore that protein kinases are an important source of phenotypic diversity.

Figure 1a in the paper shows the intriguing observation that, despite rapid evolution of individual phosphorylation sites, the relative number of phosphorylation sites within proteins from different functional classes (Gene Ontology categories) remains remarkably constant between species:

Beltrao et al., PLoS Biology, 2009, Figure 1a

However, it occurred to me that this could potentially be a consequence of longer proteins having more phosphorylation sites, and protein length being conserved through evolution. I thus counted the number of unique phosphorylation sites identified in each protein (thanks to Pedro Beltrao for providing the data) and correlated it with the length of the proteins. In the two plots below, I have pooled the proteins so that each dot corresponds to 100 proteins. The upper and lower panels show the results for S. cerevisiae and S. pombe, respectively:

Number of phosphorylation sites vs. protein lengh for S. cerevisiae

Number of phosphorylation sites vs. protein length for S. pombe

As should be evident from the plots, the average number of phosphorylation sites in a protein correlates strongly with its length, which is by no means surprisings. It is unclear to me why the intercept with the y-axis appears to differ from zero in both plots; suggestions are welcome.

The next question was whether the Gene Ontology terms that correspond to proteins with many phosphorylation sites are indeed assigned to proteins that are longer than average. I thus examined the terms “Cell budding”, “Morphogenesis”, and “Signal transduction”.

The average S. cerevisiae protein is 450 aa long. Proteins annotated with “Cell budding”, “Morphogenesis”, and “Signal transduction” are on average 1.6 (739 aa), 2.1 (945 aa), and 1.5 (679 aa) times longer, respectively. By comparison, the corresponding ratios observed for phosphorylation sites are approximately 2.3, 2.6, and 2.4. It would thus appear that differences in protein length between functional classes of proteins account for much, but not all, of the signal that was observed by Beltrao et al. when comparing the number phosphorylation sites.

Edit: Make sure to read Pedro Beltrao’s follow-up blog post, which nicely confirms that whereas protein length does play a role, it is not the full story.

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Live: Evolution of biological pathways

Orkun Soyer has just finished his excellent presentation at CoSBi on the use of toy models for understanding the principles that govern biological pathways, in particular signaling pathways. One can obviously imagine several scenarios for how pathways came about:

Evolution vs. intelligent design

The key point, however, is that we might be able to understand something about pathways through computational studies of simple toy models. The toy model discussed throughout the talk was bacterial chemotaxis:

Evolving “chemotaxis” in a computer

The idea is that evolution can to some extend be approximated as an optimization process, in which the objective function corresponds to fitness. In case of the “tumble or swim” problem, computational simulations allowed simple regulatory network to evolve that mimic the food-finding behavior of bacteria.

He also presented an interesting view on how biological complexity has evolved. The idea is to show how complex systems can evolve even if assuming a (weak) selection against complexity:

Modeling the evolution of complexity

I think that his results provide a lot of insight into how real signaling may have evolved, although all the simulations are based on simplistic toy models. I recommend that you download Orkun Soyer’s slides if you want to know more.

This talk ends the Computational and Systems Biology course at CoSBi.

Live: Networks, noise and survival in stress

Gabor Balazsi has just finished a very interesting presentation on the interplay between molecular networks, gene expression noise, and evolutionary selection – here is the opening slide:

Garbor Balazsi’s opening slide

In the first part of his talk he gave a nice introduction to global network topology and network motifs – this should be nothing new to people familiar with the work of the Barabasi and Alon labs. He also explained the “Commander, Intermediate, Executor” model for hierarchical regulatory networks, which I had personally not heard about before, and the concept of “origons”, which seems quite use for understanding the response of large signaling networks to environmental cues.

The second part of his talk was about stochastic noise in gene expression. Genetically identical cells in a culture may express the same protein at different levels; this is a result of random noise influencing transcription, mRNA degradation, translation, and protein degradation. This is simply a consequence of low copy numbers giving rise to stochastic, as opposed to deterministic, behavior.

Finally, he talked about how noise at the level of gene expression can influence the survival of species in a changing environment. This part of his talk was kicked off with the funniest slide of his presentation:

Gabor Balazsi’s funniest slide

I guess it should be seen as a lesson on how not to do. He made some very good points about how noise plays hardly any role in multicellular organisms that reproduce sexually. By contrast, stochastic variation within clonal bacterial cultures provides much higher chance of survival when faced with sudden stress such treatment with anti-bacterial drugs. I would have liked to hear more about this, but unfortunately there was not much time left for this part of the presentation due to technical problems with the projectors. It looks like Guy Shinar picked the safe strategy for his presentation.

All in all, I found it to be a really inspiring talk. I have uploaded his slides in case if you want to take a look at it.