17w5104: Mathematical Approaches to Evolutionary Trees and Networks Workshop @BIRS_math 2/12/17 #ITBio
Arriving in Banff, Alberta Sunday, February 12 and departing Friday February 17, 2017
Organizers
- Leonid Chindelevitch (Simon Fraser University)
- Caroline Colijn (Imperial College London)
- Amaury Lambert (University Pierre and Marie Curie, Paris)
- Marta Luksza (Institute of Advanced Study, Princeton University)
- Vincent Moulton (University of East Anglia)
- Tandy Warnow (University of Illinois)
Objectives
The objectives of the workshop are to bring mathematicians working in three key areas together to make progress in these problems. We will also invite several biologists who are keen to engage with mathematicians on the challenges posed by new data on evolutionary processes. Key challenges in the field at the moment are focused around the following emerging inter-related areas, each of which is raising mathematically interesting problems:
1. Inference with evolutionary trees and networks: Ultimately it is necessary not just to obtain evolutionary trees from data using standard methods, but to infer aspects of an underlying biological process. This requires understanding the likelihood of an evolutionary tree or network, or at least some of its informative features, using some stochastic process as the underlying ecological model. In principle, this approach allows simultaneous inference of both evolutionary trees and parameters of the ecological model. Coalescent theory has made considerable progress, for example, in obtaining tree likelihoods for sparsely sampled populations with geographical structure or with known past demographics (see for just one example [5]). In some simplified cases, epidemiological inference methods can estimate transmission trees [2], branching rates through time [5] and other aspects of epidemic spread [7]. However, none of these approaches is currently applicable if there is non-tree-like evolution, or where datasets are large. Furthermore, the range of models for which we can write down a tree likelihood is very limited. This raising nice new problems in probability, statistical inference and ecological modelling. Recently, more general processes (e.g. Lambda-coalescents, which allow multiple rather than strictly pairwise coalescent events) are beginning to be used to model populations with large offspring variance, or even to model selection in a non-parametric fashion [3]. This is potentially a powerful tool particularly for bacteria, which may acquire resistance to antibiotics and spread rapidly as a consequence, yielding both highly variable effective offspring numbers and a need to model selection carefully.
2. Understanding spaces of evolutionary trees: There are a large number of possible labelled, rooted binary trees for a given set of nn tips (ie for a given set of sequence data): (2n−3)!!=(2n−3)(2n−5)...(3)(1)(2n−3)!!=(2n−3)(2n−5)...(3)(1). This works out to 1018410184 trees on 100 tips; in contrast, current datasets for evolving bacteria contain thousands of tips. Not even the tools of Bayesian inference, the natural approach in such situations, can systematically explore spaces this big. This motivates the development of mathematical approaches for the exploration of tree space. These include new approaches to continuous tree spaces, including those from tropical geometry [8], and the use of tree metrics [1]. These in turn can lead to tools for averaging trees , and for navigating tree space in efficient ways [6] -- with profound applications in statistical inference from sequence data. Generalizing metrics to the case of evolutionary networks (for example tree-based networks) is another natural and important question.
3. Summarising trees and networks using combinatorial tools: Uncovering shape features, spectral features and other ways to describe trees using quantities that are mathematically tractable will be of considerable interest [4]. As one example, where likelihoods are truly intractable, rapid tools for likelihood-free inference can be used to infer evolutionary processes from sequence data, but only where there are informative ways to summarize key features of the data. Trees are natural combinatorial structures with connections to data; for example, a binary tree is a sequence of partitions of the set of tips (sequences in a dataset), where each partition is one block smaller than the previous one, moving back through time from the partition with each tip on its own to the partition with all tips in one block as we move from the tips of the tree to the root. If the tree is not binary (ie it allows multifurcations), more than two blocks can combine at a branching event. Because of the natural link to partitions, the study of tree shapes links to the enumeration of partitions and to lattice path combinatorics. These in turn allow the characterization and enumeration of possible tree shapes. Meanwhile the study of motifs in other biological networks has been fruitful, and could be extended to tree and evolutionary network shapes. Trees and evolutionary networks are of course also graphs (with an added time dimension); the tools of algebraic graph theory are now finding application in this area of mathematical biology.
The community's response to the idea for this workshop has been very positive. A * beside a participant's name indicates that they have expressed enthusiasm for the workshop, and plan to attend.
References [1] Louis J Billera, Susan P Holmes, and Karen Vogtmann. Geometry of the space of phylogenetic trees. Adv. Appl. Math., 27(4):733–767, November 2001. [2] Xavier Didelot, Jennifer Gardy, and Caroline Colijn. Bayesian inference of infectious disease transmission from whole-genome sequence data. Mol. Biol. Evol., 31(7):1869–1879, July 2014. [3] Alison M Etheridge, Robert C Griffiths, and Jesse E Taylor. A coalescent dual process in a moran model with genic selection, and the lambda coalescent limit. Theor. Popul. Biol., 78(2):77–92, September 2010. [4] Fanny Gascuel, Regis Ferriere, Robin Aguilee, and Amaury Lambert. How ecology and landscape dynamics shape phylogenetic trees. Syst. Biol., 64(4):590–607, July 2015. [5] Amaury Lambert and Tanja Stadler. Birth–death models and coalescent point processes: The shape and probability of reconstructed phylogenies. Theor. Popul. Biol., 90(0):113–128, December 2013. [6] Tom M W Nye. An algorithm for constructing principal geodesics in phylogenetic treespace. IEEE/ACM Trans. Comput. Biol. Bioinform., 11(2):304–315, March 2014. [7] David A Rasmussen, Erik M Volz, and Katia Koelle. Phylodynamic inference for structured epidemiological models. PLoS Comput. Biol., 10(4):e1003570, April 2014. 2 [8] David Speyer and Bernd Sturmfels. The tropical grassmannian. Adv. Geom., 4(3):389–411, 2004.
Happy New Year to you!
RE: Your blog vs. G+, what if I told you that you might have your cake and eat it too? I too have a WordPress blog, but I've tacked a few bits on to the back end and this allows me to use my blog as the primary hub for my work as you're suggesting you plan on doing. I then use platforms (walled silos) like Google+, Twitter, and Facebook to syndicate my material out to allow for a broader reach by leveraging social networks effects to my benefit. Even better, I've got things set up so that comments on platforms like Google+ feed back to my blog, which allows me to not only live on my own site, but it also allows me to "own" all of the content and commentary that's generated from it. (This way if Google+ is shut down next month, I'd still have all of the generated commentary, which could live indefinitely on my own site.) Thus your followers who prefer to read your material on other platforms (G+, in your case) aren't left without the content you're generating, but their commentary also appears back on the blog to add to the larger conversation.
First, consider using the "Jetpack" module (free software built by the same company that makes WordPress) and its publicize functionality to connect your G+ account and auto-post (syndicate) your Azimuth content automatically to G+. (This also frees up a good bit of time to not have to do it manually.) If nothing else, this will help to keep some of your audience engaged.
Second, to port the comments from the syndicated G+ post back to your original post on Azimuth (comments from G+ will show up as comments in WordPress that you can approve/delete just like other WP comments; this also prevents you from spending so much time in G+ after you turn off comment notifications) you can follow the details at the IndieWebCamp site (http:/
If you need help/assistance to make the technical hurdles a bit lower, I'm happy to help walk you through the details or even do it for you (gratis) if you'd like. All of the modules I've mentioned are free and open source and under very active development.
For a simple example see the comments on this post http:/
https:/
For a brief overview, you might appreciate the opening few paragraphs of http:/
Running a catblog must be a great way to #dogfood your #indieweb development work. https:/
@fadesingh, While I do agree in part with your comment that the technical side of the math could have been explored a bit more, to me, part of what this story is highlighting (and to great benefit as well) is the "other" personal side of mathematics which is rarely seen by the broader public. Most of math and math history is full of seemingly brilliant solo (read: lone wolf) researchers developing mathematics de novo in dark, smoke and caffeine-filled rooms and emerging with iron clad proofs. This particular story shows the growing more collaborative and friendly side of math in addition to the years of slow development of friendships and theory which have culminated into something potentially interesting. I would suggest that in this case you not take them too hard to task on the subject, particularly as the article was being written contemporaneously with the publication of the journal article itself. (The article here was published <i>just</i> in advance of the arXiv post, such that it didn't even include the link to the paper itself, though it was added the following day.)
I love that Quanta is continually exploring the areas of math and science at the depth and level which they've become accustomed. They're filling a very important gap in science communication between technical journal articles and somewhat sophisticated outlets like National Geographic, Scientific American, and Wired (in which they are also distributed, but yet are still an editorial cut above comparatively. [Cross reference: <a href="http://stream.boffosocko.com/2015/evolution-of-a-scientific-journal-article-title-from-nature-to">Evolution of a Scientific Journal Article Title (from Nature to TMZ)</a>] I'm sure that C.P. Snow himself would praise them for helping to close the gap between the "Two Cultures."
@jkholm You're welcome (assuming you saw my RT from earlier....) ;) Which division are you in development for?
I've followed this process from before it's administrative beginning. It's nice to see that we've got a philosophy for what academic freedom is, though I honesty fail to see how it differs from a basic definition of what academic freedom means in the last century, so congratulations to the dozens of people who spent countless hours rewriting a basic definition. We've done the academic equivalent of writing the words, "We hold these truths to be self-evident" while failing to create any actual rules or guidelines by which the administration can hold the faculty, staff, or students accountable or which actually serve to protect the faculty, staff, or students from overstepping of authority by the university.
Where is the following "Constitution"? Where is the process for "Amendments"? Is the University actually granting any real rights here, and how are they to actually be protected? Surely we've evolved past the level of even the rights available during the Carolingian Renaissance and the early days of the birth of the universitas?
In particular, I find it disconcerting to see even the scant guidelines that existed in the intermediate draft that was sent for approval before it got to the board level have been removed. For example, statements like:
"When one is speaking on matters of public interest, it should be made clear that personal views do not represent those of the institution." or
"Professors who express their personal views on a contested issue must make it clear that students may disagree with those views without penalty."
no longer appear in the statement at all.
It's lovely that we have this new "document", but when the rubber actually meets the road, what will we do? Will we trip, stumble, and fall down as we have occasionally in the past? Where are those general guidelines? No one will care what we've said in this document, but they will surely judge us more harshly in the realm of public opinion based on the future actions of the administration and this is where the real work will have to begin.
Ron Daniels has been doing some generally good things in guiding the direction of the university community, but it seems odd that, as one of the first presidents of the institution with an academic background in law and what I know to be his philosophy in social equity, that we've heard nothing of next steps. I hope that with books entitled "Rule of Law Reform and Development: Charting the Fragile Path of Progress" and "Responsibility and Responsiveness," that we will see much more.
Some of my additional thoughts on the practicality of these matters can be found at: "Reframing What Academic Freedom Means in the Digital Age" [http:/
#AcademicFreedom #JohnsHopkins
Harbingers of Failure | Are there people you DON'T want to buy your product?
This is kind of like the polar opposite of the "mavens" Malcolm Gladwell talks about in Tipping Point. This is also much like the concept of negative results in science - few pay attention to what not to do or look at the "negative image" to give them a better view of the "image" they'd like to see.
Abstract: We show that some customers, whom we call ‘Harbingers’ of failure, systematically purchase new products that flop. Their early adoption of a new product is a strong signal that a product will fail - the more they buy, the less likely the product will succeed. Firms can identify these customers either through past purchases of new products that failed, or through past purchases of existing products that few other customers purchase. We discuss how these insights can be readily incorporated into the new product development process. Our findings challenge the conventional wisdom that positive customer feedback is always a signal of future success.
Perhaps irradiated Toxoplasmosis with a genetic defect that induces other short term psychological issues instead of the often heard long term ones? The phenotypic implications are particularly intriguing. What would Jaroslav Flegr say? Cross reference http:/
Otherwise, this could be the basis for a great zombie origination story! #development