Georgia Institute of Technology College of Sciences

The paper "Complete probabilistic analysis of RNA shapes" (2006) by Voss, Giegerich, and Rehmsmeier will be discussed.

Multiscale and stochastic approaches play a crucial role in faithfully capturing the dynamical features and making insightful predictions of cellular reacting systems involving gene expression. Despite theiraccuracy, the standard stochastic simulation algorithms are necessarily inefficient for most of the realistic problems with a multiscale nature characterized by multiple time scales induced by widely disparate reactions rates. In this talk, I will discuss some recent progress on using asymptotic techniques for probability theory to simplify the complex networks and help to design efficient numerical schemes.

The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) system is a recently discovered immune defense in bacteria and archaea (hosts) that functions via directed incorporation of viral DNA into host genomes. Here, we introduce a multi-scale model of dynamic coevolution between hosts and viruses in an ecological context that incorporates CRISPR immunity principles. We analyze the model to test whether and how CRISPR immunity induces host and viral diversification and maintenance of coexisting strains. We show that hosts and viruses coevolve to form highly diverse communities through punctuated replacement of extant strains. The populations have very low similarity over long time scales. However over short time scales, we observe evolutionary dynamics consistent with incomplete selective sweeps of novel strains, recurrence of previously rare strains, and sweeps of coalitions of dominant host strains with identical phenotypes but different genotypes. Our explicit eco-evolutionary model of CRISPR immunity can help guide efforts to understand the drivers of diversity seen in microbial communities where CRISPR systems are active. 

I will present a construction of a non-measurable set using the fundamental fact that a graph with no odd cycles is 2-colorable. That will not take very long, even though I will prove everything from first principles. In the rest of the time I will discuss the Axiom of Choice and some unprovable statements. The talk should be accessible to undergraduates.

We will discuss several results (and open problems) related to rearrangements of Fourier series, particularly quantitative questions about maximal and variational operators. For instance, we show that the canonical ordering of the trigonometric system is not optimal for certain problems in this setting. Connections with analytic number theory will also be given. This is based on joint work with Allison Lewko.

Author and biologist Bernd Heinrich will discuss his research into the biological mysteries of social insects and birds, including the seemingly illogical food-sharing behavior of ravens.

I will review the well known method (pushed mainly by Karlin and McGregor) to study birth-and-death processes with the help of orthogonal polynomials. I will then look at several extensions of this idea, including ¨poker dice¨ (polynomials in several variables) and quantum walks (polynomials in the unit circle).

In the node-connectivity augmentation problem, we want to add a minimum number of new edges to an undirected graph to make it k-node-connected. The complexity of this question is still open, although the analogous questions of both directed and undirected edge-connectivity and directed node-connectivity augmentation are known to be polynomially solvable. I present a min-max formula and a polynomial time algorithm for the special case when the input graph is already (k-1)-connected. The formula has been conjectured by Frank and Jordan in 1994. In the first lecture, I shall investigate the background, present some results on the previously solved connectivity augmentation cases, and exhibit examples motivating the complicated min-max formula of my paper.

Recently, there has been great interest in understanding the fundamental limits of our ability to sample from the independent sets (i.s.) of a graph. One approach involves the study of the so-called hardcore model, in which each i.s. is selected with probability proportional to some fixed activity $\lambda$ raised to the cardinality of the given i.s. It is well-known that for any fixed degree $\Delta$, there exists a critical activity $\lambda_{\Delta}$ s.t. for all activities below $\lambda_{\Delta}$, the sampled i.s. enjoys a long-range independence (a.k.a. uniqueness) property when implemented on graphs with maximum degree $\Delta$, while for all activities above $\lambda_{\Delta}$, the sampled i.s. exhibits long-range dependencies. Such phase transitions are known to have deep connections to the inherent computational complexity of the underlying combinatorial problems. In this talk, we study a family of measures which generalizes the hardcore model by taking more structural information into account, beyond just the number of nodes belonging to the i.s., with the hope of further probing the fundamental limits of what we can learn about the i.s. of a graph using only local information. In our model, the probability assigned to a given i.s. depends not only on its cardinality, but also on how many excluded nodes are adjacent to exactly $k$ nodes belonging to the i.s., for each $k$, resulting in a parameter for each $k$. We generalize the notion of critical activity to these ``neighborly measures", and give necessary and sufficient conditions for long-range independence when certain parameters satisfy a log-convexity(concavity) requirement. To better understand the phase transitions in this richer model, we view the classical critical activity as a particular point in the parameter space, and ask which directions can one move and still maintain long-range independence. We show that the set of all such ``directions of uniqueness” has a simple polyhedral description, which we use to study how moving along these directions changes the probabilities associated with the sampled i.s. We conclude by discussing implications for choosing how to sample when trying to optimize a linear function of the underlying probabilities.