Wednesday, February 10, 2010 - 11:00 , Location: 255 Skiles , Steven Ellner , Cornell , email@example.com , Organizer:
Emerging diseases have played an important role in the major recent declinesof coral reef cover worldwide. I will present some theoretical efforts aimedat understanding processes of coral disease development and itsconsequences: (1) how the development of coral disease is regulated bymicrobial population interactions within the mucus layer surrounding thecoral, and (2) the effects of a recent fungal epizootic on populations of aCaribbean sea fan coral, focusing on how this species was able to recover tohigh abundance and low disease prevalence. Collaborators on this workinclude John Bruno (UNC-CH); C. Drew Harvell, Laura Jones, and JustinMao-Jones (Cornell), and Kim Ritchie (MOTE Marine Lab).
Wednesday, February 3, 2010 - 11:00 , Location: Skiles 255 , Ilya Nemenman , Emory University , Organizer:
Even the simplest biochemical networks often have more degrees of freedoms than one can (or should!) analyze. Can we ever hope to do the physicists' favorite trick of coarse-graining, simplifying the networks to a much smaller set of effective dynamical variables that still capture the relevant aspects of the kinetics? I will argue then that methods of statistical physics provide hints at the existence of rigorous coarse-grained methodologies in modeling biological information processing systems, allowing to identify features of the systems that are relevant to their functions. While a general solution is still far away, I will focus on a specific example illustrating the approach. Namely, for a a general stochastic network exhibiting the kinetic proofreading behavior, I will show that the microscopic parameters of the system are largely important only to the extent that they contribute to a single aggregate parameter, the mean first passage time through the network, and the higher cumulants of the escape time distribution are related to this parameter uniquely. Thus a phenomenological model with a single parameter does a good job explaining all of the observable data generated by this complex system.
Wednesday, January 20, 2010 - 11:00 , Location: SKiles 269 , Lee Childers , Georgia Tech, School of Applied Physiology , firstname.lastname@example.org , Organizer:
Cycling represents an integration of man and machine. Optimizing this integration through changes in rider position or bicycle component selection may enhance performance of the total bicycle/rider system. Increasing bicycle/rider performance via mathematical modeling was accomplished during the US Olympic Superbike program in preparation for the 1996 Atlanta Olympic Games. The purpose of this presentation is to provide an overview on the science of cycling with an emphasis on biomechanics using the track pursuit as an example. The presentation will discuss integration and interaction between the bicycle and human physiological systems, how performance may be measured in a laboratory as well as factors affecting performance with an emphasis on biomechanics. Then reviewing how people pedal a bicycle with attention focused on forces at the pedal and the effect of position variables on performance. Concluding with how scientists working on the US Olympic Superbike program incorporated biomechanics and aerodynamic test data into a mathematical model to optimize team pursuit performance during the 1996 Atlanta Olympic Games.
Wednesday, December 2, 2009 - 11:00 , Location: Skiles 269 , Laura Miller , University of North Carolina at Chapel Hill , Organizer: Christine Heitsch
The Reynolds number (Re) is often used to describe scaling effects in ﬂuid dynamics and may be thought of as roughly describing the ratio of inertial to viscous forces in the ﬂuid. It can be shown that ’reciprocal’ methods of macroscopic propulsion (e.g. ﬂapping, undulating, and jetting) do not work in the limit as Re approaches zero. However, such macroscopic forms of locomotion do not appear in nature below Re on the order of 1 − 10. Similarly, macroscopic forms of feeding do not occur below a similar range of Reynolds numbers. The focus of this presentation is to describe the scaling effects in feeding and swimming of the upside down jellyﬁsh (Cassiopeia sp.) using computational fluid dynamics and experiments with live animals. The immersed boundary method is used to solve the Navier-Stokes equations with an immersed, flexible boundary. Particle image velocimetry is used to quantify the flow field around the live jellyfish and compare it to the simulations.
Wednesday, November 18, 2009 - 11:00 , Location: Skiles 269 , Jaap de Roode , Emory University , Organizer:
Host: Meghan Duffy (School of Biology, Georgia Tech)
Why do parasites cause disease? Theory has shown that natural selection could select for virulent parasites if virulence is correlated with between-host parasite transmission. Because ecological conditions may affect virulence and transmission, theory further predicts that adaptive levels of virulence depend on the specific environment in which hosts and parasites interact. To test these predictions in a natural system, we study monarch butterflies (Danaus plexippus) and their protozoan parasite (Ophryocystis elektroscirrha). Our studies have shown that more virulent parasites obtain greater between-host transmission, and that parasites with intermediate levels of virulence obtain highest fitness. The average virulence of wild parasite isolates falls closely to this optimum level, providing additional support that virulence can evolve as a consequence of natural selection operating on parasite transmission. Our studies have also shown that parasites from geographically separated populations differ in their virulence, suggesting that population-specific ecological factors shape adaptive levels of virulence. One important ecological factor is the monarch larval host plants in the milkweed family. Monarch populations differ in the milkweed species they harbor, and experiments have shown that milkweeds can alter parasite virulence. Our running hypothesis is that plant availability shapes adaptive levels of parasite virulence in natural monarch populations. Testing this hypothesis will improve our understanding of why some parasites are more harmful than others, and will help with predicting the consequences of human actions on the evolution of disease.
Wednesday, November 11, 2009 - 11:00 , Location: Skiles 269 , Gennady Cymbalyuk , Georgia State University, Neuroscience Institute and Dept. of Physics and Astronomy , Organizer:
Bursting, tonic spiking, sub-threshold oscillations and silence are basic robust regimes of activity of a single neuron. The talk will be focused on the co-existence of regimes of activity of neurons. Such multistability enhances potential flexibility to the nervous system and has many implications for motor control and decision making. I will identify different scenarios leading to multistability in the neuronal dynamics and discuss its potential roles in the operation of the central nervous system under normal and pathological conditions.
Wednesday, November 4, 2009 - 11:00 , Location: Skiles 269 , Tanya Berger-Wolf , Department of Computer Science, University of Illinois at Chicago , Organizer: Christine Heitsch
Computation has fundamentally changed the way we study nature. Recent breakthroughs in data collection technology, such as GPS and other mobile sensors, are giving biologists access to data about wild populations that are orders of magnitude richer than any previously collected. Such data offer the promise of answering some of the big ecological questions about animal populations. The data are not unique to animal domain but is now prevalent in human interactions: emails, blogs, and online social networks. Unfortunately, our ability to analyze these data lags substantially behind our ability to collect it. In particular, interactions among individuals are often modeled as social networks where nodes represent individuals and an edge exists if the corresponding individuals have interacted during the observation period. The model is essentially static in that the interactions are aggregated over time and all information about the time and ordering of social interactions is discarded. We show that suchtraditional social network analysis methods may result in incorrect conclusions on dynamic data about the structure of interactions and the processes that spread over those interactions. We have extended computational methods for social network analysis to explicitly address the dynamic nature of interactions among individuals. We have developed techniques for identifying persistent communities, influential individuals, and extracting patterns of interactions in dynamic social networks. We will present our approach and demonstrate its applicability by analyzing interactions among zebra populations.
Wednesday, October 28, 2009 - 11:00 , Location: Skiles 269 , Troy Shinbrot , Biomedical Engineering, Rutgers University , Organizer:
Many vertebrate motor and sensory systems "decussate," or cross the midline to the opposite side of the body. The successful crossing of millions of axons during development requires a complex of tightly controlled regulatory processes. Since these processes have evolved in many distinct systems and organisms, it seems reasonable to presume that decussation confers a significant functional advantage - yet if this is so, the nature of this advantage is not understood. In this talk, we examine constraints imposed by topology on the ways that a three dimensional processor and environment can be wired together in a continuous, somatotopic, way. We show that as the number of wiring connections grows, decussated arrangements become overwhelmingly more robust against wiring errors than seemingly simpler same-sided wiring schemes. These results provide a predictive approach for understanding how 3D networks must be wired if they are to be robust, and therefore have implications both regenerative strategies following spinal injury and for future large scale computational networks.
Wednesday, October 21, 2009 - 11:00 , Location: Skiles 269 , Klas Udekwu , Biology, Emory University , email@example.com , Organizer:
Treatment of bacterial infections with antibiotics is universally accepted as one of (if not THE) most significant contributions of medical intervention to reducing mortality and morbidity during last century. Surprisingly, basic knowledge about how antibiotics kill or prevent the growth of bacteria is only just beginning to emerge and the dose and term of antibiotic treatment has long been determined by clinicians empirically and intuitively. There is a recent drive to theoretically and experimentally rationalize antibiotic treatment protocols with the aim to them and to design protocols which maximize antibiotics’ efficacy while preventing resistance emergence. Central to these endeavors are the pharmacodynamics of the antibiotic(s) and bacteria, PD (the relationship between the concentration of the antibiotic and the rate of growth/death of bacteria), and the pharmacokinetics of the antibiotic, PK (the distribution and change in concentration of the antibiotics in a treated host) of each bacteria. The procedures for estimating of PD and PK parameters are well established and standardized worldwide. Although different PK parameters are commonly employed for the design of antibiotic treatment protocols most of these considerations, a single PD parameter is usually used, the minimum inhibitory concentration (MIC). The Clinical and Laboratory Standards Institute (CLSI) approved method for estimating MICs defines testing conditions that are optimal for the antibiotic, like low densities and exponential growth, rarely obtain outside of the laboratory and virtually never in the bacteria infecting mammalian hosts. Real infections with clinical symptoms commonly involve very high densities of bacteria, most of which are not replicating, and these bacteria are rarely planktonic, rather residing as colonies or within matrices called biofilms which sometimes include other species of bacteria. Refractoriness (non-inherited resistance) is the term used to describe an observed inefficacy of antibiotics on otherwise antibiotic-susceptible bacterial populations. This talk will focus on our efforts to describe the pharmacodynamic relationship between Staphylococcus aureus and antibiotics of six classes in the light of antibiotic refractoriness. I will begin by addressing the effects of cell density on the MIC index, after which I intend to present unpublished data descriptive of physiology-related effects on antibiotic efficacy. Additionally, we will explore the potential contribution of such in vitro results, to observed/predicted clinical outcomes using standard mathematical models of antibiotic treatment which also serve to generate testable hypotheses.
Wednesday, October 14, 2009 - 11:00 , Location: Skiles 269 , Bart Haegeman , INRIA, Montpellier, France , Organizer:
Hubbell's neutral model provides a rich theoretical framework to study ecological communities. By coupling ecological and evolutionary time scales, it allows investigating how communities are shaped by speciation processes. The speciation model in the basic neutral model is particularly simple, describing speciation as a point mutation event in a birth of a single individual. The stationary species abundance distribution of the basic model, which can be solved exactly, fits empirical data of distributions of species abundances surprisingly well. More realistic speciation models have been proposed such as the random fission model in which new species appear by splitting up existing species. However, no analytical solution is available for these models, impeding quantitative comparison with data. Here we present a self-consistent approximation method for the neutral community model with random fission speciation. We derive explicit formulas for the stationary species abundance distribution, which agree very well with simulations. However, fitting the model to tropical tree data sets, we find that it performs worse than the original neutral model with point mutation speciation.