Seminars and Colloquia by Series

A Simple Quantitative Genetic Model of Parent-Offspring Interactions

Series
Mathematical Biology Seminar
Time
Wednesday, November 12, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Benjamin Ridenhour – CDC/CCID/NCIRD, CTR
Parent-offspring interactions lead to natural conflicts. Offspring want as many resources as possible from parents in order to gain maximal fitness levels. On the other hand, parents desire to invest only enough to guarantee survival to reproduction. The resolution of the parent-offspring conflict has been a topic of much debate in evolutionary biology and typically invoke the concept of 'costs' to begging by offspring. Here I present the analysis of a simple quantitative genetic model of parent-offspring interactions that does not costs to resolve parent-offspring conflicts.

Eat your spinach? The role of buffering reactions in clearing hydrogen

Series
Mathematical Biology Seminar
Time
Wednesday, November 5, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Melissa Kemp – Dept of Biomedical Engineering, Georgia Tech
Hydrogen peroxide has been long considered a harmful reactive oxygen species, but is increasingly appreciated as a cellular signaling molecule. The mechanism by which the cell buffers against intracellular H2O2 accumulation during periods of oxidative stress is not fully understood. I will introduce a detailed network model of the known redox reactions and cellular thiol modifications involved in H2O2 buffering. The model includes anti-oxidative contributions of catalase, glutathione peroxidase, peroxiredoxin, and glutaredoxin, in addition to the cytoplasmic redox buffers, thioredoxin and glutathione. Based on ordinary differential equations, the model utilizes mass action kinetics to describe changes in concentration and redox state of cytoplasmic proteins upon exposure to physiologically relevant concentrations of extracellular H2O2. Simulations match experimental observations of a rapid and transient oxidation of thioredoxin upon exposure to extracellular peroxide. The increase in the concentration of oxidized proteins predicted by the model is simultaneously accompanied by an increase in protein S-glutathionylation, possibly regulating signal transduction in cells undergoing oxidative stress. Ultimately, this network analysis will provide insight into how to target antioxidant therapies for enhanced buffering without impacting the necessary protein oxidation used by cells for signaling purposes.

Data-driven methods in protein engineering: new ways to utilize sequence and structures of proteins

Series
Mathematical Biology Seminar
Time
Wednesday, October 22, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Andy Bommarius – School of Chemistry & Biochemistry, Georgia Tech
After rational protein design and combinatorial protein engineering (directed evolution), data-driven protein engineering emerges as a third generation of techniques for improving protein properties. Data-driven protein engineering relies heavily on the use of mathematical algorithms. In the first example, we developed a method for predicting the positions in the amino acid sequence that are critical for the catalytic activity of a protein. With nucleotide sequences of both functional and non-functional variants and a Support Vector Machine (SVM) learning algorithm, we set out to narrow the interesting sequence space of proteins, i.e. find the truly relevant positions. Variants of TEM-1 β-lactamase were created in silico using simulations of both mutagenesis and recombination protocols. The algorithm was shown to be able to predict critical positions that can tolerate up to two amino acids. Pairs of amino acid residues are known to lead to inactive sequences, unless mutated jointly. In the second example, we combine SVM, Boolean learning (BL), and the combination of the two, BLSVM, to find such interactive residues. Results on interactive residues in two fluorescent proteins, Discosoma Red Fluorescent Protein (Ds-Red) and monomeric Red Fluorescent Protein (mRFP), will be presented.

Dynamics and implications of some models of hepatitis B virus infection

Series
Mathematical Biology Seminar
Time
Wednesday, October 15, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Yang Kuang – Arizona State University
Chronic HBV infection affects 350 million people and can lead to death through cirrhosis-induced liver failure or hepatocellular carcinoma. We present the rich dynamics of two recent models of HBV infection with logistic hepatocyte growth and a standard incidence function governing viral infection. One of these models also incorporates an explicit time delay in virus production. All model parameters can be estimated from biological data. We simulate a course of lamivudine therapy and find that the models give good agreement with clinical data. Previous models considering constant hepatocyte growth have permitted only two dynamical possibilities: convergence to a virus free or an endemic steady state. Our models admit periodic solutions. Minimum hepatocyte populations are very small in the periodic orbit, and such a state likely represents acute liver failure. Therefore, the often sudden onset of liver failure in chronic HBV patients can be explained as a switch in stability caused by the gradual evolution of parameters representing the disease state.

A population model of influenza designed to evaluate projected pandemic vaccine production in Taiwan

Series
Mathematical Biology Seminar
Time
Wednesday, October 8, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Dr. John Glasser – CDC/CCID/NCIRD

Background: We endeavor to reproduce historical observations and to identify and remedy the cause of any disparate predictions before using models to inform public policy-making. We have no finely age- and time-stratified observations from historical pandemics, but prior exposure of older adults to a related strain is among the more compelling hypotheses for the w-shaped age-specific mortality characterizing the 1918 pandemic, blurring the distinction between annual and pandemic influenza.

Methods: We are attempting to reproduce patterns in annual influenza morbidity and mortality via a cross-classified compartmental model whose age class sojourns approximate the longevity of clusters of closely-related strains. In this population model, we represent effective inter-personal contacts via a generalization of Hethcote's formulation of mixing as a convex combination of contacts within and between age groups. Information about mixing has been sought in face-to-face conversations, a surrogate for contacts by which respiratory diseases might be transmitted, but could also be obtained from household and community transmission studies. We reanalyzed observations from several such studies to learn about age-specific preferences, proportions of contacts with others the same age. And we obtained age-specific forces of infection from proportions reporting illness in a prospective study of household transmission during the 1957 influenza pandemic, which we gamma distributed to correct for misclassification. Then we fit our model to weekly age-specific hospitalizations from Taiwan's National Health Insurance Program, 2000-07, by adjusting a) age-specific coefficients of harmonic functions by which we model seasonality and b) probabilities of hospitalization given influenza.

Results: While our model accounts for only 30% of the temporal variation in hospitalizations, estimated conditional probabilities resemble official health resource utilization statistics. Moreover, younger and older people are most likely to be hospitalized and elderly ones to die of influenza, with modeled deaths 10.6% of encoded influenza or pneumonia mortality.

Conclusions: Having satisfactorily reproduced recent patterns in influenza morbidity and mortality in Taiwan via a deterministic model, we will switch to a discrete event-time simulator and - possibly with different initial conditions and selected parameters - evaluate the sufficiency of projected pandemic vaccine production.

Joint work with Denis Taneri, and Jen-Hsiang Chuang

Algebraic models in systems biology

Series
Mathematical Biology Seminar
Time
Wednesday, September 24, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Reinhard Laubenbacher – Virginia Bioinformatics Institute and Department of Mathematics, Virginia Tech
Since John von Neumann introduced cellular automata in the 1950s to study self-replicating systems, algebraic models of different kinds have increased in popularity in network modeling in systems biology. Their common features are that the interactions between network nodes are described by "rules" and that the nodes themselves typically take on only finitely many states, resulting in a time-discrete dynamical system with a finite state space. Some advantages of such qualitative models are that they are typically intuitive, can accommodate noisy data, and require less information about a variety of kinetic and other parameters than differential equations models. Yet they can capture essential network features in many cases. This talk will discuss examples of different types of algebraic models of molecular networks and a common conceptual framework for their analysis.

Meet your neighbors! An introduction to social insects

Series
Mathematical Biology Seminar
Time
Wednesday, September 10, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Michael Goodisman – School of Biology, Georgia Tech
The evolution of sociality represented one of the major transition points in biological history. Highly social animals such as social insects dominate ecological communities because of their complex cooperative and helping behaviors. We are interested in understanding how evolutionary processes affect social systems and how sociality, in turn, affects the course of evolution. Our research focuses on understanding the social structure and mating biology of social insects. In addition, we are interested in the process of development in the context of sociality. We have found that some social insect females mate with multiple males, and that this behavior affects the structure of colonies.  We have also found that colonies adjust their reproductive output in a coordinated and adaptive manner. Finally, we are investigating the molecular basis underlying the striking differences between queens and workers in highly social insects. Overall, our research provides insight into the function and evolutionary success of highly social organisms.

Simple models for understanding plankton dynamics in mesoscale ocean turbulence

Series
Mathematical Biology Seminar
Time
Wednesday, September 3, 2008 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 255
Speaker
Annalisa Bracco – School of Earth & Atmospheric Sciences, Georgia Tech
In the ocean, coherent vortices account for a large portion of the ocean turbulent kinetic energy and their presence significantly affects the dynamics and the statistical properties of ocean flows, with important consequences on transport processes. Mesoscale vortices also affect the population dynamics of phyto- and zooplankton, and are associated with secondary currents responsible for localized vertical fluxes of nutrients. The fact that the nutrient fluxes have a fine spatial and temporal detail, generated by the eddy field, has important consequences on primary productivity and the horizontal velocity field induced by the eddies has been suggested to play an important role in determining plankton patchiness. Owing to their trapping properties, vortices can also act as shelters for temporarily less-favoured planktonic species. In this contribution, I will review some of the transport properties associated with coherent vortices and their impact on the dynamics of planktoni ecosystems, focusing on the simplified conceptual model provided by two-dimensional turbulence.

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