Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine, and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options. As part of the NIH/NIGMS supported "MIDAS" (Models of Infectious Disease Agent Studies) network we developed and used a large-scale epidemic agent-based simulation (>300 million agents) to examine intervention options. The model and its use for evaluation of control strategies will be presented.

How immune cells respond to the presence of microbes and endogenous "danger" signals in the environment will be described. The different types of microbe-derived products that can serve as antigens, and how these are processed by innate immune cells for subsequent recognition during the adaptive immune response will be discussed.

This talk will summarize the salient features of the adaptive immune response as it relates to the immune response to infectious organisms. The respective roles of dendritic cells, T cells and B cells will be discussed

This talk will describe the basics of stochastic modeling and differential equations with emphasize on questions arising in immunology. In particular we will discuss random walks and reactions and their relevance to modeling at different scales from molecular level through cell and population levels.

Mathematical models are emerging as important tools in the study of microbiology. As an illustrative example, we present results from a number of models each generated to study the interaction of M. tuberculosis and the immune system.

This lecture will review the different assays used by immunologists to measure cytokine/chemokine production. Issues that are important for the interpretation of data acquired from the various techniques will be discussed. Techniques to be covered will include: RNA-based assays, ELISA, ELISpot, Luminex, intracellular cytokine staining, functional assays etc.

Flow cytometry is a widely used experimental tool that scientists use to characterize cellsŐ phenotype by using a combination of fluidics, optics, and electronics. Fluorescent-labeled antibodies are used as well as size in order to describe a cell type. The fluorescence is then measured with an instrument called a flow cytometer and a separate software program analyzes the results. A brief overview of flow cytometry theory and applications will be discussed during this presentation. An experimental protocol along with data from a study will be explained using the murine model as an example. A separate laboratory session will also be used to illustrate this topic.

Participants will stain a mixed population of cells for a selected set of markers.

Participants will analyze the stained samples on a flow cytometer.

This lecture will review the principles of flow cytometry in order to understand how immunologists discriminate and quantify distinct populations of cells, and examine cell function. We will discuss basic concepts such as light scatter, fluorescence and compensation. We will also examine raw data using flow cytometry analysis software.

Recent technological developments in flow cytometry allow increasing numbers of different cell-surface markers to be assessed simultaneously and promise to enhance phenotypic analysis dramatically. But the analysis of such high-dimensional data presents significant obstacles; sophisticated statistical approaches and visualization methods are required to realize the potential of this new technology. I will discuss several methods as well as present software developed in the Duke Center for Computational Immunology that assists in the performance of many of these analyses.

Microarray technology has blossomed from testing the expression of small numbers of genes to a wide variety of experimental tools. This talk will cover fundamental aspects of designing, executing, and preliminary assessment of microarray data. Microarray platforms and related molecular biology issues will be discussed. In addition, we will touch on microarray-based assays for other applications.

In this talk we will give a brief overview of data management and information retrieval, especially as they relate to biological data management. We will present prevailing data modeling paradigms, for structured and unstructured data, with an emphasis on XML. We will conclude with a hands-on XML laboratory session.

Microarrays allow the simultaneous measurement of tens of thousands of gene expression levels. This remarkable ability comes with a price: The usual framework of statistical hypothesis testing does not adapt easily to cases where there are tens of thousands of independent tests. I will present ideas from exploratory data analysis and discuss alternatives to hypothesis testing for gene expression microarray data.

Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. While useful, when analyzing these experiments researchers face many new computational challenges. Computational methods that are specifically designed for time series experiments are required so that we can take advantage of their unique features and address the unique problems they raise. In this session we will discuss several tools for different levels of analysis of time series expression data ranging from data analysis to clustering to modeling and integration with other types of high throughput biological data.

After groups of genes have been identified that share similar expression patterns the question arises is which transcription factors may contribute to their regulation. The promoters of each group of genes can be retrieved from the public databases and analyzed for statistically significant DNA patterns that might constitute the targets of transcription factors. In this session, we will present various commonly used computational tools and we will discuss their strengths and weaknesses.

Micro-arrays generate more information than our ability to analyze can cope with. That information is complex to analyze as some genes are spuriously activated, other mistakenly not activated. Many genes are not identified. Each pattern of activation involves many genes at the same time. Despite these complications, it is possible to elicit a lot of information from micro-arrays, using existing tools, some of them developed recently here. This presentation will demonstrate (using actual data) how it is possible to get information about transcription and signaling cascades as well as gene network, despite the web of complexity in which it resides.

Pathway analysis research involves a number of steps, and is often done in collaboration between experimental biologists and computational modeling experts. Software tools can make some of these tasks easier and less error-prone. Some of the steps in pathway research include:

1. Initial literature search
2. Expanding your pathway with information from experimental data, pathway databases & literature.
3. Diagramming and annotating your pathway
4. Estimating kinetic constants for reactions, translocations, etc.
   1. By searching databases
   2. By analyzing experimental data
5. Running pathway simulations
   1. Designing Experiments
   2. Predicting outcomes
   3. Comparing simulated results to actual data.
   4. Refining pathway knowledge through data analysis
6. Visualizing results
7. Creating plots and diagrams for publication of your results.

Many factors regulate the formation of the cellular immune response to virus infection. In order to function as antiviral effectors and later to form memory, T cells must survive in peripheral tissues. These extralymphoid environments are very different from those of the lymphoid organs, and little is known regarding the pro-and anti-apoptotic signals that affect T cells in these areas. This is particularly important in influenza infection, in which protection depends on T cells that survive and function in the relatively hostile environment of the infected airways. The apoptotic process is complicated, consisting of multiple, often overlapping pathways. Most of our understanding of T cell apoptosis is derived from in vitro experiments, and many questions remain as to whether the same mechanisms operate in vivo. Using polychromatic flow cytometric techniques, we have performed simultaneous analysis of multiple apoptotic pathways, activation profiles, and function among both total and virus specific CD8 T cells responding to acute influenza infection. The data suggests that we may have been underestimating the size of the virus-specific population, and that the overall response may be regulated in a way that is fundamentally different than previously thought. The data also shows that some T cell subsets are resistant to the apoptotis process via their interaction with the extralymphoid environment.

Biosimulation of Influenza virus dissemination and control within a population makes significant assumption regarding disease progression within individuals how this progression is modified by drugs or vaccination and how progression relates to transmissibility. Such population-based models would greatly benefit from mechanism-based, symptom and marker-related biological models. Such models would offer the opportunity of true multiscale simulation of disease dissemination and of containment strategies that include person-centered interventions. We will present the development of such a model, its potential applications and limitations, and the data requirements for calibration.

Influenza is among the most devastating diseases due to the ease viral spread through an aerosol route and the extreme lethality to humans. Influenza poses an even greater risk for the immunocompromised, the very young, and the elderly. Thus, there is a need for novel approaches for enhancing respiratory tract immunity to influenza virus. There is intense interest in the design and use of vaccine strategies to enhance immune responses to influenza. This is particularly important for vaccines against pandemic avian influenza. This lecture will outline the basic information on influenza and strategies for combating the next influenza pandemic.

Sepsis and trauma elicit an acute, complex inflammatory response, leading to organ dysfunction and death. We constructed mathematical models of increasing complexity, using differential equations that encompass the dynamics of relevant cells and cytokines as well as the resulting global tissue dysfunction, in order to begin to unravel these inflammatory interactions. The simplest model, consisting solely of a pathogen, a single population of inflammatory cells, and a measure of global tissue damage/dysfunction could describe both recoverable infection and septic shock. A more complex model was used to create simulated populations of septic patients and to simulate population responses to anthrax in the presence or absence of vaccination. The most complex model was calibrated in various inflammatory scenarios in mice and was able to predict responses to combinations of insults on which it was not trained. Our modeling/fitting platform was used to derive information about gene-knockout, aged, and drug-treated mice. Mathematical modeling provides insights into the complex dynamics of acute inflammation and organ dysfunction in sepsis and trauma, and may help in the development of novel therapies and diagnostic strategies.