| Description | Participants | Agenda | Summary | Talks | Products |
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Presentations for NIMBioS Investigative Workshop:
Modeling Johne's Disease, July 6-8, 2011
July 6 (Wed)
Welcome to JD Workshop, Shigetoshi Eda (9:00 am-)
Title: Welcome to the Johne’s disease modeling workshop
Abstract: The objectives, schedule, and expected outcomes of the workshop will be introduced.
Overview of JD and JDIP, Vivek Kapur (9:45 am-)
Title: Overview of Johne’s disease and JDIP ~Where we are in Johne’s disease research (tentative)
Abstract: The following topics will be covered in this talk.
I. Johne’s disease: What is Johne’s disease – hosts, causative agent, pathogenesis, symptoms; Impact of Johne’s disease to dairy industry; Potential threat to human health; Transmission and prevention strategies; Recent progress in JD research and opportunities for mathematical modeling
II. JDIP: Brief history of JDIP; Organizational structure; JDIP activities; Resources – biological specimen for immunology research and database for epidemiology
JD Immunology (1), Eiichi Momotani (10:30 am-)
Title: Immunology of Johne’s Disease ~ Invasion, Granuloma formation, Cytokine expression
JD Immunology (2), Srinand Sreevatsan (11:15 am-)
Title: Gene expression profile of MAP and its association with persistence of infection (tentative)
JD Immunology (3), Judith Stabel (1:30 pm-)
Title: Shifting Sands: Transition in Th1-Th2 Host Immunity During Mycobacterial avium subsp. paratuberculosis Infection
Immunology Modeling (1), Yoram Louzoun (2:15 pm-)
Title: Prediction and validation of Mycobacterium CD8 T cell epitope selection and their effect on disease progression
Immunology Modeling (2), Zhou Wen (3:15 pm-)
Title: Modeling MHC class II mediated immunity in infectious diseases – Th1-Th2 switch, granuloma formation, and the immune switch during infection of MAP
Abstract: MHC class II mediated immunity, which is managed by CD4+ T cells, plays essential role in immune response against infectious diseases. Motivated by Dr. Zinkernagel's postulate that the immune response is determined by the dynamics of antigen load, we developed a selfregulated functional mathematical framework to model MHC class II mediated immune response. We have considered terms that broadly describe intercellular communication, cell movement, and effector function (activation or inhibition). To recapitulate realistic scenarios, we carefully model immune cells' movement, B cell activation in the lymph nodes, helper T cell subtype activation and switch, and various type of activation of professional phagocytes. A framework to capture distinct types of pathogens is also introduced based on the secretion rate of soluble antigens, replication rate of particulate antigens, resistance of an antigen to be effectively processed by immune agents and capacity of intracellular antigens. We demonstrate using numerical simulations that the model can successfully respond to broad classes of pathogens. A highly skewed Th1 response is generated against some virtual pathogens (e.g. those modeled after Mycobacterium tuberculosis, Leishmania major etc.) and granuloma formation is observed, other virtual pathogens lead to an unskewed or mixed response (e.g. such as Leishmania mexicana etc.) and some virtual pathogens lead to a Th1 to Th2 switch (modeled after M. avium paratuberculosis), and a Th2 responses is generated against sole extracellular pathogens (e.g. parasitic worms such as nippostrongylus etc.). Both acute and chronic infections are handled by our system with realistic responses.
July 7 (Thu)
Opening remarks for JD epidemiology, Ynte Schukken (9:00 am-)
Title: History and perspectives of JD epidemiology modeling Recent findings in JD epidemiology, Scott Wells (9:30 am-)
Title: Field data – risk factors of MAP transmission, economic impact of JD on dairy business (tentative)
JD epidemiology modeling (1), Henri Seegers (10:30 am-)
Title: Modeling the spread of Map with considering indirect transmission and contact structure in small-medium-sized dairy herds
Abstract: Several Mycobacterium avium subspecies paratuberculosis (Map) transmission routes in a ruminant herd have been described. Within a herd, animals are heterogeneous in terms of susceptibility and infectivity (mainly young animals are susceptible and adults infectious). Moreover, a dairy cattle herd, even if of small size, provides an heterogeneous and by-man managed contact structure, due to gathering animals into groups according to age. A stochastic compartmental model of Map spread in a small to medium-sized dairy herd was developed. Six infection states (susceptible, resistant, transiently infectious, latently infected, subclinically infected, and clinically affected) were defined. Indirect transmission via the environment was modeled, based on two contaminated environments (whole farm and calf area). Contact structure was explicitly modeled by representing calf and young stock housing and management (individual outdoor hutches, individual indoor pens, group indoor pens or pasture groups). According to housing facilities, exposure of calves to a farm environment contaminated by adults was null (outdoor hutches) or possible indirectly through fomites. Contacts between calves before weaning did not influence Map transmission, whereas the level and the age of beginning of exposure of calves to an environment contaminated by adults were critical factors and should thus be seen as priority targets for better control.
JD epidemiology modeling (2), Rebecca Mitchell (11:15 am-)
Title: Parameterizing MAP models with age- and dose-dependent early shedding and calf-to-calf transmission
Abstract: We have been working with historical data from experimental infections of calves with controlled doses of MAP. However, real doses in the field may not be reflected in the experimental dose range. In this presentation I would like to show what we have done so far in integrating age-and dose-dependent shedding into models of MAP transmission. Then I would like to discuss options for creating plausible model output while integrating age-dependent contact rates and likely contribution of infectious material from animals in each infection state.
Epidemiology-Immunology interface (1), Graham Medley (13:30 pm-)
Title: Animal movement between herds: the metapopulation dynamics of JD (PDF)
Abstract: In this talk I will raise the issues of different scales (cells, animals, herds) from which JD can be considered. In particular I will present recent information on the distribution of antibodies to MAP in UK cattle herds (Carslake et al, 2011). I will also make comparison with bovine tuberculosis epidemiology, and the patterns of diagnoses within and between herds. I believe that the general conclusion is that animal movement between herds is centrally important for understanding the epidemiology of infection and disease.
** Carslake et al., (2011) Endemic Cattle Diseases: comparative epidemiology and governance. Philosophical Transactions of the Royal Society of London, Series B 366, 1975-1986. doi: 10.1098/rstb.2010.0396
Epidemiology-Immunology interface (2), Maia Martcheva (14:15 pm-)
Title: Linking Immunology and Epidemiology in Mathematical Models: Application to Johne's Disease
Abstract: In this talk I will discuss the nested approach for linking immunological and epidemiological models. This approach was first introduced by Gilchrist and Sasaki. The approach links immunological and epidemiological models via two mechanisms: (1) the timesince- infection structural variable, and (2) relationship between the population-level parameters (such as transmission and virulence) and the immunological dynamical variables (such as pathogen load and immune response). I will first illustrate the concept on a general immunoepidemiological model. In the second part of the talk I will discuss application of the nested approach to immune-epidemiological modeling of Johne's disease. To introduce the immunoepidemiological model, first I will introduce an immunological model of the interplay of MAP with the immune system. The model captures the switching between the T-cell immune response and the antibody immune response with the corresponding consequences for the bacterial load. The epidemiological model is a simple SI age-since-infection epidemic model that is linked to the immunological variables through relationships between the population-level parameters and immunological dynamical variables. Finally, I discuss some advantages and disadvantages of the proposed approach.
July 8 (Fri)
Optimal Control - systems analysis, Suzanne Lenhart (9:00 am-)
Title: Generic lecture of optimal control with MAP examples (tentative)
Next steps, Ynte Schukken (11:15 am-) Summary of the workshop and discussion of next steps
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