Back to basics for Bayesian model building in genomic selection

Numerous Bayesian methods of phenotype prediction and genomic breeding value estimation based on multilocus association models have been proposed. Computationally the methods have been based either on Markov chain Monte Carlo or on faster maximum a posteriori estimation. The demand for more accurate and more efficient estimation has led to the rapid emergence of workable methods, unfortunately at the expense of well-defined principles for Bayesian model building. In this article we go back to the basics and build a Bayesian multilocus association model for quantitative and binary traits with carefully defined hierarchical parameterization of Student's t and Laplace priors. In this treatment we consider alternative model structures, using indicator variables and polygenic terms. We make the most of the conjugate analysis, enabled by the hierarchical formulation of the prior densities, by deriving the fully conditional posterior densities of the parameters and using the acquired known distributions in building fast generalized expectation-maximization estimation algorithms.     

Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations

We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO₂ (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo‐Transpiration model (SIPNET). SIPNET runs at a half‐daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub‐model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations.     

Development of a versatile computer integrated control system for bioprocess controls

A general approach is described for the implementation of a networked multi-unit computer integrated control system. The use of data acquisition hardware and graphical programming tools alleviates tedious programming and maintains potency and flexibility. One application of the control system, the control of a mammalian cell perfusion culture based on a key nutrient glucose concentration, was demonstrated. The control system offers customized user interface for all process control parameters and allows the flexibility for continued improvement and implementation of new tailored functions. The temperature, pH, dissolved oxygen and glucose level were accurately controlled. This revised version was published online in June 2006 with corrections to the Cover Date.