The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.

A model-based predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation. The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model-based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.

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Summary Asymmetric cell divisions are a fundamental feature of neural development, and misregulation can lead to brain abnormalities or tumor formation. During an asymmetric cell division, molecular determinants are segregated preferentially into one daughter cell to specify its fate. An important goal is to identify the asymmetric determinants in neural progenitor cells, which could be tumor suppressors or inducers of specific neural fates. Here we show that the double-stranded RNA-binding protein Stau2 is distributed asymmetrically during progenitor divisions in the developing mouse cortex, preferentially segregating into the Tbr2+ neuroblast daughter, taking with it a sub-set of RNAs. Knockdown of Stau2 stimulates differentiation and over-expression produces periventricular neuronal masses, demonstrating its functional importance for normal cortical development. We immunoprecipitated Stau2 to examine its cargo mRNAs, and found enrichment for known asymmetric and basal cell determinants, such as Trim32, and identified novel candidates, including a subset involved in primary cilium function.