Frank Doyle

Analysis of Post-Upset Plant Data for Fault Detection

Abstract A reboiler attached to a debutanizer column allowed lighter than normal hydrocarbons in a stream supplying equipment not designed to handle the light hydrocarbons. Models of pieces of plant equipment were developed based on frrst principles and simple mathematical representations of valve behavior. The models were capable of indicating fault periods and of indicating equipment that was not functioning properly. This is the first step in a fault detection system designed for use in an actual chemical plant.

A Multivariate Ensemble Approach for Identification of Biomarkers: Application to Breast Cancer

Abstract Advances in high throughput screening experiments have significantly improved our ability to discover and predict biomarkers for complex diseases. Systems biology approaches have played a critical role in realizing these improvements by providing computational tools for modeling such diseases at the network level. Within these tools, statistical scores such as the two sample t-statistic (t-score) are commonly used to rank genes/features for downstream analyses. In this paper, we propose a new alternative to the t-score —the ensemble sensitivity (ES) metric —which is a multivariate strategy to obtain feature rankings. To validate our method, we employ the COre Module Biomarker Identification with Network Exploration (COMBINER) tool on publicly available breast cancer gene expression data sets. Top candidates obtained by both the t-score and ES method serve as an input to COMBINER, which identifies the candidate biomarkers. Our results, as quantified by the COMBINER-generated area under the ROC curve (AUC), suggest that the ES approach improves the average AUC and identifies biomarkers with ~ 93% overlap with known cancer-related genes. In addition, the overlap of genes known to be associated with cancer that are identified using the two methods is small. This suggests that our proposed approach captures signals missed by methods relying on the t-score.

Education, National Programs, and Infrastructure in Systems Biology

The future of systems biology will depend on three critical elements: education of a new generation of scientists who have both biological and mathematical training; the availability of funding that operates outside of disciplinary boundaries; and the availability of a supportive infrastructure that can accommodate the needs of an intrinsically interdisciplinary research area. This chapter will consider the status of these issues both in the U.S. and elsewhere.