In this paper, a variable gain design approach for the high-gain disturbance observer, called Proportional-Integral-Observer (PI-Observer), is proposed to solve the problem of choosing suitable observer gains. The high-gain PI-Observer is successfully applied to estimate unknown inputs of...

The estimation of the solution of a system of two differential equations introduced by Norton et al. [Predicting the course of Gompertzian growth, Nature 264 (1976) 542–544] that is equivalent to the famous Gompertz growth law is performed by means of the recent adaptive scheme of Besançon...

We study the problem of finding necessary and sufficient conditions that guarantee global uniqueness of equilibria in a pure exchange economy. We show that for every economy to have a unique equilibrium it is necessary and sufficient that (i) there are no critical economies and (ii) a compact...

Toggle switch as a common phenomenon in gene regulatory networks has been recognized important for biological functions. Despite much effort dedicated to understanding the toggle switch and designing synthetic biology circuit to achieve the biological function, we still lack a comprehensive...

Transcriptional regulation is subject to significant stochasticity due partly to the random waiting times among synthesis and degradation reactions involving a finite collection of transcripts. Additional stochasticity is attributable to the random transitions among the discrete operator states...

We propose a suite of statistical methods for inferring a cis-regulatory module, which is a combination of several transcription factors binding in the promoter regions to regulate gene expression. The approach is an integrative analysis that combines information from multiple types of...

There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for followup...

The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression....

In many scientific and medical settings, large-scale experiments are generating large quantities of data that lead to inferential problems involving multiple hypotheses. This has led to recent tremendous interest in statistical methods regarding the false discovery rate (FDR). Several authors...