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Experimental techniques in molecular biology have led to the production of enormous amounts of data on the dynamics of cellular processes. The availability of time series data characterizing genomic, proteomic and metabolic systems must be complemented wit ...
When a phenomenon is described by a parametric model and multiple datasets are available, a key problem in statistics is to discover which datasets are characterized by the same parameter values. Equivalently, one is interested in partitioning the family o ...
In this paper we consider a nonlinear constrained system observed by a sensor network and propose a distributed state estimation scheme based on Moving Horizon Estimation (MHE). In order to embrace the case where the whole system state cannot be reconstruc ...
We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [Porreca et al,Bioinformatics,2010], we described a method for the structural and parametric identific ...
Modern experimental techniques for the quantitative, time-course measurement of protein concentrations and gene activation levels enable the identification of dynamical models of genetic regulatory networks. In general, identification involves fitting appr ...
This paper presents three novel Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting ...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensor to the correct state even when constraint ...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensor to the correct state even when constraint ...
We discuss the identification of genetic networks based on a class of boolean gene activation rules known as hierarchically canalizing functions. We introduce a class of kinetic models for the concentration of the proteins in the network built on a family ...
This paper focuses on distributed state estimation using a sensor network for monitoring a linear system. In order to account for physical constraints on process states and inputs, we propose a moving horizon approach where each sensor has to solve a quadr ...
