A5043481570- Greenhouse Technology and Climate Control
- Advanced Control Systems Optimization
- Control Systems and Identification
- Gene Regulatory Network Analysis
- Plant Water Relations and Carbon Dynamics
- Microbial Metabolic Engineering and Bioproduction
- Probabilistic and Robust Engineering Design
- Fault Detection and Control Systems
- Wildlife Ecology and Conservation
- Odor and Emission Control Technologies
- Irrigation Practices and Water Management
- Bioinformatics and Genomic Networks
- Model Reduction and Neural Networks
- Manufacturing Process and Optimization
- Flexible and Reconfigurable Manufacturing Systems
- Wastewater Treatment and Nitrogen Removal
- Neural Networks and Applications
- Rangeland Management and Livestock Ecology
- Scheduling and Optimization Algorithms
- Fluid Dynamics and Vibration Analysis
- Numerical methods for differential equations
- Lepidoptera: Biology and Taxonomy
- Conservation, Biodiversity, and Resource Management
- Energy, Environment, Agriculture Analysis
- Receptor Mechanisms and Signaling
Wageningen University & Research
2014-2023
Swedish University of Agricultural Sciences
2015
Delft University of Technology
1992-2005
KU Leuven
1999-2001
National Postdoctoral Association
2001
University of Georgia
1994
Abstract For a reconstruction of state and parameter values in dynamic system model, first the question whether these can be uniquely determined from data must answered. This structural model property is known as observability or, case calibration only, identifiability . Testing given for well studied problem systems control sciences. However, it increasingly difficult, if not impossible, to address this large size models that, nowadays, are frequently used. We demonstrate application...
Abstract Brassinosteroid (BR) signaling is essential for plant growth and development. In Arabidopsis (Arabidopsis thaliana), BRs are perceived by the BRASSINOSTEROID INSENSITIVE1 (BRI1) receptor. Root hypocotyl elongation convenient downstream physiological outputs of BR signaling. A computational approach was employed to predict root solely on basis BRI1 receptor activity. The developed mathematical model predicts that during normal growth, few receptors occupied with ligand. faithfully as...
An efficient method that assists in the re-parametrization of structurally unidentifiable models is introduced. It significantly reduces computational demand by combining numerical and symbolic identifiability calculations. This hybrid approach facilitates large ordinary differential equation models, including where state transformations are required. A model first assessed numerically, to discover potential parameters. We then use calculations confirm results, after which we describe...
Abstract The problem of optimal input design (OID) for a fed‐batch bioreactor case study is solved recursively. Here an adaptive receding horizon control problem, involving the so‐called E‐criterion, “on‐line,” using current estimate parameter vector θ at each sample instant {t k , = 0, …, N − h}, where marks end experiment and h which solved. feed rate F (t ) thus obtained applied observation y(t k+1 that becomes available subsequently used in recursive prediction error algorithm to find...
Multiple factors determine diet selection of herbivores. However, in many studies single nutrients is studied or optimization models are developed using only one currency. In this paper, we use linear programming to explain by African elephant based on plant availability and nutrient deterrent content over time. Our results indicate that at our study area maximized intake phosphorus throughout the year, possibly response deficiency region. After adjusting model incorporate effects...
State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in dynamical system given the sensor measurements related states. They can be used to derive fast accurate estimates that cannot measured directly (‘soft sensing’) or which only noisy, intermittent, delayed, indirect, unreliable available, perhaps from multiple sources (‘sensor fusion’). In this paper, we introduce concepts main methods state estimation review recent...
Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of may be hard to estimate from data, whereas others not. One might expect that parameter uncertainty automatically leads uncertain predictions, but this is not the case. We illustrate by showing prediction each six sloppy varies enormously among different predictions. Statistical approximations lead dramatic errors estimation. argue assessment must therefore performed on a per-prediction basis...
We present and apply an alternative method for the investigation of well-known parameter identifiability question non-linear system models. The is based on a geometric analysis parametric output sensitivities is, in fact, application tools that are available control theory to augmented system, including sensitivities. Accessibility Lie algebras calculated yield insight (through simple rank test) controllability this system. demonstrated example due Dochain et al [4]. Results confirmed by has...
Flavonoids are secondary metabolites present in all terrestrial plants. The flavonoid pathway has been extensively studied, and many of the involved genes have described literature. Despite this extensive knowledge, functioning vivo is still poorly understood. Here, we study using both experiments mathematical models. We measured metabolite dynamics two tissues, hypocotyls cotyledons, during tomato seedling development. Interestingly, same backbone interactions leads to very different...
The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection time consuming and costly. This paper covers both these issues by addressing following question: "Which outputs should measured to ensure that unique model parameters calculated?". Stated formally, we examine topic minimal output sets guarantee model's structural identifiability. To end, introduce an algorithm guides researcher as which measure. Our consists iterative...
Abstract Most models of environmental systems are based on sets differential equations. The paper investigates the problem identifying number and form appropriately parameterized terms in such continuous‐time state‐space models, a referred to as model structure identification. Filtering theory (recursive estimation) is used an approach solution this problem. Central notion that patterns (posterior) trajectories model's parameters, when contrasted with prior assumptions about their expected...