A5032919554- Gene Regulatory Network Analysis
- Microbial Metabolic Engineering and Bioproduction
- Bioinformatics and Genomic Networks
- Protein Structure and Dynamics
- Evolution and Genetic Dynamics
- Receptor Mechanisms and Signaling
- Teleoperation and Haptic Systems
- Advanced Control Systems Optimization
- Soft Robotics and Applications
- Fault Detection and Control Systems
- Control Systems and Identification
- Geophysics and Sensor Technology
- Adaptive Control of Nonlinear Systems
- Advanced Multi-Objective Optimization Algorithms
- Computational Drug Discovery Methods
- SARS-CoV-2 and COVID-19 Research
- Advanced Thermodynamics and Statistical Mechanics
- COVID-19 epidemiological studies
- thermodynamics and calorimetric analyses
- Robot Manipulation and Learning
- Viral Infectious Diseases and Gene Expression in Insects
- Mental Health Research Topics
- Chromatography in Natural Products
- Advanced Scientific Research Methods
- Gene expression and cancer classification
Centro de Supercomputación de Galicia
2022-2025
Universidade de Vigo
2014-2025
National Research Council
2020
Instituto de Investigacións Mariñas
2010-2019
University of Minho
2015-2017
Consejo Superior de Investigaciones Científicas
2011-2017
Science Oxford
2016
University of Oxford
2016
Institut de Ciències del Mar
2010
Stanford University
2010
A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such model called structurally identifiable if it possible to determine the values its parameters from measurements outputs. Structural identifiability prerequisite for parameter estimation, and should be assessed before exploiting model. However, this analysis seldom performed due high computational cost involved in necessary...
Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics particular have proven be most efficient methods for applications. Despite their utility, there limited availability of metaheuristic tools. We present MEIGO, an R Matlab toolbox (also available Python via wrapper version), that implements capable diverse arising systems biology bioinformatics. The includes enhanced scatter search...
Abstract Motivation Kinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. This task can be computationally challenging due presence of local optima and ill-conditioning. While a variety optimization methods have been suggested surmount these issues, it is difficult choose best one for given problem priori. A systematic comparison parameter estimation problems with tens hundreds variables currently missing, smaller studies provided...
The prediction of links among variables from a given dataset is task referred to as network inference or reverse engineering. It an open problem in bioinformatics and systems biology, well other areas science. Information theory, which uses concepts such mutual information, provides rigorous framework for addressing it. While number information-theoretic methods are already available, most them focus on particular type problem, introducing assumptions that limit their generality....
Kinetic models of biochemical systems usually consist ordinary differential equations that have many unknown parameters. Some these parameters are often practically unidentifiable, is, their values cannot be uniquely determined from the available data. Possible causes lack influence on measured outputs, interdependence among parameters, and poor data quality. Uncorrelated can seen as key tuning knobs a predictive model. Therefore, before attempting to perform parameter estimation (model...
Observability is a modelling property that describes the possibility of inferring internal state system from observations its output. A related property, structural identifiability, refers to theoretical determining parameter values In fact, identifiability becomes particular case observability if parameters are considered as constant variables. It possible simultaneously analyse and model using conceptual tools differential geometry. Many complex biological processes can be described by...
In this paper, we address the system identification problem in context of biological modelling. We present and demonstrate a methodology for (i) assessing possibility inferring unknown quantities dynamic model (ii) effectively estimating them from output data. introduce term Full Input-State-Parameter Observability (FISPO) analysis to refer simultaneous assessment state, input parameter observability (note that is also known as identifiability). This type has often remained elusive presence...
Reproducibility and reusability of the results data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for specification parameter estimation problems in systems biology. Here, we introduce PEtab, a which facilitates using Systems Biology Markup Language (SBML) models set tab-separated value files describing observation model experimental data as well parameters to be estimated. We already implemented PEtab support into eight well-established...
Abstract Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These typically have many unknown nonmeasurable parameters, which to be determined by fitting model experimental data. In order perform this task, known as parameter estimation or calibration, modeller faces challenges such poor identifiability, lack sufficiently informative data existence local minima in objective function landscape....
The theoretical possibility of determining the state and parameters a dynamic model by measuring its outputs is given structural identifiability observability. These properties should be analysed before attempting to calibrate model, but their priori analysis can challenging, requiring symbolic calculations that often have high computational cost. In recent years, number software tools been developed for this task, mostly in systems biology community. vastly different features capabilities,...
Dynamic modelling is one of the cornerstones systems biology. Many research efforts are currently being invested in development and exploitation large-scale kinetic models. The associated problems parameter estimation (model calibration) optimal experimental design particularly challenging. community has already developed many methods software packages which aim to facilitate these tasks. However, there a lack suitable benchmark allow fair systematic evaluation comparison contributions. Here...
Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models estimated by optimizing the fit model simulation and experimental data. resulting parameter estimates inevitably possess some degree uncertainty. In practical applications it is important to quantify uncertainties as well prediction uncertainty, which potentially time-dependent characteristics. Unfortunately, estimating accurately nontrivial, due nonlinear dependence...
Communication delays are problematic for teleoperated systems. They give rise to a tradeoff between speed and robustness, which cannot be overcome by means of linear controllers. In order solve this problem, in paper, we present novel approach that combines passivity-based techniques reset-control principles. way, it is possible obtain simultaneously the robust stability properties passive control performance improvement enabled reset strategies. Experimental simulation results presented,...
Building mathematical models of cellular networks lies at the core systems biology. It involves, among other tasks, reconstruction structure interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in goal extracting much information possible from available data. A large number methods founded on these concepts have been proposed literature, not only biology journals, but a wide range areas. Their critical comparison...
Mathematical models play a key role in systems biology: they summarize the currently available knowledge way that allows to make experimentally verifiable predictions. Model calibration consists of finding parameters give best fit set experimental data, which entails minimizing cost function measures goodness this fit. Most mathematical biology present three characteristics problem very difficult solve: are highly non-linear, have large number be estimated, and information content data is...
We review a coherent mesoscopic presentation of thermodynamics and fluctuations far from near equilibrium, applicable to chemical reactions, energy transfer transport processes, electrochemical systems. Both uniform spatially dependent systems are considered. The focus is on processes leading in non‑equilibrium stationary states; with multiple issues relative stability such states. establish thermodynamic state functions, the irreversible simple physical interpretations that yield work...
Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when objective to obtain a dynamic model capable predicting effect novel perturbations not considered during training. ill-posed due nonlinear nature these systems, fact that only fraction involved proteins their post-translational modifications can be measured, limitations on technologies used for growing cells in...
A dynamic model is structurally identifiable if it possible to infer its unknown parameters by observing output. Structural identifiability depends on the system dynamics, output, and input, as well specific values of initial conditions parameters. Here we present a symbolic method that characterizes input requires be identifiable. It determines which derivatives must non-zero in order have sufficiently exciting input. Our approach considers structural generalization nonlinear observability...