25 papers · ranked by Valyu relevance
Oana-Teodora Chis, Julio R. Banga, Eva Balsa-Canto, Johannes Jaeger
Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be…
Franz-Georg Wieland, Adrian L. Hauber, Marcus Rosenblatt, Christian Tönsing + 1 more
'Christian Tönsing' 'Jens Timmer'] We discuss issues of structural and practical identifiability of partially observed differential equations which are often applied in systems biology. The development of mathematical methods to investigate structural non-identifiability has a long tradition. Computationally efficient…
David L. I. Janzén, Linnéa Bergenholm, Mats Jirstrand, Joanna Parkinson + 3 more
'Joanna Parkinson' 'James Yates' 'Neil D. Evans' 'Michael J. Chappell'] Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output…
Martina Conte, Ryan T. Woodall, Margarita Gutova, Bihong T. Chen + 4 more
Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of…
Yuganthi R. Liyanage, Nora Heitzman-Breen, Necibe Tuncer, Stanca M. Ciupe
'Stanca M. Ciupe'] Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model’s ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is…
Gemma Massonis, Alejandro F. Villaverde, Julio R. Banga
Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in…
Yuganthi R. Liyanage, Omar Saucedo, Necibe Tuncer, Gerardo Chowell
Structural identifiability is the theoretical ability to uniquely recover model parameters from ideal, noise-free data and is a prerequisite for reliable parameter estimation in epidemic modeling. Despite its relevance in model calibration and inference, structural identifiability analysis remains underused and…
Mario Castro, Rob J. de Boer
Successful mathematical modeling of biological processes relies on the expertise of the modeler to capture the essential mechanisms in the process at hand and on the ability to extract useful information from empirical data. The very structure of the model limits the ability to infer numerical values for the…
Yuganthi R. Liyanage, Gerardo Chowell, Gleb Pogudin, Necibe Tuncer
Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters' structural and practical identifiability. In this study, we…
Yuganthi R. Liyanage, Gerardo Chowell, Gleb Pogudin, Necibe Tuncer
Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we…
Yuganthi R. Liyanage, Omar Saucedo, Necibe Tuncer, Gerardo Chowell
Structural identifiability—the theoretical ability to uniquely recover model parameters from ideal, noise-free data—is a prerequisite for reliable parameter estimation in epidemic modeling. Despite its importance, structural identifiability analysis remains underutilized in the infectious disease modeling literature.…
Eivind S. Haus, Tormod Drengstig, Kristian Thorsen, Attila Csikász-Nagy
'Attila Csikász-Nagy'] Controller motifs are simple biomolecular reaction networks with negative feedback. They can explain how regulatory function is achieved and are often used as building blocks in mathematical models of biological systems. In this paper we perform an extensive investigation into structural…
Marisa C. Eisenberg
Structural identifiability for parameter estimation addresses the question of whether it is possible to uniquely recover the model parameters assuming noise-free data, making it a necessary condition for successful parameter estimation for real, noisy data. One established approach to this question for nonlinear…
Yuganthi R. Liyanage, Nora Heitzman-Breen, Necibe Tuncer, Stanca M. Ciupe
'Stanca M. Ciupe'] Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model’s ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is…
Yuganthi R. Liyanage, Nora Heitzman-Breen, Necibe Tuncer, Stanca M. Ciupe
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model’s ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for…
Johannes Borgqvist, Alexander P. Browning, Fredrik Ohlsson, Ruth E. Baker
symmetries Authors: ['Johannes Borgqvist' 'Alexander P. Browning' 'Fredrik Ohlsson' 'Ruth E. Baker'] A key initial step in mechanistic modelling of dynamical systems using first-order ordinary differential equations is to conduct a global structural identifiability analysis. This entails deducing which parameter…
Peter Thompson, Benjamin Jan Andersson, Gunnar Cedersund
Practical identifiability analysis – to determine whether a model property can be determined from given data – is central to model-based data analysis in biomedicine. The main approaches used today all require that the coverage of the parameter space be exhaustive, which is usually not possible. An attractive…
Jessica R. Conrad, Marisa C. Eisenberg
identifiability Authors: ['Jessica R. Conrad' 'Marisa C. Eisenberg'] - We provide an alternative method to constrain existing models using novel input data streams - The structural identifiability of nonlinear ODE models is not worsened with the introduction of a forcing input function (new data stream) - The inclusion…
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Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
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Traditional and non-classical machine learning models for solid-state structure prediction have predominantly relied on compositional features (derived from properties of constituent elements) to predict the existence of structure and its properties. However, the lack of structural information can be a source of…
Felix Bänsch, Jonas Schaub, Betül Sevindik, Samuel Behr + 3 more
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the…
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When producing plant-protein-based meat analogues via high moisture extrusion (HME), the structure of extrudates is determined by complex interactions between ingredient composition and processing conditions. To facilitate consumers in their transition towards a diet higher in plant-based proteins, the food industry…
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Metal–organic frameworks (MOFs) feature a rich structural diversity, including crystalline, amorphous, and liquid phases of varying topologies. Their structural characterization is often performed either at the local scale (through pair distribution functions, bond angle distributions, etc.) or, for crystalline phases…
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Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how…
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Heterogeneous and electrocatalysts play a crucial role in enabling various industrial chemical transformations, with quantum chemistry calculations serving as a fundamental tool for investigating their atomic-scale properties. Advances in computational power have facilitated the study of increasingly complex catalytic…