23 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…
Preston, Simon P., Wilkinson, Richard D. + 6 more
Reliable predictions from systems biology models require knowing whether parameters can be estimated from available data, and with what certainty. Identifiability analysis reveals whether parameters are learnable in principle (structural identifiability) and in practice (practical identifiability). We introduce the…
Dasuni Amanda Salpadoru, Matthew P. Adams, Kate Helmstedt, David J. Warne
Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is…
Mark P. Little, Wolfgang F. Heidenreich, Guangquan Li, Fabio Rapallo
Background Models for complex biological systems may involve a large number of parameters. It may well be that some of these parameters cannot be derived from observed data via regression techniques. Such parameters are said to be unidentifiable, the remaining parameters being identifiable. Closely related to this idea…
Kyle Nguyen, Kan Li, Kevin Flores, Georgia D. Tomaras + 2 more
Discovery research for therapeutic antibodies and vaccine development requires an in-depth understanding of antibody-antigen interactions. Label-free techniques such as Surface Plasmon Resonance (SPR) enable the characterization of biomolecular interactions through kinetics measurements, typically by binding antigens…
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…
Shun Wang, Wenrui Hao
Practical identifiability is a fundamental challenge in the data-driven modeling of biological systems, as many model parameters cannot be directly measured and must be estimated from experimental data. Without confirming the identifiability of these parameters, model predictions may be unreliable, limiting their…
Shun Wang, Wenrui Hao
Practical identifiability is a critical concern in data-driven modeling of mathematical systems. In this paper, we propose a novel framework for practical identifiability analysis to evaluate parameter identifiability in mathematical models of biological systems. Starting with a rigorous mathematical definition of…
Matthew J. Simpson
Many mathematical models in the field of theoretical biology involve challenges relating to parameter identifiability. Non-identifiability implies that different combinations of parameter values lead to indistinguishable solutions of the mathematical model. This means that it is difficult, and sometimes impossible, to…
Marisa C. Eisenberg, Michael A. L. Hayashi
Identifiability is a necessary condition for successful parameter estimation of dynamic system models. A major component of identifiability analysis is determining the identifiable parameter combinations, the functional forms for the dependencies between unidentifiable parameters. Identifiable combinations can help in…
Alexey Ovchinnikov, Gleb Pogudin, Peter L. Thompson
Structural parameter identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equationsin the absence of noise. One of the standard approaches to assessing this problem is via input-output equations and, in particular, characteristic sets of…
Olivia Walch, Marisa C. Eisenberg
The use of Hodgkin-Huxley (HH) equations abounds in the literature, but the identifiability of the HH model parameters has not been broadly considered. Identifiability analysis addresses the question of whether it is possible to estimate the model parameters for a given choice of measurement data and experimental…
Linda Wanika, Joseph R. Egan, Nivedhitha Swaminathan, Carlos A. Duran-Villalobos + 3 more
Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown…
Shun Wang, Wenrui Hao
Practical identifiability is a fundamental challenge in the data-driven modeling of biological systems, as many model parameters cannot be directly measured and must be estimated from experimental data. Without confirming the identifiability of these parameters, model predictions may be unreliable, limiting their…
Wim J. van der Linden, Michelle D. Barrett
With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of test equating scores on different test forms. This paper argues, however, that the use of item response models does not require any test score equating.…
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…
Authors not listed
This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…
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…
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…
Arsalan Rahimabadi, Habib Benali
Parameterization and a priori identifiability analysis are two interconnected steps that should be carried out in advance of model calibration. In the first place, we propose a framework for parameterizing a recently introduced and analytically studied generalization of the celebrated…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…