13 papers · ranked by Valyu relevance
Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and ‘-omics’…
Hua-Dong Xiong, Li Ji-An, Marcelo G. Mattar, Robert C. Wilson
Cognitive modeling provides a formal method to articulate and test hypotheses about cognitive processes. However, accurately and reliably estimating model parameters remains challenging due to common issues in behavioral science, such as limited data, measurement noise, experimental constraints, and model complexity.…
Daniel Kaschek, Wolfgang Mader, Mirjam Fehling-Kaschek, Marcus Rosenblatt + 1 more
In a wide variety of research elds, dynamic modeling is employed as an instrument to learn and understand complex systems. The differential equations involved in this process are usually non-linear and depend on many parameters whose values decide upon the characteristics of the emergent system. The inverse problem…
Harsh Chhajer, Rahul Roy
Quantitative experiments are essential for investigating, uncovering and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been…
César Parra-Rojas, Esteban A. Hernandez-Vargas
Partial differential equations (PDEs) is a well-established and powerful tool to simulate multi-cellular biological systems. However, available free tools for validation against data are not established. The PDEparams module provides flexible functionality in Python for parameter estimation in PDE models. The PDEparams…
Philipp H. Boersch-Supan, Leah R. Johnson
Mechanistic representations of individual life-history trajectories are powerful tools for the prediction of organismal growth, reproduction and survival under novel environmental conditions. Dynamic energy budget (DEB) theory provides compact models to describe the acquisition and allocation of energy by organisms…
Rodolfo Blanco-Rodriguez, Tanya A. Miura, Esteban Hernandez-Vargas
The integration of computational models with experimental data is a cornerstone for gaining insight into biomedical applications. However, parameter fitting procedures often require an immense availability and frequency of data that are challenging to obtain from a single source. Here, we present a novel methodology…
Ludger Starke, Dirk Ostwald
Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation…
Xin Yan, Goshi Ogita, Shuji Ishihara, Kaoru Sugimura
Cell-based mechanical models, such as the Cell Vertex Model (CVM), have proven useful for studying the mechanical control of epithelial tissue dynamics. We recently developed a statistical method called image-based parameter inference for formulating CVM model functions and estimating their parameters from image data…
Matthew J. Simpson, Michael J. Plank
Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability, and model prediction are well-developed…
Elba Raimúndez, Michael Fedders, Jan Hasenauer
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter uncertainties, and thereby the reliability of models and their predictions. Yet, generating representative samples from the Bayesian posterior distribution is often computationally…
Ronan Duchesne, Anissa Guillemin, Olivier Gandrillon, Fabien Crauste
Nonlinear mixed effects models provide a way to mathematically describe experimental data involving a lot of inter-individual heterogeneity. In order to assess their practical identifiability and estimate confidence intervals for their parameters, most mixed effects modelling programs use the Fisher Information Matrix.…
Daniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman + 55 more
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our…