23 papers · ranked by Valyu relevance
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…
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…
Dong, Xinshuai, Ng, Ignavier + 12 more
| Xinshuai Dong | Ignavier Ng | Biwei Huang | Yuewen Sun | Songyao Jin | | --- | --- | --- | --- | --- | | CMU | CMU | UCSD | MBZUAI | MBZUAI | | Roberto Legaspi | | Peter Spirtes | Kun Zhang | | | KDDI Research | | CMU | CMU & MBZUAI | |
Yue Liu, Kevin Suh, Philip K. Maini, Daniel J. Cohen + 1 more
When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide ranges of unseen scenarios, as well as for understanding the underlying mechanisms. In this work, we use a profile-likelihood approach to investigate parameter…
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…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
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…
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, 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…
Sahil Bhola, Karthik Duraisamy
A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No…
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…
Helen M. Byrne, Heather A. Harrington, Alexey Ovchinnikov, Gleb Pogudin + 2 more
'Gleb Pogudin' 'Hamid Rahkooy' 'Pedro Soto'] Abstract. Differential equation models are crucial to scientific processes. The values of model parameters are important for analyzing the behaviour of solutions. A parameter is called globally identifiable if its value can be uniquely determined from the input and output…
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…
Sahil Bhola, Karthik Duraisamy
A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No…
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…
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…
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
Finding microkinetic parameters for heterogeneously catalyzed processes with conventional methods is a challenging task. Recently, the use of artificial neural networks has been described as a promising and flexible tool for kinetic parameter estimation. In this work, an extension to the methodology of chemical reaction…
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…