23 papers · ranked by Valyu relevance
Bertrand Iooss, Paul Lemaître
This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smoothing, tests, statistical learning, Monte Carlo, . . . ) aim at determining the model input variables which mostly contribute…
Nate Bade, Lindsay Erickson
Sensitivity analysis is an important component of simulation-based decision support because it helps analysts determine which inputs most strongly influence model outcomes under uncertainty. This paper organizes the broad sensitivity analysis literature into a coherent framework for use in complex simulation settings…
Alon Korngreen
Markov models are widely used to describe the gating kinetics of voltage-gated ion channels, but increasing model complexity can introduce parameters that are difficult to constrain from macroscopic measurements. In this study, I used global variance-based Sobol sensitivity analysis to examine how model topology…
Simen Tennøe, Geir Halnes, Gaute T. Einevoll
Computational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods…
Max Floettmann, Jannis Uhlendorf, Till Scharp, Edda Klipp + 1 more
'Thomas W. Spiesser'] Summary: SensA is a web-based application for sensitivity analysis of mathematical models. The sensitivity analysis is based on metabolic control analysis, computing the local, global and time-dependent properties of model components. Interactive visualization facilitates interpretation of usually…
Sarah Iaquinta, Shahram Khazaie, Samer Albanna, Sylvain Fréour + 1 more
'Frédéric Jacquemin'] Title: ABSTRACT Experimental studies on the cellular uptake of nanoparticles (NPs), useful for the investigation of NP-based drug delivery systems, are often difficult to interpret due to the large number of parameters that can contribute to the phenomenon. It is therefore of great interest to…
Zhike Zi, Yanan Zheng, Ann E Rundell, Edda Klipp
Background It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software…
Marco S. Nobile, Vasco Coelho, Dario Pescini, Chiara Damiani
Background Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of…
Robert Rockenfeller, Michael Günther, Syn Schmitt, Thomas Götz
We mathematically compared two models of mammalian striated muscle activation dynamics proposed by Hatze and Zajac. Both models are representative for a broad variety of biomechanical models formulated as ordinary differential equations (ODEs). These models incorporate parameters that directly represent known…
Arnald Puy, Andrea Saltelli
While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the nonspecialist. Here we propose a sensitivity analysis approach based on the…
Yang Yang, Haiyan Liu, Qichun Zhang
The disease-information coupling propagation dynamics model is a widely used model for studying the spread of infectious diseases in society, but the parameter settings and sensitivity are often overlooked, which leads to enlarged errors in the results. Exploring the influencing factors of the disease-information…
P. Rangamani, A. Behzadan, M. Holst
The Helfrich energy is commonly used to model the elastic bending energy of lipid bilayers in membrane mechanics. The governing differential equations for certain geometric characteristics of the shape of the membrane can be obtained by applying variational methods (minimization principles) to the Helfrich energy…
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to…
Mariia Kozlova, Antti Ahola, Pamphile T. Roy, Julian Scott Yeomans
Models of complex technological systems inherently contain interactions and dependencies among their input variables that affect their joint influence on the output. Such models are often computationally expensive and few sensitivity analysis methods can effectively process such complexities. Moreover, the sensitivity…
Robert Rockenfeller, Michael Guenther, Syn Schmitt, Thomas Goetz
In this paper, we mathematically compared two models of mammalian striated muscle activation dynamics proposed by Hatze [8] and Zajac [27]. Both models are representative of a broad variety of biomechanical models formulated as ordinary differential equations (ODEs). The models incorporate some parameters that directly…
Danial Faghihi, Subhasis Sarkar, Mehdi Naderi, Lloyd A. Hackel + 1 more
'Nagaraja Iyyer'] In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncertainty in experimental data and computational model. The methodology involves (i) fatigue test data conducted on coupons of Ti6Al4V…
Peter J. Gawthrop, Michael Pan
The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing…
shriprakash sinha
Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials augmented by recent developments in advanced computational modeling of…
Itamar Borges Jr, Júlio César Duarte, Romulo Dias da Rocha
We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, ∑▒〖Q_0 (NO_2)〗 (the charge of the nitro groups)…
Authors not listed
Amines play a significant role in everyday life, and their detection remains a crucial focus in research and development. Although conducting polymer-based gas sensors have been widely reported for amine detection using DC resistivity measurements, they often lack selectivity to distinguish between intragroup…
Justus T. Metternich, Björn Hill, Janus A. C. Wartmann, Chen Ma + 4 more
Enzymatic reactions are used to detect analytes in a range of biochemical methods such as enzyme-linked immunosorbent assays (ELISAs). To measure the presence of an analyte, they are conjugated to a recognition unit and convert a substrate into a (colored) product that is detectable by visible (VIS) light. Thus, the…
Authors not listed
Single-walled carbon nanotubes (SWCNTs) are powerful building blocks for near-infrared (NIR) fluorescent biosensors. They can be chemically tailored to detect specific biomolecules. Their performance in (bio)imaging applications depends on how fast analytes bind and unbind and change their fluorescence but measuring…
Justus T. Metternich, Sujit K. Patjoshi, Tanuja Kistwal, Sebastian Kruss
Optical sensors/probes are powerful tools to identify and image (biological) molecules. Because of their optoelectronic properties, nanomaterials are often used as building blocks. Such nanosensors are assembled from an optically sensitive nanomaterial, a (biological) recognition unit, and linker chemistry that…