Sensitivity Analysis by Design of Experiments

Schepdael, An Van; Carlier, Aurélie; Geris, Liesbet

doi:10.1007/978-3-319-21296-8_13

Cited by 25 publications

(14 citation statements)

References 32 publications

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“…As more and more parameters become determined experimentally, and the sensitivity of a given parameter is observed both in silico and in vitro, sensitivity analyses would increasingly gain value in how they help translate the results of the small scale assays to identify targets for therapeutic intervention. Sensitivity analyses, and uncertainty quantification (UQ) more generally, are the rough analogs, for modeling and simulation, of statistical analyses for experimental assays …”

Section: Resultsmentioning

confidence: 99%

Mathematics as a conduit for translational research in post‐traumatic osteoarthritis

Ayati

Kapitanov

Coleman

et al. 2016

Journal Orthopaedic Research

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Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a “conduit of translation”. The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge.

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Section: Resultsmentioning

confidence: 99%

Mathematics as a conduit for translational research in post‐traumatic osteoarthritis

Ayati

Kapitanov

Coleman

et al. 2016

Journal Orthopaedic Research

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“…The DOE method consists of three steps: (1) determine a suitable experimental design, (2) perform the experiment with the proposed factor combinations, and (3) analyze the results and draw conclusions. 129 A number of experimental designs are available; here we will focus on one-at-a-time analysis, full and fractional factorial designs to illustrate the concepts of DOE. We refer the interested reader to more dedicated and advanced resources for more details.…”

Section: Materiobiologymentioning

confidence: 99%

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

et al. 2021

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The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.

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“…'Design of Experiments' (discussed by Van Schepdael et al [16]) is a technique originally developed to optimize physical experiments allowing to comprehensively determine the effect of parameters settings (individual parameters and their interactions) on the process with a minimal amount of experimental runs. For computational models, the limits on the specific parameter values that can be tested and the amount of runs that can be executed are generally less stringent but the amount of parameters (and especially their interactions) might be considerably higher than for physical experiments.…”

Section: Sensitivity Analysis and Model Adaptationmentioning

confidence: 99%

“…The design of experiment approach for computational models allows choosing a minimum amount of parameter combinations that will result in a maximum amount of information about the computational model. The chapter by Van Schepdael et al [16] explains several designs and analysis methodologies.…”

Section: Sensitivity Analysis and Model Adaptationmentioning

confidence: 99%

“…The structure of this chapter (and the whole book) follows that of the model development life cycle, starting with model establishment over parameter optimisation and model adaptation and ending with model prediction. [6]; C3, F2 [8]; C4, F1 [9]; C6, F4 [11]; C9, F3 [7]; C10, F5 [14]; C11, F5 [15]; C12, F1 [17]; C13, F16 [16]; C16, F3 [20]; C16, F3 [20]; C17, F3 [21].…”

Section: Introductionmentioning

confidence: 99%

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An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

Geris

Gomez-Cabrero

2015

Uncertainty in Biology

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This chapter aims to provide an introduction to the different ways in which uncertainty can be dealt with computational modelling of biological processes. The first step is model establishment under uncertainty. Once models have been established, data can further be used to select which of the proposed models best meets the predefined criteria. Subsequently, parameter values can be optimized for a specific model configuration. Sensitivity analyses allow to assess the influence of the previous choices on the model output. Additionally, model adaptation permits to focus on specific aspects of the model without losing its global predictive capacity. Finally, predictions with the established models should also consider the effect of uncertainty in the model development process.

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Sensitivity Analysis by Design of Experiments

Cited by 25 publications

References 32 publications

Mathematics as a conduit for translational research in post‐traumatic osteoarthritis

Mathematics as a conduit for translational research in post‐traumatic osteoarthritis

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

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