A tentative taxonomy for predictive models in relation to their falsifiability

Viceconti, Marco

doi:10.1098/rsta.2011.0227

Cited by 14 publications

(9 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, a key use for multi‐resolution modeling is generating fast and accurate predictions, and the focus is on inductive and deductive reasoning. Our interest is different: models and methods were needed to enable mechanistic exploration and subsequent development of improved explanations of phenomena including improved phenotype overlap (Figure ). Improving insight requires shifting the aspect of focus of new virtual experiments, i.e., resolution tuning, in ways not easily anticipated.…”

Section: Evolving Principles For Building Tuneable Resolution Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models

Kirschner

Hunt

Marino

et al. 2014

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g. multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work.

show abstract

Section: Evolving Principles For Building Tuneable Resolution Modelsmentioning

confidence: 99%

“…Improving insight requires shifting the aspect of focus of new virtual experiments, i.e., resolution tuning, in ways not easily anticipated. Those activities require expanding the range of reasoning methods used to include analogical and abductive reasoning . (Figure ).…”

Section: Evolving Principles For Building Tuneable Resolution Modelsmentioning

confidence: 99%

Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models

Kirschner

Hunt

Marino

et al. 2014

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

show abstract

“…For phenomenological models, such as machine learning, applicability is framed in term of the generalisation error [17]. For mechanistic models it is related to the concept of "limit of Validity" of the theory used to develop the model [18].…”

Section: )! Applicabilitymentioning

confidence: 99%

Credibility of In Silico Trial Technologies—A Theoretical Framing

Viceconti

Juárez

Curreli

et al. 2020

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

show abstract

“…(Patterson and Whelan, 2017 ) describe a broad concept of validation of biological models, which includes but is more expansive than the engineering/DoD understanding of validation. (Viceconti, 2011 ) refer to model “falsification,” rather than validation, based on the contention that models can only be invalidated (falsified). One common feature of most of the different interpretations of validation is that validation must involve new data not used in the construction of the model, i.e., “calibration is not validation” (Roache, 2009 ).…”

Section: Why Trust a Computational Model?mentioning

confidence: 99%

Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology

Pathmanathan

Gray

2018

Front. Physiol.

View full text Add to dashboard Cite

Computational models of cardiac electrophysiology have a long history in basic science applications and device design and evaluation, but have significant potential for clinical applications in all areas of cardiovascular medicine, including functional imaging and mapping, drug safety evaluation, disease diagnosis, patient selection, and therapy optimisation or personalisation. For all stakeholders to be confident in model-based clinical decisions, cardiac electrophysiological (CEP) models must be demonstrated to be trustworthy and reliable. Credibility, that is, the belief in the predictive capability, of a computational model is primarily established by performing validation, in which model predictions are compared to experimental or clinical data. However, there are numerous challenges to performing validation for highly complex multi-scale physiological models such as CEP models. As a result, credibility of CEP model predictions is usually founded upon a wide range of distinct factors, including various types of validation results, underlying theory, evidence supporting model assumptions, evidence from model calibration, all at a variety of scales from ion channel to cell to organ. Consequently, it is often unclear, or a matter for debate, the extent to which a CEP model can be trusted for a given application. The aim of this article is to clarify potential rationale for the trustworthiness of CEP models by reviewing evidence that has been (or could be) presented to support their credibility. We specifically address the complexity and multi-scale nature of CEP models which makes traditional model evaluation difficult. In addition, we make explicit some of the credibility justification that we believe is implicitly embedded in the CEP modeling literature. Overall, we provide a fresh perspective to CEP model credibility, and build a depiction and categorisation of the wide-ranging body of credibility evidence for CEP models. This paper also represents a step toward the extension of model evaluation methodologies that are currently being developed by the medical device community, to physiological models.

show abstract

A tentative taxonomy for predictive models in relation to their falsifiability

Cited by 14 publications

References 51 publications

Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models

Tuneable resolution as a systems biology approach for multi‐scale, multi‐compartment computational models

Credibility of In Silico Trial Technologies—A Theoretical Framing

Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology

Contact Info

Product

Resources

About