Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways

Abstract

Computational modelling of biochemical reaction pathways is an increasingly important part of neuroscience research. In order to be useful, computational models need to be valid in two senses: First, they need to be consistent with experimental data and able to make testable predictions (external validity). Second, they need to be internally consistent and independently reproducible (internal validity). Here, we discuss both types of validity and provide a brief overview of tools and technologies used to ensure they are met. We also suggest the introduction of new collaborative technologies to ensure model validity: an incentivised experimental database for external validity and reproducibility audits for internal validity. Both rely on FAIR principles and on collaborative science practices.

Data Hazards as an ethical toolkit for neuroscience

“…Faulty results can manifest in many ways, such as (but not restricted to) inconsistencies in graph outputs; erroneous statistical reporting (Bakker & Wicherts, 2011); errors in the use of programming languages due to typos or misinterpretation of function documentation; misreporting of methods in wet-lab research. Mitigations for this label may not always be explicit, but rather be implicit in the form of good research practices (Maggs-Rapport, 2001;Fitzpatrick & Stefan, 2022;Tomić et al, 2022) and scientific rigour (Hofseth, 2018). Addressing potential sources of error enhances the reproducibility of findings and minimizes the risk of wasted time and resources due to futile replication attempts.…”

Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum

“…These shortcomings reduce the validity or interpretability of the predicted results. Only if modelers and experimenters work closely together can they check predictions made by the model and, if necessary, change the structure of the model or the experimental setup to gradually arrive at a meaningful representation of reality in the model that yields reliable results ( Fitzpatrick and Stefan, 2022 ).…”