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Developing and improving mechanism-oriented computational models to better explain biological phenomena is a dynamic and expanding frontier. As the complexity of targeted phenomena has increased, so too has the diversity in methods and terminologies, often at the expense of clarity, which can make reproduction challenging, even problematic. To encourage improved semantic and methodological clarity, we describe the spectrum of Mechanism-oriented Models being used to develop explanations of biological phenomena. We cluster explanations of phenomena into three broad groups. We then expand them into seven workflow-related model types having distinguishable features. We name each type and illustrate with examples drawn from the literature. These model types may contribute to the foundation of an ontology of mechanism-based biomedical simulation research. We show that the different model types manifest and exert their scientific usefulness by enhancing and extending different forms and degrees of explanation. The process starts with knowledge about the phenomenon and continues with explanatory and mathematical descriptions. Those descriptions are transformed into software and used to perform experimental explorations by running and examining simulation output. The credibility of inferences is thus linked to having easy access to the scientific and technical provenance from each workflow stage.

Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.

PURPOSE: Colorectal cancer liver metastasis (CRLM) and hepatocellular carcinoma (HCC) are widely treated using microwave and radiofrequency ablation. Local tumor progression (LTP) may develop depending on the shortest vascular distance and large lesion diameter. This study aims to explore the effect of these spatial features and to investigate the correlation between tumor-specific variables and LTP. METHODS: This is a retrospective study covering the period between January 2007 and January 2019. One hundred twenty-five patients (CRLM: HCC: 64:61) with 262 lesions (CRLM: HCC: 142:120) were enrolled. The correlation between LTP and the variables was analyzed using the chi-square test, Fischer's exact test, or the Fisher-Freeman-Halton test where applicable. The local progression-free survival (Loc-PFS) was analyzed using the Kaplan-Meier method. Univariable and multivariable Cox regression analyses were performed to identify prognostic factors. RESULTS: Significant correlations were observed for LTP in both CRLM and HCC at a lesion diameter of 30-50 mm (P = 0.019 and P < 0.001, respectively) and SVD of =3 mm (P < 0.001 for both). No correlation was found between the ablation type and LTP (CRLM: P = 0.141; HCC: P = 0.771). There was no relationship between residue and the ablation type, but a strong correlation with tumor size was observed (P = 0.127 and P < 0.001, respectively). In CRLM, LTP was associated with mutant K-ras and concomitant lung metastasis (P < 0.001 and P = 0.003, respectively). In HCC, a similar correlation was found for Child-Pugh B, serum alpha-fetoprotein (AFP) level of >10 ng/mL, predisposing factors, and moderate histopathological differentiation (P < 0.001, P = 0.008, P = 0.027, and P<0.001, respectively). In CRLM, SVD of =3 mm proved to be the variable with the greatest negative effect on Loc-PFS (P = 0.007), followed by concomitant lung metastasis (P = 0.027). In HCC, a serum AFP level of >10 ng/mL proved to be the variable with the greatest negative effect on Loc-PFS (P = 0.045). CONCLUSION: In addition to the lesions' spatial features, tumor-specific variables may also have an impact on LTP. [ABSTRACT FROM AUTHOR]