Rockets, gauges, and pendulums: applying engineering principles to cell biology

Application of the Taguchi method to explore a robust condition of tumor-treating field treatment

Tumor-treating fields have potential as minimally invasive cancer treatment. This study aimed to explore the optimum tumor-treating field conditions that minimize unpredicted variations in therapeutic outcomes resulting from differences in cell size and electrical properties. The electric field concentration that induces a dielectrophoretic force near the division plane of a mitotic cell was calculated by finite element analysis for 144 cases, based on different combinations of six noise factors associated with cells and four controllable factors including frequency, as determined by the Taguchi method. Changing the frequency from 200 to 400 kHz strongly increased robustness in producing a dielectrophoretic force, irrespective of noise factors. However, this frequency change reduced the force magnitude, which can be increased by simply applying a higher voltage. Based on additional simulations that considered this trade-off effect, a frequency of 300 kHz is recommended for a robust TTF treatment with allowable variations. The dielectrophoretic force was almost independent of the angle of applied electric field deviated from the most effective direction by ±20 degrees. Furthermore, increased robustness was observed for extracellular fluid with higher conductivity and permittivity. The Taguchi method was useful for identifying robust tumor-treating field therapy conditions from a considerably small number of replicated simulations.

Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data

Background

Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs.

Methods

We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure.

Results

Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios.

Conclusion

We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.