A Python-Based Program for Estimating Biological Surface Acidity by Using a Non-Electrostatic Adsorption Model

Abstract

The objective of this research is to develop an open-source Python-based surface complexation modeling program to estimate the acidity of biological surface. Several computer software already exists with such function installed, such as ProtoFit and FITEQL. However, these programs lack capabilities in constraining fitting parameters, resulting in model fits that are not necessarily justified by the input data. Here, a new Python-based model algorithm was developed to estimate surface acidity and protonation constants for biological surfaces. The program was developed based on the algorithm of ProtoFit, and improved to allow for user-defined boundary conditions for model fitting parameters. This model was tested on potentiometric pH titration data for suspensions of Pseudomonas fluorescens and Bacillus subtilis bacterial cells and suspensions of colloidal particles (e.g., extracellular vesicles) that were isolated from cell cultures. Model testing was also performed for titration data collected for aqueous buffer solutions with known chemical species and concentration. The estimated surface acidities from Python script and ProtoFit are compared, and error analysis was conducted. Error analysis showed that the Python script modeled the titration data with lower curve-fitting error than models by ProtoFit, which suggests a better optimization performance in Python script. However, the model comparisons for the aqueous buffer titrations (for which acidity constants were known) did not showing such trend. We believe that is because experimental error is much larger than model error in our setups. Therefore, variance-based sensitivity analysis was further conducted on the Python script, and the result shows that the titrant concentration (N_tit) and adsorbent mass (M_ads) were two variables that contributed the most variance in our model output.