Using ensemble modeling to determine causes of multifactorial disorders

Multifactorial disorders are diseases whose underlying causes may be due to a combination of factors, including genetics, environmental influences, and lifestyle. Deconvoluting the factors responsible for such issues can be tremendously challenging. However, evolutionary algorithms (EAs) are particularly well suited to address this class of problem. EAs may be used to generate populations of solutions to carry out ensemble-based analyses of mathematical models that can determine which factors may have the largest impact on a particular disease state. In this work, a commonly utilized mathematical model for bone physiology was used to explore the underlying mechanism responsible for secondary hyperparathyroidism due to renal failure. Using a genetic algorithm (GA), a population of parameter sets for the bone model were generated based on a clinical data set. The average result of the best five GA-based models was compared to the standard bone model prediction as reported in the literature. The GA-based models manipulated several parameters simultaneously while the standard literature model manipulated only the glomerular filtration rate (GFR) parameter. The GA model resulted in a superior fit of the clinical data. It further indicated that six of the 108 parameters were significantly changed from what would be expected of a healthy individual. The GFR parameter was among the six significantly changed parameters. The remaining significantly changed parameters involved phosphate imbalances. Interestingly, hyperphosphatemia has been recognized as one of the primary causes of secondary hyperparathyroidism. These results would appear to corroborate the effectiveness of the GA in better understanding multifactorial disorders.