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The application of recursion theory to computer science resulted in the information revolution that powered the technological advances of the 20th century. If the same ideas were to be applied to evolutionary biology, a similar revolution would likely occur in health care and bioinformatics. Recursion provides a hypothesis driven, testable framework to explain the interaction between the genotype and phenotype. A mathematical model is proposed that may explain how biological networks, at the genetic, neural, and metabolic levels, are interacting with each other in a manner directly analogous to how computing devices function. This may explain how negative and positive signaling loops may compete with each other over substrates to shift metabolic pathways towards one phenotype vs another in both health and disease, the communication patterns within and between organs, the inability to reproduce GWAS results across populations, and the mechanisms underlying neuronal signaling within the brain and between the brain and the rest of the body. Additionally, several batch correction methods were compared to each other to determine the most effective method. Data with a known set of differentially expressed genes at predetermined parameters were simulated and then the optimal batch correction with the lowest false positive rate was determined. Finally, the role of the mitochondrial carrier family, a set of central nodes connecting metabolic cycles between the mitochondria and the cytosol in eukaryotes, was determined in healthy heart compared to healthy liver and then in ischemic vs dilated cardiomyopathy.