Cell fitness is an omniphenotype

Moore’s law states that computers get faster and less expensive over time. In contrast in biopharma, there is the reverse spelling, Eroom’s law, which states that drug discovery is getting slower and costing more money every year. At the current pace, we estimate it costing $9.9T and it taking to the year 2574 to find drugs for less than 1% of all potentially important protein-protein interactions. Herein, we propose a solution to this problem. Borrowing from how Bitcoin works, we put forth a consensus algorithm for inexpensively and rapidly prioritizing new factors of interest (e.g., a gene or drug) in human disease research. Specifically, we argue for synthetic interaction testing in mammalian cells using cell fitness – which reflect changes in cell number that could be due many effects – as a readout to judge the potential of the new factor. That is, if we combine perturbing a known factor with perturbing the unknown factor and they produce a synergistic, i.e., multiplicative rather than additive cell fitness phenotype, this justifies proceeding with the unknown gene/drug in more complex models where the known perturbation is already validated. This recommendation is backed by the following evidence we demonstrate herein: 1) human genes currently known to be important to cell fitness involve nearly all classifications of cellular and molecular processes; 2) Nearly all human genes important in cancer – a disease defined by altered cell number – are also important in other common diseases; 3) Many drugs affect a patient’s condition and the fitness of their cells comparably. Taken together, these findings suggest cell fitness could be a broadly applicable phenotype for understanding gene, disease, and drug function. Measuring cell fitness is robust and requires little time and money. These are features that have long been capitalized on by pioneers using model organisms that we hope more mammalian biologists will recognize. Short summary Cell fitness is a biological hash function that enables interoperability of biomedical data. ### Competing Interest Statement This work was funded by NIH grants, K99/R00 AG047255, R01 AR073017, R41 GM137625 and R42 DK121652, and AWS Research Credits to T.R.P. Though there are no pending patent applications or financial transactions based on this work, T.R.P acknowledges potential future financial conflicts of interest as a major shareholder in the St. Louis-based, biotechnology companies, Bioio, LLC and Healthspan Technologies, Inc., which conduct research related to this work. The spouse of N.L.S. is listed as an inventor on Issued U.S. Patent 8,080,371 "Markers for Addiction" covering the use of certain single nucleotide polymorphisms in determining the diagnosis, prognosis, and treatment of addiction.