Biology is messy. Proponents of intelligent design look at biology and wonder why it works so well. We see things differently. We wonder, given how many mistakes get made, how biology manages to work at all.
We model the evolution of rates of error in processes like transcription, translation, and protein folding (Rajon & Masel 2011, Xiong et al. 2017). These errors have profound consequences for evolvability; selection acts not just on the focal phenotype, but also on the cloud of phenotypes associated with adjacent genotypes, leading to the evolution of superior mutational neighborhoods (Rajon & Masel 2011, Rajon & Masel 2013).
We have quantified empirical patterns in the rates of error of protein-protein interactions (Brettner & Masel 2017), mistranscription (Meer et al. 2019), translation of non-coding transcripts (Wilson & Masel 2011), and stop codon readthrough (Kosinski & Masel 2020). E. coli has higher mistranscription rates than S. cerevisiae (Meer et al. 2019), contradicting the naive version of drift barrier theory, but in agreement with our more refined version of the theory in which the evolution of cost-free locus-specific error rates obviates the need for expensive proofreading (Rajon & Masel 2011, Xiong et al. 2017). Higher stop codon readthrough leads to higher levels of intrinsic protein disorder (Kosinski & Masel 2020), which is likely to promote evolvability.
We are also interested in errors that accumulate somatically in multicellular organisms (Nelson & Masel 2017, Nelson et al. 2020). We note the process of multicellular aging creates a double bind; errors inevitably accumulate unless intercellular competition acts to purge them, but if cells compete then cancer and other deleterious clonal expansions become an inevitable alternative mode of aging (Nelson & Masel 2017).
Nelson P, Promislow DEL, Masel J. (2020) Biomarkers for aging identified in cross-sectional studies tend to be non-causative, The Journals of Gerontology: Series A 75:466–472.
Kosinski L. J., Masel J. (2020). Readthrough errors purge deleterious cryptic sequences, facilitating the birth of coding sequences, Molecular Biology and Evolution, 37(6), 1761-1774.
Meer K. M., Nelson P. G., Xiong K., Masel J. (2019). High transcriptional error rates vary as a function of gene expression level, Genome Biology & Evolution, 12(1), 3754-3761.
Xiong K., Lancaster A. K., Siegal M. L., & Masel J. (2019) Feed forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise, Nature Communications 10:2418.
Nelson P, Masel J. (2017) Intercellular competition and the inevitability of multicellular aging, PNAS 114: 12982–12987.
Xiong K., McEntee J., Porfirio D., Masel J. (2017) Drift barriers to quality control when genes are expressed at different levels, Genetics 205: 397-407.
Rajon, E., & Masel, J. (2013) Compensatory evolution and the origins of innovations, Genetics, 193(4):1209-20.
Siegal M. L., & Masel J. (2012) Hsp90 depletion goes wild, BMC Biology 10:14 .
Brettner, L.M., Masel, J. (2012) Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast, BMC Systems Biology, 6:128
Wilson, B. A., & Masel, J. (2011). Putatively noncoding transcripts show extensive association with ribosomes, Genome Biology & Evolution, 3, 1245-1252
Rajon, E., & Masel, J. (2011). Evolution of molecular error rates and the consequences for evolvability. Proc. Natl. Acad. Sci. USA, 108(3), 1082-7.
Masel, J., & Siegal, M. L. (2009). Robustness: mechanisms and consequences. Trends in Genetics