This is a fascinating niche of evolutionary biology that I have worked in for a while. The short answer is that yes, as far as we can tell all organisms evolve increasingly more efficient replication machinery, however at some point the strength of selection is no longer powerful enough to overcome the strength of genetic drift and some degree of error rate persists. As far as we can tell it seems like population size governs where this balance ends up such that small populations have high mutation rates and large populations have reduced mutation rates. Michael Lynch coined the term drift barrier hypothesis to describe this phenomenon. https://pubmed.ncbi.nlm.nih.gov/23077252/

If the organism is too efficient at preventing mutations - it would evolve slower, right?

Yes, but note the mutation rate of germline cells - that are passed to your offspring and hence influence evolution - is estimated to be two orders of magnitude lower than other (somatic) cells.

Not necessarily if it had recombination (as in sexual reproduction), but as far as I understand, yes, you’d probably get fewer novel alleles/coding sequences of DNA generated per organism replication

Location: Boston, Massachusetts, USA

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Technologies: - Fields: Cancer Biology, Cancer Genetics, Population Genetics, Bioinformatics - Coding Languages: Python, R, Bash, Go, Rust - Computational Methods: Sequencing Analysis, Rare Variant Detection, GWAS, Sequence Alignment, Variant Calling, Mendelian Randomization, Polygenic Risk Scoring - Computational Tools: Scikit-learn, Numpy, Matplotlib, Pandas, SciPy, GATK, samtools, Seurat, Git/Github, Cluster Computing, Linux, Google Cloud Computing, AWS, Code Ocean, Random Forest, Deep Learning, K-Nearest Neighbor, Regression, Clustering, UKB, TOPMed, AllofUs, EHR - Wetlab Technologies: Amplicon Sequencing, Liquid Biopsies, NGS, sc-DNASeq, ddPCR, scRNA-Seq, CRISPR-Cas, System, Genome Editing, Cell Culture, ctDNA

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Email: orb_05daring@icloud.com

LinkedIn: https://www.linkedin.com/in/lutheraliggett/

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I am a scientist with over 15 years of experience, principally with a focus on cancer genetics and computational biology. In most of my research positions I was responsible for both the wet-lab and the computational aspects of my projects. I have worked extensively on detecting rare somatic mutations of the hematopoietic system and used these mutations to model and understand the steps involved in the process of oncogenesis. In graduate school I built one of the most sensitive mutation detecting platforms in the world. I also have spent time translating my research to the clinic to inform physician decision making during cancer treatment. Additionally, I have worked to understand the role of mutation accumulation in the first human gene therapy clinical trials. In my most recent research I have been pursuing a population genetics approach to understand the evolution of somatic mutation rates in humans. Most recently I worked as a Senior Scientist in Computational Biology at a startup in Cambridge that leveraged mitochondrial mutations for lineage tracing of the hematopoietic tissue as a means of diagnosing early cancers.

Personal: I spend most of my spare time running, both in the woods, and when i'm late for meetings.