Reflections on Schrödinger’s “What is life?”

Nick Barton “What is life?” - Trinity College Dublin, 1943

- contrasts deterministic with statistical laws - heredity must be based on a stable “aperiodic crystal” - X-rays induce , implying a small target

Schrödinger’s ideas had little direct influence, yet drew many Into molecular biology.

Contrast molecular with

Does life depend on general principles, independent of details?

Judson, 1979 The Eighth Day of Creation Barton et al. 2007, Watson & Crick, 1953: structure of DNA

1953-1962: - DNA-> RNA-> protein - genetic code (Crick…Nirenberg) - gene regulation (Jacob, Monod) - protein structure (Perutz, Kendrew)

Ingenious arguments failed to decipher the code - e.g. Gamow’s argument from DNA structure

Extraordinary technical developments - yet few fundamental advances in 50 years? Evolutionary biology: a synthesis between Darwin and Mendel

Darwin, 1859, Origin of species Mendel, 1866

Evolution was widely accepted, yet natural selection was not Rediscovery of Mendel’s work in 1900 led to bitter disputes Eventually, population led to the evolutionary synthesis : - sophisticated theory, based on simple genetics - little data on natural variation - showed that even weak selection can be effective (t ~ 1/s)

Discovery of abundant molecular variation in 1966: - largely explained by neutral theory - shift to statistical inference

Now, an abundance of DNA sequence ….

General principles: - diffusion of genes through space - the coalescent process - Diffusion of genes across a hybrid zone in Podisma pedestris A. majus pseudomajus

A. m. striatum

SNPs across the Planoles transect Individual (West to East)

Flavia

Sulf Locus (ordered)

Rosea Eluta

cp ROS ELUTA The coalescent process: - rate of coalescence of lineages ~ 1/2N - with constant population size, coalescence times ~ exp(-t/2N) - distribution of coalescence times across the genome -> N(t) Li & Durbin, 2011 infer N(t) for humans:

What can we really infer about ancestral populations ? Quantitative genetics: Fisher (1918)

- VP = VG+VE = VA+VD+VAA+VAD … VE - variance components can be estimated - variance components are insensitive to selection - the infinitesimal model Given sequence data, why not just find the genes ? Wood et al., Nature 2014: human height ~250,000 people -> 700 variants explain 20% of variance

In practice, we cannot account for most of the variance: - how can we use sequence data ?

Genomic selection improves the efficiency of artificial selection, assuming the infinitesimal model How can we best understand organisms? - even complete information may not be enough - complex traits may evolve via subtle changes at many sites

How much can we know? IST Austria Institute of Science and Technology

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