arXiv subjects

Tom A. Williams

Publications and source records attributed to Tom A. Williams.

3 recordsLinked to original sources

arXiv2026

The Emergence of Life in the Light of Evolution

The origin of life is often framed primarily as a chemical problem, yet life s defining feature is evolution. Advances in geochemistry, prebiotic chemistry and molecular biology have suggested diverse scenarios for the emergence of genomes, metabolism and cellular compartments on the early Earth, but most of these models ignore the relevance of a population genetics perspective. Here, we argue that origin of life research must expand from asking simply how life began to exploring how it evolved from pre biological systems. Synthesizing evidence from comparative genomics, phylogenetics, biochemistry, and geoscience, we emphasize that the last universal common ancestor (LUCA) was already a complex, ecologically adapted population of cells far removed from the starting point of life, implying a deep, pre LUCA evolutionary history. We highlight how population genetics, ecology, and synthetic biology can constrain origin of life scenarios by making explicit the roles of selection, drift, mutation, horizontal gene transfer, parasites and compartmentalization in shaping early communities. Finally, we outline an evolutionary research agenda spanning proto metabolic autocatalytic networks, protocells, and the emergence of translation and the transition to DNA genomes, such that qualitative models can be formalized through evolution driven hypotheses testable with theory and laboratory experiments, including those with synthetic cells.

q-bio.PE↗

arXiv2020

Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference

Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees. Substitutions in sequences are modelled through a continuous-time Markov process, characterised by an instantaneous rate matrix, which standard models assume is time-reversible and stationary. These assumptions are biologically questionable and induce a likelihood function which is invariant to a tree's root position. This hampers inference because a tree's biological interpretation depends critically on where it is rooted. Relaxing both assumptions, we introduce a model whose likelihood can distinguish between rooted trees. The model is non-stationary, with step changes in the instantaneous rate matrix at each speciation event. Exploiting recent theoretical work, each rate matrix belongs to a non-reversible family of Lie Markov models. These models are closed under matrix multiplication, so our extension offers the conceptually appealing property that a tree and all its sub-trees could have arisen from the same family of non-stationary models. We adopt a Bayesian approach, describe an MCMC algorithm for posterior inference and provide software. The biological insight that our model can provide is illustrated through an analysis in which non-reversible but stationary, and non-stationary but reversible models cannot identify a plausible root.

q-bio.PE↗

arXiv2019

Generalising rate heterogeneity across sites in statistical phylogenetics

Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees relating species. Along branches, sequence evolution is modelled using a continuous-time Markov process characterised by an instantaneous rate matrix. Early models assumed the same rate matrix governed substitutions at all sites of the alignment, ignoring variation in evolutionary pressures. Substantial improvements in phylogenetic inference and model fit were achieved by augmenting these models with multiplicative random effects that describe the result of variation in selective constraints and allow sites to evolve at different rates which linearly scale a baseline rate matrix. Motivated by this pioneering work, we consider an extension using a quadratic, rather than linear, transformation. The resulting models allow for variation in the selective coefficients of different types of point mutation at a site in addition to variation in selective constraints. We derive properties of the extended models. For certain non-stationary processes, the extension gives a model that allows variation in sequence composition both across sites and taxa. We adopt a Bayesian approach, describe an MCMC algorithm for posterior inference and provide software. Our quadratic models are applied to alignments spanning the tree of life and compared with site-homogeneous and linear models.

stat.ME↗