Publications by authors named "Mackenzie M Johnson"

Article Synopsis
  • Somatic hypermutation (SHM) is crucial for antibody diversity and requires probabilistic models for analyzing mutations and understanding affinity maturation.
  • The authors create more efficient "thrifty" models using convolutions on 3-mer embeddings, resulting in fewer parameters yet a wider contextual understanding of SHM compared to traditional 5-mer models.
  • They discover that a per-site effect isn't necessary to explain SHM patterns, and note discrepancies between two current methods for fitting SHM models, which do not improve performance when combined.
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Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation.

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The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors.

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Article Synopsis
  • * Researchers analyzed transcriptomic and proteomic data from *E. coli* and *S. cerevisiae*, finding that gene expression varies significantly based on growth rates, particularly showing that rapid growth enhances codon usage biases.
  • * The findings suggest that understanding gene expression under specific conditions, such as rapid growth, is essential for comprehending the long-term evolution of microbial gene sequences and their translational constraints.
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Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience.

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In many applications of evolutionary inference, a model of protein evolution needs to be fitted to the amino acid variation at individual sites in a multiple sequence alignment. Most existing models fall into one of two extremes: Either they provide a coarse-grained description that lacks biophysical realism (e.g.

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Gene duplication is seen as a major source of structural and functional divergence in genome evolution. Under the conventional models of sub or neofunctionalization, functional changes arise in one of the duplicates after duplication. However, we suggest here that the presence of a duplicated gene can result in functional changes to its interacting partners.

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