Yasa Baig

All Publications

• Scaling laws of bacterial and archaeal plasmids. Nature communications Maddamsetti, R., Shyti, I., Wilson, M. L., Son, H. I., Baig, Y., Zhou, Z., Lu, J., You, L. 2025; 16 (1): 6023

  Abstract

  The capacity of a plasmid to express genes is constrained by its length and copy number. However, the interplay between these parameters and their constraints on plasmid evolution have remained elusive due to the absence of comprehensive quantitative analyses. Here, we present 'Pseudoalignment and Probabilistic Iterative Read Assignment' (pseuPIRA), a computational method that overcomes previous computational bottlenecks, enabling rapid and accurate determination of plasmid copy numbers at large scale. We apply pseuPIRA to all microbial genomes in the NCBI RefSeq database with linked short-read sequencing data (4644 bacterial and archaeal genomes including 12,006 plasmids). The analysis reveals three scaling laws of plasmids: first, an inverse power-law correlation between plasmid copy number and plasmid length; second, a positive linear correlation between protein-coding genes and plasmid length; and third, a positive correlation between metabolic genes per plasmid and plasmid length, particularly for large plasmids. These scaling laws imply fundamental constraints on plasmid evolution and functional organization, indicating that as plasmids increase in length, they converge toward chromosomal characteristics in copy number and functional content.

  View details for DOI 10.1038/s41467-025-61205-2

  View details for PubMedID 40603865

  View details for PubMedCentralID PMC12222811

• Linear scaling reveals low-dimensional structure in observable microbial dynamics. bioRxiv : the preprint server for biology Zhou, Z., Chen, X., Şimşek, E., Hamrick, G. S., Baig, Y., Holmes, Z. A., Du, Z., Karig, D. K., You, L. 2025

  Abstract

  Microbial communities often exhibit apparently complex dynamics driven by myriad interactions among community members and with their environments. Yet, practical modeling and control are often based on limited number of observables, raising a fundamental question: to what extent are these observed dynamics predictable given unobserved background complexity? Here, we report an emergent simplicity that the temporal dynamics of observable microbial populations can be captured by low-dimensional representations. Using variational autoencoders (VAEs), we define a critical latent dimension (Ec ) that quantifies the minimal number of variables required to represent observable microbial dynamics. We find that Ec scales linearly with the number of observables, despite the complexity of unobserved background dynamics. This principle holds across simulations of ecological, spatial, and gene-transfer models, experiments with engineered and environment-derived communities, and human microbiomes. Our findings establish a scaling law for microbial community dynamics and demonstrate observable dynamics alone contain sufficient information for prediction and control, even without full knowledge of the community.

  View details for DOI 10.1101/2025.06.13.659614

  View details for PubMedID 40666893

  View details for PubMedCentralID PMC12262697