Bio

I was born and raised in central Mexico, in a state called Guanajuato. Although I was trained as an engineer due to social circumstances, my passion always resided in the natural world and the way to understand it that physics offered. Guided by this passion, I did my Ph.D. with Rob Phillips at Caltech, working at the interface between physics and biology. For my postdoc, I want to bring the Physical Biology mindset to the question of evolution. That is why I joined Dmitri Petrov's lab to study the evolutionary dynamics of microbial populations from a theory-experiment dialogue perspective.

Honors & Awards

  • Schmidt Science Fellow, Schmidt Science Fellows (2021)

Professional Education

  • Doctor of Philosophy, California Institute of Technology (2021)
  • BSc, Instituto Politecnico Nacional (Mexico), Biotech. engineering (2014)
  • Ph.D., Caltech, Biophysics (2021)

Stanford Advisors

Community and International Work

  • Clubes de Ciencia Mexico, Mexico

    Topic

    STEM education for URM students

    Populations Served

    Students in Mexico

    Location

    International

    Ongoing Project

    Yes

    Opportunities for Student Involvement

    Yes

Contact

  • Academic
    mrazo@stanford.edu
    University - Scholar Department: Biology Position: Postdoctoral Scholar

Additional Info

Lab Affiliations

All Publications

  • Bayesian inference of relative fitness on high-throughput pooled competition assays. PLoS computational biology Razo-Mejia, M., Mani, M., Petrov, D. 2024; 20 (3): e1011937

    Abstract

    The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

    View details for DOI 10.1371/journal.pcbi.1011937

    View details for PubMedID 38489348

  • Bayesian inference of relative fitness on high-throughput pooled competition assays. bioRxiv : the preprint server for biology Razo-Mejia, M., Mani, M., Petrov, D. 2023

    Abstract

    The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

    View details for DOI 10.1101/2023.10.14.562365

    View details for PubMedID 37904971

  • Reconciling kinetic and thermodynamic models of bacterial transcription PLOS COMPUTATIONAL BIOLOGY Morrison, M., Razo-Mejia, M., Phillips, R. 2021; 17 (1): e1008572

    Abstract

    The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

    View details for DOI 10.1371/journal.pcbi.1008572

    View details for Web of Science ID 000611971200005

    View details for PubMedID 33465069

    View details for PubMedCentralID PMC7845990

https://orcid.org/0000-0002-9510-0527