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

  • BSc, Instituto Politecnico Nacional (Mexico), Biotech. engineering (2014)
  • Ph.D., Caltech, Biophysics (2021)

Stanford Advisors

Community and International Work

  • Clubes de Ciencia Mexico, Mexico


    STEM education for URM students

    Populations Served

    Students in Mexico



    Ongoing Project


    Opportunities for Student Involvement


Lab Affiliations

All Publications

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


    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