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)
BSc, Instituto Politecnico Nacional (Mexico), Biotech. engineering (2014)
Ph.D., Caltech, Biophysics (2021)
Dmitri Petrov, Postdoctoral Faculty Sponsor
Community and International Work
Clubes de Ciencia Mexico, Mexico
STEM education for URM students
Students in Mexico
Opportunities for Student Involvement
Bayesian inference of relative fitness on high-throughput pooled competition assays.
bioRxiv : the preprint server for biology
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
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