Projects
(2020) Predicting how many bits of information cells can extract from the environment
open-access PDF | paper website | github repository
Living organisms are constantly sensing intra- and extracellular cues and responding accordingly. The quality and precision of such responses can mean the difference between surviving or not certain challenges; therefore, there is a constant selection pressure for cells to gather enough information from any stimulus to build an adequate response. In this context, the information that cells can obtain has a precise mathematical definition measured —just as in computers— in bits.
In a 2020 publication, our goal was to predict how many bits of information a cell can harbor using a simple genetic circuit process. To do so, I wrote down a theoretical model to predict the full distribution of gene expression based on the physics of this molecular process. I calibrated our model with previous information to perform parameter-free predictions. To test the model, I compared the predictions with experimental single-cell gene expression data finding great agreement.
(2018) Quantitative modeling of gene regulation via allosteric proteins
open-access PDF | paper website | github repository
Despite lacking a nervous system, single bacterial cells can make decisions given signals from their surroundings. How can individual molecules sense and transmit these signals? The answer comes from one of the crowning scientific achievements of the past century: allostery. Simply stated, allostery is the property of certain macromolecules to exist in multiple conformations with different properties. For example, transcription factors —proteins that control gene expression— can be active (able to bind the DNA) or inactive (unable to bind DNA) depending on the concentration of a signaling molecule.
In a 2018 paper, our socialist team wrote down a theoretical model that predicts the expression level of a gene regulated by an allosteric transcription factor. We then tested the model experimentally, showing that the model was able to predict how changes to the regulation of the gene translates to changes in the cellular response.