Biology subjects

Gomez-Romero, L.

Publications and source records attributed to Gomez-Romero, L..

4 recordsLinked to original sources

bioRxiv2026

Metabolic cost sets the shear stress profile of optimal vascular trees

Vascular remodeling responds to local wall shear stress, but transport efficiency belongs to the network as a whole. We connect these scales by minimizing dissipation under a fixed maintenance budget, with vessel cost proportional to mass raised to an exponent b. At an optimum, dissipated power is proportional to vessel cost. This yields the shear set point |{tau} | {propto} r^(b-1){ell}^(b-1)/2 for radius r and length {ell}, with uniform shear recovered at b = 1. The associated local adaptation rule decreases the global functional, although the topology it reaches retains a memory of development. Simulations with 400 and 700 sinks show that staged growth improves transport mainly by allowing pruned connections to regrow. Fluctuating demand sustains loops without increasing the maintenance budget. The local condition fixes allocation on a given support, whereas growth and variable demand shape the topology that survives.

biophysics↗

bioRxiv2024

A Cryptic Prophage Transcription Factor Drives Phenotypic Changes via Host Gene Regulation

Cryptic prophages (CPs) are elements of bacterial genomes acquired from bacteriophage that infect the host cell and ultimately become stably integrated within the host genome. While some proteins encoded by CPs can modulate host phenotypes, the potential for Transcription Factors (TFs) encoded by CPs to impact host physiology by regulating host genes has not been thoroughly investigated. In this work, we report hundreds of host genes regulated by DicC, a DNA-binding TF encoded in the Qin prophage of Esherichia coli. We identified host-encoded regulatory targets of DicC that could be linked to known phenotypes of its induction. We also demonstrate that a DicC-induced growth defect is largely independent of other Qin prophage genes. Our data suggest a greater role for cryptic prophage TFs in controlling bacterial host gene expression than previously appreciated.

microbiology↗

bioRxiv2024

Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

bioinformatics↗

bioRxiv2018

ExtendAlign: a computational algorithm for delivering multiple global alignment results originated from local alignments.

In this work, we evaluated several tools used for the alignment of short sequences and found that most aligners execute reasonably well for identical sequences, whereas a variety of alignment errors emerge for dissimilar ones. Since alignments are essential in computational biology, we developed ExtendAlign, a post-analysis tool that corrects these errors and improves the alignment of dissimilar short sequences. We used simulated and biological data to show that ExtendAlign outperforms the other aligners in most metrics tested. ExtendAlign is useful for pinpointing the identity percentage for alignments of short sequences in the range of [~]35-50% similarity.

bioinformatics↗