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Ignacio Medina

Publications and source records attributed to Ignacio Medina.

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Active Growth Layer Induced by Micromechanical Feedback Shapes Proliferating Cell Collectives

Proliferating cell collectives often develop an active growth layer near their boundary that regulates expansion and morphology, as observed in systems ranging from bacterial biofilms to epithelial tissues and tumor spheroids. While such layers have been attributed to diverse mechanisms, their microscopic origin remains unclear in many situations. Here, we show that micromechanical feedback alone provides a minimal mechanism for their emergence. We introduce a particle-based model of non-motile proliferating cells in which growth is locally inhibited by compressive stress, coupling division to mechanical interactions and generating an active growth layer without biochemical regulation. An emergent mechanical length scale, denoted by $\chi$, sets the extent of the proliferative region and controls the system's behavior across scales, governing growth dynamics, morphology and organizing internal stress and velocity fields. Coarse-graining the model yields a continuum description with no adjustable parameters, providing a microscopic foundation for existing approaches. When the colony expands into a passive environment, we observe and characterize fingering instabilities driven purely by mechanical feedback. These instabilities can be tuned through the system geometry relative to $\chi$, and leads to an exponential acceleration of colony growth, enhancing the collective growth rate. We further establish a correspondence with nutrient-depletion models, providing a route to study the statistical properties of expanding fronts within a minimal microscopic framework.

cond-mat.soft

Concurrent and Accurate RNA Sequencing on Multicore Platforms

In this paper we introduce a novel parallel pipeline for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, named HPG-Aligner, leverages the speed of the Burrows-Wheeler Transform to map a large number of RNA fragments (reads) rapidly, as well as the accuracy of the Smith-Waterman algorithm, that is employed to deal with conflictive reads. The aligner is complemented with a careful strategy to detect splice junctions based on the division of RNA reads into short segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing useful information for the successful alignment of the complete reads. Experimental results on platforms with AMD and Intel multicore processors report the remarkable parallel performance of HPG-Aligner, on short and long RNA reads, which excels in both execution time and sensitivity to an state-of-the-art aligner such as TopHat 2 built on top of Bowtie and Bowtie 2.

q-bio.GN