Las proteínas Myb constituyen una familia de reguladores eucariontes con un dominio de unión al DNA (MYB-DBD) cuya estructura tridimensional se ha conservado a lo largo de la evolución, a pesar de la notable diversidad de funciones que desempeñan. En esta plática presentaremos un análisis evolutivo, estructural y funcional de las proteínas Myb del género Entamoeba, integrando herramientas de bioinformática, genómica, proteómica, biología celular y biología estructural. Los resultados muestran cómo la conservación estructural del dominio MYB ha favorecido la diversificación funcional de esta familia de proteínas, cuya participación se extiende desde la regulación transcripcional hasta la organización de la cromatina, la protección telomérica, el procesamiento de RNA y procesos asociados con la diferenciación y la virulencia de estos parásitos.

Los islotes pancreáticos son micro-órganos formados por células α, β y δ que secretan, respectivamente, glucagón, insulina y somatostatina para mantener el equilibrio de la glucosa en sangre. Dado que la función de los islotes emerge de interacciones eléctricas, paracrinas y estructurales difíciles de estudiar experimentalmente, el modelado computacional se ha convertido en un complemento esencial del trabajo experimental. Nuestra investigación se ha centrado en construir una mejor comprensión de la fisiología de los islotes desde una perspectiva multiescala, desde modelos celulares de canales iónicos y dinámica del calcio, hasta la reconstrucción tridimensional de islotes humanos y de ratón, incorporando más recientemente el transporte de oxígeno para revelar cómo la hipoxia limita el tamaño viable del islote.

Con Javier Martínez Luengas. Existen distintos microorganismos que se desplazan para buscar alimento, así como varias formas de desplazamiento individual para este propósito. Entre ellos destacan las mixobacterias, tales como Myxococcus xanthus, que producen un líquido viscoso que permite contener su motilidad en trayectorias previas y promueve el uso múltiple de estas trayectorias por bacterias de la misma especie, así como la agregación para formar el cuerpo fructífero cuando el alimento escasea. En este trabajo se presenta un modelo para describir la movilidad colectiva de estos microbios, tomando la interacción entre ellos por medio de la fuerza capilar como la principal contribución a su confinamiento y agregación. Mostramos algunas simulaciones numéricas que reproducen aspectos relevantes de la dinámica. Este modelo puede extenderse al caso mucho más complicado del ciclo de vida de Dictyostelium discoideum.

Nanofluidics is rapidly advancing toward applications in desalination and water purification, fuel cells, energy harvesting, healthcare, and many other fields. The unique physicochemical properties of two-dimensional (2D) materials make them ideal platforms for developing advanced nanofluidic devices. In this talk, I will discuss the fundamental properties of 2D materials relevant to high-performance membrane design, strategies for preparing stable colloidal dispersions, and methods for fabricating hybrid and functionalized membranes featuring bioinspired, tunable sub-nanometer channels, with applications in water purification, direct lithium extraction, and hydrogen energy technologies.

The interaction of α-synuclein with cellular membranes represents one of the earliest events in the pathogenesis of Parkinson’s disease; however, the molecular determinants governing this process remain poorly understood. In this lecture, I will present findings from molecular dynamics simulations of wild-type α-synuclein and five Parkinson’s disease-associated variants interacting with an anionic lipid membrane. The results reveal common membrane recognition patterns together with mutation-specific differences that modulate the stability and depth of membrane association, providing an atomistic perspective on the earliest stages of α-synuclein–membrane interactions.

Processes controlled by the stochastic synthesis and degradation (SSD) of molecules are fundamental in cell biology but their reaction kinetics are not well understood. Using methods borrowed from the theory of resetting processes, we determine the mean first-passage time at a target site of a collection of independent particles that are synthesized and degraded at constant rates, and follow an arbitrary diffusive motion. In bounded domains, despite particle degradation, SSD improves the mean search time compared to a single non-degrading particle if the synthesis rate exceeds a critical value which obeys a universal relation. We illustrate these findings with Brownian diffusion on the infinite line and in an interval.

Los retardos temporales representan procesos biológicos fundamentales, como la incubación, la recuperación o la señalización intercelular, y permiten modelar aquellos fenómenos donde existen demoras inherentes en la propagación de información. En esta charla se exponen dos aplicaciones de esta perspectiva. Primero, un modelo epidemiológico aplicado a la propagación de COVID-19 en México, cuyos parámetros y retardos se estimaron simultáneamente sobre datos nacionales usando algoritmos inspirados por la biología. Segundo, el análisis de bifurcaciones inducidas por retardo y caos intermitente en un modelo de quorum sensing bacteriano, donde lapsos heterogéneos gobiernan el tránsito entre regímenes periódicos y caóticos. Ambos casos destacan el papel de la estructura temporal en la complejidad de los sistemas no lineales y las herramientas computacionales para su caracterización.

FUS is an RNA-binding protein that forms biomolecular condensates through liquid–liquid phase separation. Although these condensates are important for cellular function, changes in their molecular organization may promote the formation of persistent and pathological aggregates associated with neurodegenerative diseases. In this talk, I will present our work using atomistic and coarse-grained molecular dynamics simulations to understand how FUS molecules interact within condensates. We explore how salt concentration, different protein regions, and negatively charged lipid membranes modulate FUS association and condensate organization. Our results show that environmental conditions can reorganize the intermolecular interaction network and change the relative contribution of specific FUS regions. These simulations provide a molecular view of how dynamic FUS condensates may evolve toward less reversible assemblies, helping us connect phase separation, protein aggregation, and cellular dysfunction.

Biological surfaces provide valuable models for developing functional materials with tailored wettability. This work presents an ongoing research project that integrates the biophysical characterization of surface morphology and chemical composition across organisms exhibiting distinctive wetting properties, including the desert beetle (Eleodes eschscholtzii), with natural language processing and machine learning to investigate relationships between surface microgeometry, chemical composition, and water interactions. Building on previous experimental findings, we introduce a literature-mining pipeline that combines Sentence-BERT embeddings, automated information extraction, and semantic retrieval to expand the available physicochemical dataset. Preliminary results demonstrate the feasibility of identifying relevant scientific literature and organizing wettability-related information into structured datasets. This interdisciplinary approach seeks to address data scarcity and establish a predictive machine-learning framework for the rational design and optimization of bioinspired surfaces, with potential applications in atmospheric water harvesting.

Alzheimer’s disease is characterized by the abnormal aggregation of proteins that disrupt neuronal function and connectivity. Among these proteins, tau plays a central role due to its intrinsically disordered nature and propensity to self-assemble under specific environmental conditions. In this presentation, Alzheimer’s disease is examined through the lens of physics, treating protein aggregation as a collective phenomenon emerging from intermolecular interactions. Using coarse-grained molecular dynamics simulations, we investigate the behavior of tau proteins in three systems: isolated tau, tau in the presence of arachidonic acid, and tau exposed to both arachidonic acid and functionalized fullerenes. The simulations reproduce experimental observations showing that arachidonic acid promotes aggregation, while functionalized fullerenes inhibit this process. Our results suggest that arachidonic acid facilitates aggregation through micelle formation, increasing effective protein connectivity, whereas fullerenes interfere with these interactions and reduces aggregation propensity. These findings highlight how molecular-scale interactions shape pathological self-assembly and demonstrate the value of computational physics in uncovering mechanisms that are difficult to resolve experimentally.