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Valdivia Ortega, J.

Publications and source records attributed to Valdivia Ortega, J..

3 recordsLinked to original sources

Layered helical order reveals assembly states in podosome actin networks

How stochastic molecular events are converted into ordered cellular machines that perform mechanical work remains a central question in biology. Cryo-electron tomography enables visualization of macromolecular assemblies inside cells at molecular resolution, but static tomograms are generally interpreted as structural snapshots, with limited access to assembly state and temporal progression. Here, we show that cellular tomograms retain interpretable information about the assembly of a force-generating actin network. Using human macrophage podosomes as a physiologically relevant system, we combine spatial mapping of actin filaments (F-actin) and Arp2/3-mediated branch junctions, orientation analysis of deep-learning-based filament segmentations and Markov-chain modeling to infer actin-network assembly in situ. We identify favored membrane-directed actin polymerization and Arp2/3-mediated branching, establishing a local growth axis for podosome network organization. At the network scale, podosomes display layered helical order: filament-orientation classes recur at intervals of approximately 33 nm along the membrane-normal axis, forming a three-class cycle that returns to an approximately equivalent non-polar orientation. Markov modeling identifies preferred mother-to-daughter filament-orientation transitions and yields a stationary composition of branch-orientation states that closely matches that observed in structurally more advanced network regions. Together, these results link local actin nucleation, network-scale architecture and structural assembly state in a native cellular machine. Our study advances cryo-electron tomography from structural description toward temporal inference and 4D in situ structural biology.

cell biology↗

Mathematical model of molecular evolution through a stochastic analysis

1Today we are able to describe genome evolution but, there are still several open questions on this area such as: Which is the probability that a mutation occurs at a nucleotide level? Is it possible to predict the evolution of a particular genome?, or talking about preservation, is there a way to simulate the genetic diversity for endangered species? In this paper it is shown that it is possible to make a mathematical model not only of mutations on the genome of species, but of evolution itself, including factors such as artificial and natural selection. It is also presented the algorithm to obtain the probabilities of mutation for each specific part of the genome and for each specie.\n\nEven more, it is presented a mathematical method to estimate the amount of generations between two related genomes and a function capable of predict the amount of mutations through time a genome will suffer.\n\nThe potential of having this tool is giantic going from genetic engineering applied to medicine to filling up blank spaces in phylogenetic studies or preservation of endangered species due to genetic diversity.

evolutionary biology↗

Markov chains applied to molecular evolution simulation

Today we are able to describe genome evolution but, there are still several open questions on this area such as: Which is the probability that a mutation occurs at a nucleotide level? Is it possible to predict the evolution of a particular genome?, or talking about preservation, is there a way to simulate the genetic diversity for endangered species? In this paper it is shown that it is possible to make a mathematical model not only of mutations on the genome of species, but of evolution itself, including factors such as artificial and natural selection. It is also presented the algorithm to obtain the probabilities of mutation for each specific part of the genome and for each specie. The potential of having this tool is giantic going from genetic engineering applied to medicine to filling up blank spaces in phylogenetic studies or preservation of endangered species due to genetic diversity.

genomics↗