Martínez Ríos, Félix Orlando

Main Affiliation

Facultad de Ingeniería - CampCM

Preferred name

Official Name

Martínez Ríos, Félix Orlando

ORCID

0000-0002-2346-5922

Researcher ID

N-7502-2018

Scopus Author ID

24339038500

Now showing 1 - 10 of 46

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A cross-scale physical framework for transcription-associated CPEB4 microexon susceptibility and crowding-enhanced isoform self-association: toward a biophysical mechanism of idiopathic autism
(IOP Publishing, 2026-07-31)
Alvarado, Ysaías J
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Cardozo-Urdaneta, Arlene
;
Vivas, Alejandro
;
Lossada, Carla
;
Mendez, Aníbal
Co-transcriptional splicing and protein self-assembly are governed by coupled kinetic and thermodynamic constraints, such that modest changes in exon processing can propagate into substantial shifts in isoform-dependent mesoscale behavior. Here, we develop a cross-scale physical framework to examine whether transcription-associated kinetic pressure could differentially bias CPEB4 microexon selection and thereby reshape downstream isoform behavior. Using a simplified transcriptional kinetic model, we define an acetylation-associated high-throughput regime as a coarse-grained proxy for reduced time available for co-transcriptional exon recognition. Comparative sequence and structural analyses identify microexon 4 (me4) as less robust than microexon 3 (me3), with weaker cis-regulatory support and lower thermodynamic stability, consistent with greater susceptibility to omission under kinetically constrained conditions. A reduced probabilistic splicing framework accordingly predicts a directional bias against me4, superimposed on a basal transcript landscape in which the full-length isoform remains present. As a complementary downstream analysis, scaled-particle-theory calculations indicate that representative Δ4-enriched scenarios thermodynamically favor homotypic self-association under macromolecular crowding, suggesting a plausible physical amplification route for modest splicing bias. Orthogonal measurements in a yeast perturbation system identify oxidative and spectroscopic signatures compatible with strong butyrate-associated physicochemical stress, but these are interpreted as perturbation readouts rather than direct measurements of neuronal histone acetylation or splicing. Together, these results define a testable cross-scale framework linking transcription-associated kinetic constraints, directional microexon susceptibility, and crowding-dependent remodeling of the CPEB4 isoform assembly landscape. © 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the IOP-Standard License.
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A hybrid simulated annealing and threshold accepting for satisfiability problems using dynamically cooling schemes
(2007)
Martínez Ríos, Félix Orlando
;
Frausto-Solís, Juan
For Satisfiability (SAT) Problem there is not a deterministic algorithm able to solve it in a polynomial time. Simulated Annealing (SA) and similar algorithms like Threshold Accepting (TA) are able to find very good solutions of SAT instances only if their control parameters are correctly tuned. Classical TA usually uses the same Markov chain length for each temperature cycle but they spend a lot of time. In this paper a method based on the neighborhood structure to get the Markov chain length in a dynamical way for each temperature cycle is proposed. Three cooling schemes are also presented in the paper. The experimentation presented in the paper shows that the proposed method is more efficient than the classical one.
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Innovative Alignment-Based Method for Antiviral Peptide Prediction
(2024)
Daniela de Llano García
;
Marrero Ponce, Yovani
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Guillermin Agüero-Chapin
;
Francesc J. Ferri
;
Agostinho Antunes
<jats:p>Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models’ robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew’s correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.</jats:p>
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A first-year design experience based on SAE Aero Design contest to support ABET learning outcomes and engineering vocation in freshmen student
(2017)
Martínez Ríos, Félix Orlando
The a-k outcomes established by Accreditation Board for Engineering and Technology (ABET) for Engineering students in their self-assessment framework, should be reflected in the different subjects that taught to the students of the first two years of the various engineering programs. On the other hand, in those first semesters, the vocation of the students about the different Engineering is not very well defined. This experiment shows a proposal that links the results of ABET with an international student competition such as Society of Automotive Engineers (SAE) Aero Design, to reinforce and guide the new students in their future choice of specialization in the School of Engineering. We also show the relationship between the challenges and problems in the SAE Aero Design competition for new students and ABET's a-k outcomes. We show the results obtained with nineteen students over three years. It is important to mention that none of the students involved in this experiment comes from Aeronautical Engineering (or similar to it).
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New Particle Swarm Optimizer Algorithm with Chaotic Maps for Combinatorial Global Optimization Problems. An Application to the Deconvolution of Mössbauer Spectra
(Springer, 2024-01-01)
Martínez Ríos, Félix Orlando
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Jiménez-López, Omar
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González Morfín, Juan
In this chapter, we present a novel method for addressing global optimization problems inspired by evolutionary algorithms found in nature. We integrate the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm with random value generation based on chaotic maps. The resulting algorithm is applied to the computationally complex task of deconvoluting Mossbauer spectra. We implement ten chaotic maps to generate random values and compare their performance with traditional random number generators. Through experiments, we demonstrate that the developed algorithm excels in exploring the search space and exhibits fast intensification in finding the global minimum. In addition, we perform a comprehensive review of existing solutions to the Mossbauer spectrum deconvolution problem, highlighting the scarce availability of developments in this area. We also present a user-friendly program designed with an intuitive interface to facilitate the deconvolution process by Spector Mossbauer. This program will be freely distributed without operational restrictions. Experimental validation is performed on Mossbauer spectra generated using the developed program and those obtained by experimental means, affirming the efficiency of the new algorithm conceived. ©Springer.
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An efficient method to compare latencies in order to obtain the best route for SDN
(2017)
Elguea, Lorenzo M.
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Martínez Ríos, Félix Orlando
Comparing two or more routes on Internet is difficult owing to the variability of the measurements resulting from the different routes or use conditions. With current tools such as SDN[1], it is important to determine with certainty which the best route between a user and an internet service. This will be achieved with fast measurements which do not affect the operation of the network. With trends such as IoT, the best routes can be identified based on latency and not just on the jumps between autonomous systems, fact that optimizes data traffic in a specific way whether it is IPv4 or IPv6. As time elapses, it becomes more important to have a perfect setting for the LAN, which means optimal DNS, LDAP Servers appropriate number, etc. Thats why we propose a precise method that contemplates every possible variation of data, thus making a comparison by means of the use of confidence limits.
Scopus© Citations 6 1 12
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A new swarm algorithm for global optimization of multimodal functions over multi-threading architecture hybridized with simulating annealing
(2018)
Martínez Ríos, Félix Orlando
;
Murillo-Suarez, Alfonso
This paper presents a new algorithm, PCLPSO, based on particle swarm optimization, which uses comprehensive learning particle swarm optimizer. Our algorithm executes C parallel CLPSO algorithms. We adopted as a criterion of completion a maximum value of evaluations of the objective function. During the execution of the CLPSO algorithms, when a certain evaluation value of the functions is reached, the best k are selected, and different initialization criteria are applied to continue the execution of the CLPSO algorithms: restarting the worst ones for the best solution or restores the worst ones to a random solution. For this restart, we use the Boltzmann criterion in a similar way as Simulating Annealing (SA) does. In this work, the experimental results obtained for the search of the minimum of 16 multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Brannin, Schwefel, and others. Our algorithm proved to be more efficient than the traditional CLPSO in its experimental results, and the nonparametric Wilcoxon test confirmed this.
1Scopus© Citations 10 13
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Biological Implications of the Intrinsic Deformability of Human Acetylcholinesterase Induced by Diverse Compounds: A Computational Study
(MDPI, 2024)
Alvarado, Ysaías J.
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González-Paz, Lenin
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Paz , José L.
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Loroño-González, Marcos A.
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Santiago Contreras, Julio
The enzyme acetylcholinesterase (AChE) plays a crucial role in the termination of nerve impulses by hydrolyzing the neurotransmitter acetylcholine (ACh). The inhibition of AChE has emerged as a promising therapeutic approach for the management of neurological disorders such as Lewy body dementia and Alzheimer’s disease. The potential of various compounds as AChE inhibitors was investigated. In this study, we evaluated the impact of natural compounds of interest on the intrinsic deformability of human AChE using computational biophysical analysis. Our approach incorporates classical dynamics, elastic networks (ENM and NMA), statistical potentials (CUPSAT and SWOTein), energy frustration (Frustratometer), and volumetric cavity analyses (MOLE and PockDrug). The results revealed that cyanidin induced significant changes in the flexibility and rigidity of AChE, especially in the distribution and volume of internal cavities, compared to model inhibitors such as TZ2PA6, and through a distinct biophysical-molecular mechanism from the other inhibitors considered. These findings suggest that cyanidin could offer potential mechanistic pathways for future research and applications in the development of new treatments for neurodegenerative diseases. ©The authors ©MDPI
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Modeling the functional impact of CPEB3 and CPEB4 dysregulation in autism: A theoretical–computational framework
(Elsevier BV, 2026)
González-Paz, Lenin
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Vivas, Alejandro
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Cardozo-Urdaneta, Arlene
;
Lossada, Carla
;
Mendez, Anibal
Autism spectrum disorder (ASD) involves impaired synaptic plasticity tightly coupled to local mRNA translation. Cytoplasmic polyadenylation element-binding proteins 3 and 4 (CPEB3 and CPEB4) are post-transcriptional regulators of neuronal mRNA translation that may contribute to ASD-related molecular alterations. In this theoretical–computational study, we develop a weighted functional impact model that integrates transcriptomic expression with intrinsic molecular constraints of CPEB3 and CPEB4 to estimate regional and cell type–specific vulnerability in ASD. Coarse-grained molecular dynamics (MD) simulations were quantitatively analyzed to assess aggregation, diffusion, and cluster stability under cell type–specific cytoplasmic conditions, with statistical uncertainty explicitly evaluated. The anterior cingulate cortex and thalamus emerged as primary vulnerability sites. Despite higher CPEB4 expression—mainly in glial cells—our weighted functional impact model predicted greater theoretical susceptibility linked to CPEB3 dysfunction, particularly in inhibitory and excitatory neurons. MD simulations revealed that CPEB3 forms transient diffusion-permissive aggregates, whereas CPEB4 tends to assemble into more stable condensates. These complementary behaviors suggest differential but interdependent regulation of neuronal and glial functions. Importantly, the proposed framework provides experimentally testable predictions on how protein–protein interactions, microexon loss, and cytoplasmic crowding influence translational control in ASD. This integrative approach provides a quantitative and biologically grounded framework to investigate how post-transcriptional regulators contribute to ASD-relevant molecular vulnerability. ©The authors ©Sciencedirect ©Elsevier.
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μ𝜃-EGF: A New Multi-Thread and Nature-Inspired Algorithm for the Packing Problem
(2020)
García-Jacas, César R.
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Martínez Ríos, Félix Orlando
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Marmolejo Saucedo, José Antonio
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Murillo-Suarez, Alfonso
In this paper, the authors present a new algorithm efficient solution to the packing problem in two dimensions. The authors propose a new heuristic using the value of the electromagnetic field to determine the best position to place a circular object in a configuration of other circular objects previously packed. Also, this algorithm simulates two processes to compact objects already placed, inspired by gravitational forces, to minimize the empty space in the container and maximizing the number of objects in the container. To determine the efficacy of this algorithm, the authors carried out experiments with twenty-four instances. Parallel computing can contribute to making decision processes such as optimization and prediction more agile and faster. Real-time decision making involves the use of solution methodologies and algorithms. For this reason the present manuscript shows an alternative for the solution of a classic industry problem that must be solved quickly. Packaging optimization can help reduce waste of container material. The material used to transport the products can reduce its environmental impact due to an efficient packaging process. Light-weighting can also be accomplished by reducing the amount of packaging material used. © Springer Nature
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Martínez Ríos, Félix Orlando

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