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Dr. Ángel Monteagudo
Research Assistant, University of A Coruña

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0 Computational and Experimental Fluid Dynamics
0 tech
0 Computacional Intelligence
0 Compuational Neuroscience
0 Computability analysis

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Journal article
Published: 03 September 2020 in Sustainability
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The COVID–19 pandemic led to restrictions on activities and mobility in many parts of the world. After the main peak of the crisis, restrictions were gradually removed, returning to a new normal situation. This process has impacted urban mobility. The limited information on the new normal situation shows changes that can be permanent or reversible. The impact on the diverse urban transport modes varies. This study analyzes the changes in transit ridership by line, the use of stops, the main origin–destination flows, changes in transit supply, operation time, and reliability of the city bus network of A Coruña. It is based on data from automatic vehicle location, bus stop boarding, and smart card use. Data from the first half of 2020 were compared to similar data in 2017–2019, defining suitable baselines for each analysis to avoid seasonal and day of week effects. The impact on transit ridership during the lockdown process was more significant than that on general traffic. In the new normal situation, the general traffic and the shared bike system recovered a higher percentage of their previous use than the bus system. These impacts are not uniform across the bus network.

ACS Style

Alfonso Orro; Margarita Novales; Ángel Monteagudo; José-Benito Pérez-López; Miguel Bugarín. Impact on City Bus Transit Services of the COVID–19 Lockdown and Return to the New Normal: The Case of A Coruña (Spain). Sustainability 2020, 12, 7206 .

AMA Style

Alfonso Orro, Margarita Novales, Ángel Monteagudo, José-Benito Pérez-López, Miguel Bugarín. Impact on City Bus Transit Services of the COVID–19 Lockdown and Return to the New Normal: The Case of A Coruña (Spain). Sustainability. 2020; 12 (17):7206.

Chicago/Turabian Style

Alfonso Orro; Margarita Novales; Ángel Monteagudo; José-Benito Pérez-López; Miguel Bugarín. 2020. "Impact on City Bus Transit Services of the COVID–19 Lockdown and Return to the New Normal: The Case of A Coruña (Spain)." Sustainability 12, no. 17: 7206.

Journal article
Published: 27 March 2017 in BMC Bioinformatics
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The canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form. The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution. A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code. The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered. The analyses show that the canonical code is not in a deep local minimum and that the fitness landscape is not a multimodal fitness landscape with deep and separated peaks. Moreover, the canonical code is clearly far away from the areas of higher fitness in the landscape. Given the non-presence of deep local minima in the landscape, although the code could evolve and different forces could shape its structure, the fitness landscape nature considered in the error minimization theory does not explain why the canonical code ended its evolution in a location which is not an area of a localized deep minimum of the huge fitness landscape.

ACS Style

José Santos; Ángel Monteagudo. Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability. BMC Bioinformatics 2017, 18, 1 -18.

AMA Style

José Santos, Ángel Monteagudo. Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability. BMC Bioinformatics. 2017; 18 (1):1-18.

Chicago/Turabian Style

José Santos; Ángel Monteagudo. 2017. "Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability." BMC Bioinformatics 18, no. 1: 1-18.

Book chapter
Published: 01 January 2016 in Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology
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We have used cellular automata to model tumor growth behavior in multicellular systems. We modeled the behavior at a cellular level, based on the presence of the main cancer hallmarks in each cell in the avascular phase. The abstract model of cancer hallmarks and the cellular automata tool allow the analysis of the emergent behavior of the multicellular system in different scenarios in which the different hallmarks are predominant. The tool also allows to easily include the behavior and effects of cancer stem cells with their regrowth capability. This permits an analysis of the behavior of the system when different treatment strategies are used against the different types of cancer cells.

ACS Style

J. Santos; Á. Monteagudo. Tumor Growth Emergent Behavior Analysis Based on Cancer Hallmarks and in a Cancer Stem Cell Context. Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology 2016, 533 -543.

AMA Style

J. Santos, Á. Monteagudo. Tumor Growth Emergent Behavior Analysis Based on Cancer Hallmarks and in a Cancer Stem Cell Context. Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology. 2016; ():533-543.

Chicago/Turabian Style

J. Santos; Á. Monteagudo. 2016. "Tumor Growth Emergent Behavior Analysis Based on Cancer Hallmarks and in a Cancer Stem Cell Context." Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology , no. : 533-543.

Conference paper
Published: 11 July 2015 in Proceedings of the 2015 on MobiSys PhD Forum
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We used evolutionary computing for optimizing cancer treatments taking into account the presence and effects of cancer stem cells. We used a cellular automaton to model tumor growth at cellular level, based on the presence of the main cancer hallmarks in the cells. The cellular automaton allows the study of the emergent behavior of the multicellular system evolution in different scenarios defined by the predominance of the different hallmarks. When cancer stem cells (CSCs) are modeled, the multicellular system evolution is additionally dependent on the CSC tumor regrowth capability because their differentiation to non-stem cancer cells. When a standard treatment is applied against non-stem (differentiated) cancer cells, different effects are present depending on the strategy used to eliminate these non-stem cancer cells. We used Differential Evolution to optimize the treatment application strategy in terms of intensity, duration and periodicity to minimize the final outcome of tumor growth and regrowth.

ACS Style

Ángel Monteagudo; José Santos. Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context. Proceedings of the 2015 on MobiSys PhD Forum 2015, 1 .

AMA Style

Ángel Monteagudo, José Santos. Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context. Proceedings of the 2015 on MobiSys PhD Forum. 2015; ():1.

Chicago/Turabian Style

Ángel Monteagudo; José Santos. 2015. "Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context." Proceedings of the 2015 on MobiSys PhD Forum , no. : 1.

Journal article
Published: 01 January 2014 in Biosystems
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We used a cellular automaton model for cancer growth simulation at cellular level, based on the presence of different cancer hallmarks acquired by the cells. The presence of the hallmarks in each of the cells determines cell mitotic and apoptotic behaviors. Depending on the presence of the different hallmarks and some associated parameters of the hallmarks, the system can evolve to different dynamics. We used the cellular automaton model to inspect the capability of different hallmarks to generate tumor growth in different conditions, using this study in a cancer stem cell context to analyze the capability of the hallmarks to tumor regrowth in different circumstances.

ACS Style

Ángel Monteagudo; José Santos. Studying the capability of different cancer hallmarks to initiate tumor growth using a cellular automaton simulation. Application in a cancer stem cell context. Biosystems 2014, 115, 46 -58.

AMA Style

Ángel Monteagudo, José Santos. Studying the capability of different cancer hallmarks to initiate tumor growth using a cellular automaton simulation. Application in a cancer stem cell context. Biosystems. 2014; 115 ():46-58.

Chicago/Turabian Style

Ángel Monteagudo; José Santos. 2014. "Studying the capability of different cancer hallmarks to initiate tumor growth using a cellular automaton simulation. Application in a cancer stem cell context." Biosystems 115, no. : 46-58.

Conference paper
Published: 01 January 2013 in Computer Vision
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We used a cellular automaton model for cancer growth simulation at cellular level, based on the presence of different cancer hallmarks acquired by the cells. The rules of the cellular automaton determine cell mitotic and apoptotic behaviors, which are based on the acquisition of the hallmarks in the cells by means of mutations. The simulation tool allows the study of the emergent behavior of tumor growth. This work focuses on the simulation of the behavior of cancer stem cells to inspect their capability of regeneration of tumor growth in different scenarios.

ACS Style

Ángel Monteagudo; José Santos Reyes. Cancer Stem Cell Modeling Using a Cellular Automaton. Computer Vision 2013, 7931, 21 -31.

AMA Style

Ángel Monteagudo, José Santos Reyes. Cancer Stem Cell Modeling Using a Cellular Automaton. Computer Vision. 2013; 7931 ():21-31.

Chicago/Turabian Style

Ángel Monteagudo; José Santos Reyes. 2013. "Cancer Stem Cell Modeling Using a Cellular Automaton." Computer Vision 7931, no. : 21-31.

Conference paper
Published: 01 January 2012 in Computer Vision
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We studied the relative importance of the different cancer hallmarks in tumor growth in a multicellular system. Tumor growth was modeled with a cellular automaton which determines cell mitotic and apoptotic behaviors. These behaviors depend on the cancer hallmarks acquired in each cell as consequence of mutations. Additionally, these hallmarks are associated with a series of parameters, and depending on their values and the activation of the hallmarks in each of the cells, the system can evolve to different dynamics. Here we focus on the relevance of each hallmark in the progression of the first avascular phase of tumor growth and in representative situations.

ACS Style

José Santos; Ángel Monteagudo. Study of Cancer Hallmarks Relevance Using a Cellular Automaton Tumor Growth Model. Computer Vision 2012, 7491, 489 -499.

AMA Style

José Santos, Ángel Monteagudo. Study of Cancer Hallmarks Relevance Using a Cellular Automaton Tumor Growth Model. Computer Vision. 2012; 7491 ():489-499.

Chicago/Turabian Style

José Santos; Ángel Monteagudo. 2012. "Study of Cancer Hallmarks Relevance Using a Cellular Automaton Tumor Growth Model." Computer Vision 7491, no. : 489-499.

Book chapter
Published: 01 January 2012 in Advances in Intelligent and Soft Computing
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We used cellular automata for simulating tumor growth in a multicellular system. Cells have a genome associated with different cancer hallmarks, indicating if those are activated as consequence of mutations. The presence of the cancer hallmarks defines cell states and cell mitotic behaviors. These hallmarks are associated with a series of parameters, and depending on their values and the activation of the hallmarks in each of the cells, the system can evolve to different dynamics. We focus here on how the cellular automata simulating tool can provide a model of the tumor growth behavior in different conditions.

ACS Style

Ángel Monteagudo; José Santos. A Cellular Automaton Model for Tumor Growth Simulation. Advances in Intelligent and Soft Computing 2012, 147 -155.

AMA Style

Ángel Monteagudo, José Santos. A Cellular Automaton Model for Tumor Growth Simulation. Advances in Intelligent and Soft Computing. 2012; ():147-155.

Chicago/Turabian Style

Ángel Monteagudo; José Santos. 2012. "A Cellular Automaton Model for Tumor Growth Simulation." Advances in Intelligent and Soft Computing , no. : 147-155.

Journal article
Published: 21 February 2011 in BMC Bioinformatics
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As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative.

ACS Style

José Santos; Ángel Monteagudo. Simulated evolution applied to study the genetic code optimality using a model of codon reassignments. BMC Bioinformatics 2011, 12, 56 -56.

AMA Style

José Santos, Ángel Monteagudo. Simulated evolution applied to study the genetic code optimality using a model of codon reassignments. BMC Bioinformatics. 2011; 12 (1):56-56.

Chicago/Turabian Style

José Santos; Ángel Monteagudo. 2011. "Simulated evolution applied to study the genetic code optimality using a model of codon reassignments." BMC Bioinformatics 12, no. 1: 56-56.

Journal article
Published: 07 June 2010 in Journal of Theoretical Biology
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We used simulated evolution to study the adaptability level of the canonical genetic code. An adapted genetic algorithm (GA) searches for optimal hypothetical codes. Adaptability is measured as the average variation of the hydrophobicity that the encoded amino acids undergo when errors or mutations are present in the codons of the hypothetical codes. Different types of mutations and point mutation rates that depend on codon base number are considered in this study. Previous works have used statistical approaches based on randomly generated alternative codes or have used local search techniques to determine an optimum value. In this work, we emphasize what can be concluded from the use of simulated evolution considering the results of previous works. The GA provides more information about the difficulty of the evolution of codes, without contradicting previous studies using statistical or engineering approaches. The GA also shows that, within the coevolution theory, the third base clearly improves the adaptability of the current genetic code.

ACS Style

José Santos; Ángel Monteagudo. Study of the genetic code adaptability by means of a genetic algorithm. Journal of Theoretical Biology 2010, 264, 854 -865.

AMA Style

José Santos, Ángel Monteagudo. Study of the genetic code adaptability by means of a genetic algorithm. Journal of Theoretical Biology. 2010; 264 (3):854-865.

Chicago/Turabian Style

José Santos; Ángel Monteagudo. 2010. "Study of the genetic code adaptability by means of a genetic algorithm." Journal of Theoretical Biology 264, no. 3: 854-865.

Journal article
Published: 18 September 2008 in Natural Computing
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We have studied the canonical genetic code optimality by means of simulated evolution. A genetic algorithm is used to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes. Such analysis is performed within the coevolution theory of the genetic code organization. We have studied the progression of the canonical genetic code optimality within such theory, considering a possible scenario of a previous code with two-letter codons as well as the current organization of the canonical code. Moreover, we have analysed the particular optimality and progression of adaptability of the individual nucleotide bases.

ACS Style

José Santos; Ángel Monteagudo. Genetic code optimality studied by means of simulated evolution and within the coevolution theory of the canonical code organization. Natural Computing 2008, 8, 719 -738.

AMA Style

José Santos, Ángel Monteagudo. Genetic code optimality studied by means of simulated evolution and within the coevolution theory of the canonical code organization. Natural Computing. 2008; 8 (4):719-738.

Chicago/Turabian Style

José Santos; Ángel Monteagudo. 2008. "Genetic code optimality studied by means of simulated evolution and within the coevolution theory of the canonical code organization." Natural Computing 8, no. 4: 719-738.

Conference paper
Published: 29 June 2007 in Computer Vision
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In this work we use simulated evolution to corroborate the adaptability of the natural genetic code. An adapted genetic algorithm searches for optimal hypothetical codes. The adaptability is measured as the average variation of the hydrophobicity that experiment the encoded amino acids when errors or mutations are presented in the codons of the hypothetical codes. Different types of mutations and base position mutation probabilities are considered in this study.

ACS Style

Ángel Monteagudo; José Santos. Simulated Evolution of the Adaptability of the Genetic Code Using Genetic Algorithms. Computer Vision 2007, 478 -487.

AMA Style

Ángel Monteagudo, José Santos. Simulated Evolution of the Adaptability of the Genetic Code Using Genetic Algorithms. Computer Vision. 2007; ():478-487.

Chicago/Turabian Style

Ángel Monteagudo; José Santos. 2007. "Simulated Evolution of the Adaptability of the Genetic Code Using Genetic Algorithms." Computer Vision , no. : 478-487.