Search results for "Mod"

showing 10 items of 39605 documents

SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments

2017

Abstract Motivation Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons. Results SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case…

0301 basic medicineStatistics and ProbabilityComputer science0206 medical engineeringComputational Biology02 engineering and technologycomputer.software_genreModels BiologicalBiochemistryComputer Science ApplicationsSet (abstract data type)03 medical and health sciencesComputational Mathematics030104 developmental biologyComputational Theory and MathematicsStochastic optimizationData miningMolecular BiologycomputerSoftware020602 bioinformaticsCombinatorial explosionBioinformatics
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FLYCOP: metabolic modeling-based analysis and engineering microbial communities

2018

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0301 basic medicineStatistics and ProbabilityComputer scienceMetaboliteAuxotrophy030106 microbiologyMicrobial ConsortiaEccb 2018: European Conference on Computational Biology ProceedingsEvolutionary engineeringmedicine.disease_causeBiochemistry03 medical and health scienceschemistry.chemical_compoundmedicineEscherichia coliMetabolic modelingMolecular BiologyEscherichia coli2. Zero hungerbiologyMicrobiotaSystemsBiological evolutionSynechococcusbiology.organism_classificationComputer Science ApplicationsComputational MathematicsMulticellular organism030104 developmental biologyComputational Theory and MathematicschemistryMetabolic EngineeringBiochemical engineeringSoftwareBioinformatics
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L1-Penalized Censored Gaussian Graphical Model

2018

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…

0301 basic medicineStatistics and ProbabilityFOS: Computer and information sciencesgraphical lassoComputer scienceGaussianNormal DistributionInferenceMultivariate normal distribution01 natural sciencesMethodology (stat.ME)010104 statistics & probability03 medical and health sciencessymbols.namesakeGraphical LassoExpectation–maximization algorithmHumansComputer SimulationGene Regulatory NetworksGraphical model0101 mathematicsStatistics - MethodologyEstimation theoryReverse Transcriptase Polymerase Chain ReactionEstimatorexpectation-maximization algorithmGeneral MedicineCensoring (statistics)High-dimensional datahigh-dimensional dataGaussian graphical model030104 developmental biologysymbolscensored dataCensored dataExpectation-Maximization algorithmStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaAlgorithmAlgorithms
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Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

2016

Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value…

0301 basic medicineStatistics and ProbabilityFactorialDependency (UML)Computer scienceGaussianNormal Distributionpenalized inferencesparse networkscomputer.software_genreMachine learning01 natural sciencesNormal distribution010104 statistics & probability03 medical and health sciencessymbols.namesakeSparse networksGeneticsComputer SimulationGene Regulatory NetworksGraphical model0101 mathematicsgene-regulatory systemMolecular BiologyProbabilityMarkov chainModels GeneticPenalized inferencebusiness.industryModel selectiongraphical modelGene-regulatory systemsComputational Mathematics030104 developmental biologysymbolsA priori and a posterioriData miningArtificial intelligenceGraphical modelsSettore SECS-S/01 - StatisticabusinesscomputerNeisseriaAlgorithmsStatistical applications in genetics and molecular biology
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The adaptive value of tandem communication in ants:Insights from an agent-based model

2021

AbstractSocial animals often share information about the location of resources, such as a food source or a new nest-site. One well-studied communication strategy in ants is tandem running, whereby a leader guides a recruit to a resource. Tandem running is considered an example of animal teaching because a leader adjusts her behaviour and invests time to help another ant to learn the location of a resource more efficiently. Tandem running also has costs, such as waiting inside the nest for a leader and a reduced walking speed. Whether and when these costs outweigh the benefits of tandem running is not well understood. We developed an agent-based simulation model to investigate the conditions…

0301 basic medicineStatistics and ProbabilityForage (honey bee)Adaptive valueOperations researchComputer scienceForagingGeneral Biochemistry Genetics and Molecular BiologyRunning03 medical and health sciences0302 clinical medicineResource (project management)NestAnimalsLearningAgent-based modelGeneral Immunology and MicrobiologyTandemAntsCommunicationApplied MathematicsGeneral MedicineBeesVariable (computer science)030104 developmental biologyModeling and SimulationSocial animalFemaleGeneral Agricultural and Biological Sciences030217 neurology & neurosurgeryTandem running
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A generalization of Kingman's model of selection and mutation and the Lenski experiment.

2017

Kingman’s model of selection and mutation studies the limit type value distribution in an asexual population of discrete generations and infinite size undergoing selection and mutation. This paper generalizes the model to analyze the long-term evolution of Escherichia. coli in Lenski experiment. Weak assumptions for fitness functions are proposed and the mutation mechanism is the same as in Kingman’s model. General macroscopic epistasis are designable through fitness functions. Convergence to the unique limit type distribution is obtained.

0301 basic medicineStatistics and ProbabilityGeneralizationPopulationBiology01 natural sciencesModels BiologicalGeneral Biochemistry Genetics and Molecular Biology010104 statistics & probability03 medical and health sciencesStatisticsEscherichia coliApplied mathematicsQuantitative Biology::Populations and EvolutionLimit (mathematics)0101 mathematicsSelection GeneticeducationSelection (genetic algorithm)education.field_of_studyFitness functionGeneral Immunology and MicrobiologyApplied MathematicsGeneral MedicineQuantitative Biology::GenomicsBiological Evolution030104 developmental biologyDistribution (mathematics)Modeling and SimulationMutation (genetic algorithm)MutationEpistasisGeneral Agricultural and Biological SciencesMathematical biosciences
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Nature lessons: the whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics

2016

Reductive genome evolution is a universal phenomenon observed in endosymbiotic bacteria in insects. As the genome reduces its size and irreversibly losses coding genes, the functionalities of the cell system, including the energetics processes, are more restricted. Several energetic pathways can also be lost. How do these reduced metabolic networks sustain the energy needs of the system? Among the bacteria with reduced genomes Candidatus Portiera aleyrodidarum, obligate endosymbiont of whiteflies, represents an extreme case since lacks several key mechanisms for ATP generation. Thus, to analyze the cell energetics in this system, a genome-scale metabolic model of this endosymbiont was const…

0301 basic medicineStatistics and ProbabilityGenome evolutionAnabolismSystems biology030106 microbiologyCell EnergeticsBiologyModels BiologicalGenomeGeneral Biochemistry Genetics and Molecular BiologyHemiptera03 medical and health sciencesMetabolic flux analysisAnimalsAmino AcidsSymbiosisGeneGenome sizeCarotenoidchemistry.chemical_classificationGeneral Immunology and MicrobiologyObligateApplied MathematicsEnergeticsGeneral MedicineMetabolismbeta Carotenebiology.organism_classificationMetabolic Flux AnalysisAmino acidHalomonadaceae030104 developmental biologychemistryBiochemistryModeling and SimulationEnergy MetabolismGeneral Agricultural and Biological SciencesGenome BacterialMetabolic Networks and PathwaysBacteria
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A deterministic model for highly contagious diseases: The case of varicella

2016

[EN] The classic nonlinear Kermack-McKendrick model based upon a system of differential equations has been widely applied to model the rise and fall of global pandemic and also seasonal epidemic by introducing a forced harmonic infectivity which would change throughout the year. These methods work well in their respective domains of applicability, and for certain diseases, but they fail when both seasonality and high infectivity are combined. In this paper we consider a Susceptible-Infected-Recovered, or SIR, model with two latent states to model the propagation and evolutionary history of varicella in humans. We show that infectivity can be calculated from real data and we find a nonstanda…

0301 basic medicineStatistics and ProbabilityInfectivity030106 microbiologyBiologyHighly contagious diseasesInfectivity evolutionCondensed Matter PhysicsVaricella03 medical and health sciences0302 clinical medicineSystem of differential equationsPandemicEconometrics030212 general & internal medicineMATEMATICA APLICADACompartmental models
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panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data.

2018

Abstract Motivation The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic spec…

0301 basic medicineStatistics and ProbabilityLineage (genetic)Computer scienceAb initioComputational biologyBacterial genome size[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]BiochemistryGenome[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing03 medical and health sciences[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR][SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]Insertion sequenceMolecular BiologyGenomic organizationHigh-Throughput Nucleotide SequencingSequence Analysis DNA[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM][SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/BacteriologyPipeline (software)[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and Mathematics[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]DNA Transposable Elements[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Genome BacterialSoftwareBioinformatics (Oxford, England)
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Mathematical investigation of innate immune responses to lung cancer: The role of macrophages with mixed phenotypes

2021

Abstract Macrophages’ role in the evolution of solid tumours is a well accepted fact, with the M1-like macrophages having an anti-tumour role and the M2-like macrophages having a pro-tumour role. Despite the fact that some clinical studies on lung tumours have emphasised also the presence of macrophages with mixed M1 and M2 phenotypes in addition to macrophages with distinct phenotypes, the majority of studies still use the distinct M1-M2 classification to predict the evolution of tumours and patient survival. In this theoretical study we use a mathematical modelling and computational approach to investigate the role of macrophages with mixed phenotype on growth/control/elimination of lung …

0301 basic medicineStatistics and ProbabilityLung Neoplasms[SDV]Life Sciences [q-bio]BiologyGeneral Biochemistry Genetics and Molecular Biology03 medical and health sciences0302 clinical medicinemedicineMacrophageHumans[NLIN]Nonlinear Sciences [physics][MATH]Mathematics [math]Lung cancerComputingMilieux_MISCELLANEOUSInnate immune systemGeneral Immunology and MicrobiologyApplied MathematicsMacrophagesPatient survivalGeneral MedicineModels Theoreticalmedicine.diseasePhenotypeImmunity Innate030104 developmental biologyPhenotypeModeling and SimulationCancer researchLung tumoursGeneral Agricultural and Biological Sciences030217 neurology & neurosurgery
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