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Computación Evolutiva Proteómica Un análisis de representaciones de ubicación libre basadas en proteomas utilizando el algoritmo genético proporcional PowerPoint PPT Presentation

Computación Evolutiva Proteómica Un análisis de representaciones de ubicación libre basadas en proteomas utilizando el algoritmo genético proporcional Iván Garibay, Ph.D. Office of Research and Commercialization & School of Electrical Engineering and Computer Science

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Computación Evolutiva Proteómica Un análisis de representaciones de ubicación libre basadas en proteomas utilizando el algoritmo genético proporcional

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Computación Evolutiva ProteómicaUn análisis de representaciones de ubicación libre basadas en proteomas utilizando el algoritmo genético proporcional

Iván Garibay, Ph.D.

Office of Research and Commercialization &

School of Electrical Engineering and Computer Science

Evolutionary Computation Laboratory

University of Central Florida

[email protected]

http://ivan.research.ucf.edu

Version 2.0 110304 1:04AM


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Rethinking Evolutionary Computation

Computación Evolutiva (EC)

  • Método de computacional inspirado en el concepto Darwiniano de evolución por selección natural.

  • Es como crianza de caballos de raza: uno determina quien es el mejor y dirige y controla la evolución

  • Computadoras hacen posible “evolucionar” estructuras muy rápido: horas o días

  • Las estructuras que se “crian” o evolucionan son:

    • Vectores (para optimización)

    • Programas de computadora (control)

    • Programas SPICE (circuitos)

    • Estructuras Geométricas (antenas)


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Aplicaciones EC: Circuitos

  • Koza GP

  • 21 reinvenciones

  • 2 nuevas patentes

More info: http://www.genetic-programming.org/


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Antenas

  • En el espacio 2004

  • NASA Ames Research Center

  • Hardware Evolutivo

  • Funciona, mejor que la que diseñaron grupo de expertos en nasa

  • No entienden completamente por que funciona

More info: http://ic.arc.nasa.gov/projects/esg/research/antenna.htm


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Introduction

Problema: Complejidad

  • Necesitamos herramientas para tratar la complejidad

  • Computación Evolutiva (CE) ha probado ser efectiva

  • CE afronta limitaciones debido a espacios de búsqueda muy grandes y muy complejos:

    • Convergencia prematura a sub-optimas

    • Estancamiento de la búsqueda

    • Efectos negativos epistaticos (interferencia genética)

    • Destrucción de bloques de construcción genética muy largos, etc.

  • Problema de la complejidad: superar limitaciones actuales para poder evolucionar estructuras mucho mas complejas


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Introduction

Aprendiendo de la Naturaleza

  • Nature evolve strikingly complex organisms in response to complex environmental adaptation problems with apparent ease

  • Localize and extract principles from nature

  • Apply them to design better algorithms

Pictures credit: Sanjeev Kumar: http://www.cs.ucl.ac.uk/staff/S.Kumar/


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Rethinking Evolutionary Computation

Representación es critica

  • Representacion adecuadamente de el problema es crucial.

  • Define the space to be explored

  • Mapping between possible problem solutions and internal representation space

GenotypetoPhenotype

Genome (DNA)Organisms

Computational Instance of Evolutionary Problem

Structure Solution

Bit StringOrdering of

cities for TSP

“10 01 11 01”(Boston, NY, LA, Orlando)


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Rethinking Evolutionary Computation

Estructuras de Información

  • DNA molecule is an information structure:

    • Store information digitally (chain of nucleotides)

    • Nucleotide = deoxyribose sugar + phosphate + Nitrogenous base

    • Nitrogenous bases: Adenine, Thymine, Cytosine, Guanine

  • DNA is an amazingly efficient, highly specialized structure for information storage, replication, expression and evolution

image credit: U.S. Department of Energy Genomes to Life Program, http://doegenomestolife.org.


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Rethinking Evolutionary Computation

Estructuras de Función

  • Proteins:

    • Most elementary building blocks of functionality

    • Assembled directly from segments of DNA

    • Self-assemble into a characteristic three-dimensional shape

    • Involved in almost every biological process

    • Ultimately responsible for all the organism’s functionalities

image credit: U.S. Department of Energy Genomes to Life Program, http://doegenomestolife.org.


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Rethinking Evolutionary Computation

Del Genotype al Phenotype

  • Classical Genetics: linear relation

    • Gene  Phene (visible trait)

    • Gene type (hair color gene)  Phene type (hair color)

  • Modern Genetics: non-linear relation

    • Sum (ki*genei) + cascade metabolic reactions (protein-protein, gene- protein, gene-mRNA, and others) + Environment  Phene

Genes

mRNA

Proteins

Metabolic Pathways

Visible Traits

Epigenetic Factors


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Nature

Complex genotype to phenotype mapping

Genes to proteins, proteins interact in complex ways to produce biological function and behavior

Functional structures: proteins

EC

Usually direct genotype to phenotype

Each gene represents one characteristic of the problem (similar to have one gene for intelligence or tallness, clearly not the case)

No functional structures involved

Rethinking Evolutionary Computation

Representaciones en la Naturaleza y en EC


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Proteomic Approach

Genomics y Proteomics

  • Unique perspective:

    • Study complete sets of functional building blocks that conform an organism (not single gene or protein)

    • Genomics focus on the study of organism genomes: complete set of genes

    • Proteomics: study of organisms proteomes: protein complement of genome


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98.7%

Proteomic Approach

Resultados Intrigantes

  • Complexity not correlated with their genome

    • Rice genome contain more genes than human genome (Goff, 2002)

    • Humans and chimpanzees genomes are 98.7% identical (Fujiyama, 2002)

  • Complexity may be correlated with their proteome


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Image: Oltvai & Barabási 2002

Proteomic Approach

Representando como la Naturaleza(revisited: life’s complexity pyramid [Barabási, 2002])

  • Genomics and proteomics provide a better understanding at organism level

Emergent Complex function

Self-organization, interaction networks

Basic biological building blocks


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Proteomic Approach

Una nueva forma de representar

S

Complex Solution

Subject to fitness evaluation

(Organism)

Complexity Building

Proteins cooperate, compete and antagonize.

Self-organization, self-assembly

(proteome)

Low complexity building blocks encode solution subject to crossover, mutation, etc.

(genome)

Proteins (Functional BBs)

Genes (Information BBs)


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f

Proteomic Approach

El Método Proteómico

  • Introduce two fundamental departures from traditional EC

  • The focus of our work is the study of the effects of introducing such an interaction space into EC, as modeled by a multiset

1. Interaction

Space

2. Functional Units


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Resultados Publicadosen revistas arbitradas

  • Journal of Genetic Programming and Evolvable Hardware 3(2), pp. 157-192, Kluwer Academic Publishers; Wu A.S., Garibay I., (2002), “The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm”

    • Introducimos el Algoritmo Genético Proporcional: PGA

    • Análisis matemático y estadístico inicial de la representación PGA

    • Experimentalmente probamos que PGA es tan competente o mejor que el GA

    • Genoma: bloques de construcción muy peculiares


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Resultados Publicadosen revistas arbitradas

  • IEEE Transactions on Systems, Man and Cybernetics Part B 34(3), pp. 1423-1434, IEEE Press; Wu A.S., Garibay I., (2003), “Intelligent Automated Control of Life Support Systems Using Proportional Representations”

    • Aplicamos el PGA a un problema muy complejo: sistema dinámico acoplado

    • NASA: Sistema de Soporte de Vida para misiones largas en el espacio

    • Proteínas mejoran resultados de GA


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Resultados Publicadosen revistas arbitradas

  • Journal of Genetic Programming and Evolvable Hardware, To Appear, Kluwer Academic Publishers; Garibay I., Wu A.S., Garibay O.(2006), “Emergence of Genomic Self-similarity in Location Independent Representations: favoring positive correlation between the form and the quality of candidate solutions”

    • Propiedad clave para el éxito de EC:

    • Correlación positiva entre la forma y la calidad de las soluciones a prueba

    • Mostramos experimentalmente que genomas del Método Proteómico se auto-organizan en estructuras auto-similares

    • Probamos formalmente que el Método Proteómico favorece esta propiedad clave


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Otros Resultados Publicadosen conferencias, etc.

  • Garibay I., Wu A.S., Garibay O., (2006), “ Emergence of Genomic Self-Similarity in Location Independent Representations: Favoring Possitive Correlations Between the Form and Quality of Candidate Solutions”, Genetic Programming and Evolvable Hardware Journal To Appear, Kluwer Academic Publishers.

  • Garibay I., Wu A.S., Garibay O. (2005), “On Favoring Positive Correlations between Form and Quality of Candidate Solutions via the Emergence of Genomic Self-Similarity”, In Proceedings of Genetic and Evolutionary Computation Conference - GECCO 2005, Washington, DC, USA, June 25-29, ACM Press. pp. 1177-1184. Nominated for Best Paper Award

  • Garibay I.(2004), “The Proteomics Approach to Evolutionary Computation: An Analysis of Proteome-based Location Independent Representations Based on the Proportional Genetic Algorithm”[short format][official format] , Doctoral Dissertation, College of Engineering and Computer Science, University of Central Florida, Orlando, Florida, 2004.

  • Garibay I., Wu A.S. (2004), “Emergence of Genomic Self-similarity in a Proteome-Based Representation”, In Proceedings of the Self-Organization and Development in Artificial and Natural Systems (SODANS) 2004, Workshop and Tutorial Proceedings: Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE IX) Boston, Massachusetts, Sep 12 2004, pp. 9-12.

  • Garibay I.,Garibay O., Wu A.S. (2004), “Effects of module encapsulation in repetitively modular genotypes on the search space”, In Proceedings of Genetic and Evolutionary Computation Conference - GECCO 2004, Seattle, USA, Jun 26-30 . Vol. 1, pp. 1125-1137

  • Garibay I., Wu A.S. (2004), “Emergent white noise behavior in location independent representations”, In Proceedings of the Self-organization in Representations for Evolutionary Algorithms Workshop - GECCO 2004, Seattle, USA, Jun 26-30 . Workshop Proceedings CD.

  • Garibay I., Wu A.S. (2004), “Workshop on Self-Organization in Representations for Evolutionary Algorithms: Building complexity from simplicity”, In Proceedings of the Self-organization in Representations for Evolutionary Algorithms Workshop - GECCO 2004, Seattle, USA, Jun 26-30 . Workshop Proceedings CD.

  • Garibay 0.,Garibay I., Wu A.S. (2004), “ No Free Lunch for Module Encapsulation”, In Proceedings of the Modularity, Regularity and Hierarchy in Open-ended Evolutionary Computation Workshop - GECCO 2004, Seattle, USA, Jun 26-30. Workshop Proceedings CD.

  • Wu A.S., Garibay I., (2003), “Intelligent Automated Control of Life Support Systems Using Proportional Representations”, IEEE Transactions on Systems, Man and Cybernetics Part B 34(3), pp. 1423-1434, IEEE Press.

  • Garibay O.,Garibay I., Wu A.S. (2003), “The modular genetic algorithm: exploiting regularities in the problem space”, In Proceedings of ISCIS 2003 The International Symposium on Computer and Information Systems at Antalya, TR, Nov 3-5 , LNCS series by Springer-Verlag, pp. 578-585.

  • Garibay O.,Garibay I., Wu A.S. (2003), “The modular genetic algorithm: motivation and first results on repetitive modularity”, In Proceedings of Genetic and Evolutionary Computation Conference Late Breaking Papers - GECCO 2003, Chicago, USA, Jul 12-16 , pp. 100-107

  • Garibay I., Wu A.S. (2003), “Cross-fertilization between Proteomics and Computational Synthesis”, In proceedings of the 2003 AAAI Spring Symposium Series---Computational Synthesis at Stanford.

  • Wu A.S., Garibay I., (2002), “The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm”, Genetic Programming and Evolvable Hardware 3(2), pp. 157-192, Kluwer Academic Publishers.

  • Wu A.S., Garibay I., (2002), “The Proportional Genetic Algorithm Representation”, In Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2002, p. 703, Morgan Kaufmann Publishers.

  • Wu A.S., Garibay I., (2002), “The Proportional Genetic Algorithm”, Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, GECCO 2002, p. 200-205, AAAI.

  • Garibay I., (2000), “Generating Text with a Theorem Prover”, Proceedings of the 6th Applied Natural Language Processing and 1st Meeting of the North American Chapter of the Association of Computational Linguistics, Student Research Workshop, pp. 13-18.


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