Annals of the New York Academy of Sciences

Cover image for Vol. 1115 Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference

December 2007

Volume 1115 Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference

Pages xi–xiv, 1–287

  1. Preface

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
    1. Preface (pages xi–xiv)

      Gustavo Stolovitzky and Andrea Califano

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.022

  2. Part I. Community Efforts for Pathway Inference

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
  3. Part II. Overview of Reverse Engineering Methods: Experiment and Theory

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
    1. Reconstructing Signal Transduction Pathways : Challenges and Opportunities (pages 32–50)

      ARNOLD J. LEVINE, WENWEI HU, ZHAOHUI FENG and GERMAN GIL

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.018

  4. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
    1. Comparison of Reverse-Engineering Methods Using an in Silico Network (pages 73–89)

      DIOGO CAMACHO, PAOLA VERA LICONA, PEDRO MENDES and REINHARD LAUBENBACHER

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.006

    2. Reconstruction of Metabolic Networks from High-Throughput Metabolite Profiling Data : In Silico Analysis of Red Blood Cell Metabolism (pages 102–115)

      ILYA NEMENMAN, G. SEAN ESCOLA, WILLIAM S. HLAVACEK, PAT J. UNKEFER, CLIFFORD J. UNKEFER and MICHAEL E. WALL

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.013

  5. Part IV. Theoretical Analyses of Reverse Engineering Algorithms

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
  6. Part V. Some Reverse Engineering Algorithms

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
    1. Reverse Engineering of Dynamic Networks (pages 168–177)

      B. STIGLER, A. JARRAH, M. STILLMAN and R. LAUBENBACHER

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.012

    2. Learning Regulatory Programs That Accurately Predict Differential Expression with MEDUSA (pages 178–202)

      ANSHUL KUNDAJE, STEVE LIANOGLOU, XUEJING LI, DAVID QUIGLEY, MARTA ARIAS, CHRIS H. WIGGINS, LI ZHANG and CHRISTINA LESLIE

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.020

  7. Part VI. Reverse Engineering of Parameters in Quantitative Models

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
    1. Extracting Falsifiable Predictions from Sloppy Models (pages 203–211)

      RYAN N. GUTENKUNST, FERGAL P. CASEY, JOSHUA J. WATERFALL, CHRISTOPHER R. MYERS and JAMES P. SETHNA

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.003

    2. Dynamic Pathway Modeling : Feasibility Analysis and Optimal Experimental Design (pages 212–220)

      THOMAS MAIWALD, CLEMENS KREUTZ, ANDREA C. PFEIFER, SEBASTIAN BOHL, URSULA KLINGMÜLLER and JENS TIMMER

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.007

    3. Sensitivity Analysis of a Computational Model of the IKK–NF-κB–IκBα–A20 Signal Transduction Network (pages 221–239)

      JAEWOOK JOO, STEVE PLIMPTON, SHAWN MARTIN, LAURA SWILER and JEAN-LOUP FAULON

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.014

  8. Part VII. Integration of Prior Information in Reverse Engineering Algorithms

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
  9. Index of Contributors

    1. Top of page
    2. Preface
    3. Part I. Community Efforts for Pathway Inference
    4. Part II. Overview of Reverse Engineering Methods: Experiment and Theory
    5. Part III. Establishing in Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
    6. Part IV. Theoretical Analyses of Reverse Engineering Algorithms
    7. Part V. Some Reverse Engineering Algorithms
    8. Part VI. Reverse Engineering of Parameters in Quantitative Models
    9. Part VII. Integration of Prior Information in Reverse Engineering Algorithms
    10. Index of Contributors
    1. Index of Contributors (page 287)

      Article first published online: 16 NOV 2007 | DOI: 10.1196/annals.1407.auindex_1

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