Agent-based Signaling Models

Agent-Based Signaling Models is a computational modeling framework in signal transduction research that simulates intracellular signaling dynamics by representing individual molecular entities as autonomous agents with defined rules governing their interactions, localization, and state transitions, thereby capturing spatial heterogeneity, stochasticity, and emergent behaviors not readily apparent in population-averaged approaches.

27

Publications

1.1K

Citations

110

Authors

53

Institutions

Publications per year

2017–2026

6

Authors

110

  1. MH
    Mike Holcombe

    University of Sheffield, United Kingdom

    3 publications · 236 citations

    3

    h-index

  2. SM
    Shayn M. Peirce

    University of Virginia, United States

    3 publications · 91 citations

    3

    h-index

  3. MA
    Mary Ann Raymond-Stinz

    University of New Mexico, United States

    2 publications · 87 citations

    2

    h-index

  4. JA
    Jason A. Papin

    University of Virginia, United States

    2 publications · 51 citations

    2

    h-index

  5. SY
    Shujie Yang

    University of Iowa, United States

    2 publications · 87 citations

    2

    h-index

1–5 of 110

Venues

  1. BMC Systems Biology

    4 publications · 130 citations

    4

    h-index

  2. Biosystems

    2 publications · 227 citations

    2

    h-index

  3. PLoS ONE

    2 publications · 140 citations

    2

    h-index

  4. Bioinformatics

    1 publications · 19 citations

    1

    h-index

  5. Ecology and Society

    1 publications · 78 citations

    1

    h-index

1–5