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<p>Cell-cell communication underlies key processes in development, immunity, and disease, yet capturing its mechanistic complexity remains challenging. While advances in single-cell omics have revealed new insights into cell-type diversity, mathematical modelling has become essential for deriving mechanistic understanding of their communication networks. Here, we overview established modelling approaches and highlight the need for frameworks that move beyond steady-state assumptions and single-step processes, better reflecting the nature of cell–cell communication.</p>

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Decoding cellular population dynamics through mechanistic modelling and statistical data analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Nissrin Alachkar,
  • Nicholas Kwasi-Do Ohene Opoku,
  • Nicholas A. M. Monk,
  • Kevin Thurley

摘要

Cell-cell communication underlies key processes in development, immunity, and disease, yet capturing its mechanistic complexity remains challenging. While advances in single-cell omics have revealed new insights into cell-type diversity, mathematical modelling has become essential for deriving mechanistic understanding of their communication networks. Here, we overview established modelling approaches and highlight the need for frameworks that move beyond steady-state assumptions and single-step processes, better reflecting the nature of cell–cell communication.