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Purpose of Review <p>Human pathogenic fungi are increasingly recognized as major threats to human health, yet their complex biology and interactions with the host remain poorly understood. This review highlights the role of image-based systems biology in fungal infection research, emphasizing how computational models bridge experimental observations and mechanistic understanding.</p> Recent Findings <p>Recent advances in imaging, quantitative image analysis, and mechanistic modeling have enabled dynamic and precise characterization of fungal infections. Integrative studies in <i>Candida albicans</i> and <i>Aspergillus fumigatus</i> have linked measurable cellular behaviors to infection outcomes, revealing how fungal growth, immune evasion, and host responses jointly determine disease progression. Computational models now reproduce infection dynamics, identify key parameters shaping immune control, and guide therapeutic strategies.</p> Summary <p>Coupling quantitative imaging with computational modeling transforms fungal systems biology from descriptive observation to predictive and mechanistic insights, enabling the rational design of diagnostics and therapeutic strategies.</p>

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From Observation to Mechanistic Insight: Image-based Systems Biology of Human Pathogenic Fungi

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

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  • Anastasia Solomatina,
  • Mounir Zerdani,
  • Karoline Schied,
  • Marc Thilo Figge

摘要

Purpose of Review

Human pathogenic fungi are increasingly recognized as major threats to human health, yet their complex biology and interactions with the host remain poorly understood. This review highlights the role of image-based systems biology in fungal infection research, emphasizing how computational models bridge experimental observations and mechanistic understanding.

Recent Findings

Recent advances in imaging, quantitative image analysis, and mechanistic modeling have enabled dynamic and precise characterization of fungal infections. Integrative studies in Candida albicans and Aspergillus fumigatus have linked measurable cellular behaviors to infection outcomes, revealing how fungal growth, immune evasion, and host responses jointly determine disease progression. Computational models now reproduce infection dynamics, identify key parameters shaping immune control, and guide therapeutic strategies.

Summary

Coupling quantitative imaging with computational modeling transforms fungal systems biology from descriptive observation to predictive and mechanistic insights, enabling the rational design of diagnostics and therapeutic strategies.