Conditional Programming of Cells, Advanced Computational Methods, and Intelligent Systems in Prostate Cancer Research
Special Issue Editor
Special Issue Information
Dear Colleagues,
The biology of prostate cancer is shaped not only by genetic alterations but also by the dynamic conditions cells encounter in their environment. In recent years, researchers have begun exploring how prostate cancer cells respond to controlled cues in culture, including nutrient shifts, hormonal changes, mechanical forces, or targeted molecular stimuli. These condition-dependent behaviors offer insight into tumor progression, treatment resistance, and lineage plasticity. When paired with modern computational models, they provide an opportunity to study how cancer cells “decide” between different fates under specific circumstances.
This Special Issue highlights work that brings together the conditional programming of cells in culture with emerging technologies in machine learning, computational biology, and intelligent systems. Relevant topics include studies that examine how prostate cancer cells respond to defined environmental changes. This includes investigations of reversible or state-dependent cellular programs; computational models that predict transitions between cell states and frameworks that connect condition-specific culture data with imaging, pathology, or clinical outcomes. We also welcome research on organoid systems, co-culture models, and microenvironment-mimicking platforms, provided the focus is on understanding how conditional cues shape cellular behavior.
By encouraging contributions from cell biologists, computational scientists, and clinicians, we aim to deepen understanding of how prostate cancer cells adapt under pressure, and how these insights can guide better diagnostic and therapeutic strategies.
Within this Special Issue, research may cover the following topics:
- State-dependent cell behavior, where cells shift phenotype or signaling patterns when exposed to defined conditions.
- Hormone-responsive transitions, especially those related to androgen dependence and resistance.
- Microenvironment-guided programming, examining how cues such as stromal factors or stress conditions influence cell fate.
- Organoid and ex vivo systems used to explore conditional lineage decisions.
- Condition-aware computational models, predicting how cells transition between states or acquire treatment-tolerant features.
- Integration of culture-based data with AI, linking cell behavior to imaging, pathology, or genomics.
- Interpretation, modeling, and discovery of condition-specific principles rather than actionable laboratory protocols.
Dr. Ahmed Fadiel
Guest Editor
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Keywords
- prostate cancer
- conditional programming
- cellular reprogramming
- computational biology
- cell-state transitions
- lineage plasticity
- tumor microenvironment
- ex vivo models
- machine learning
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