.png){kind=icon}
Return
Efficient computational frameworks for whole-cell models
.png){kind=icon}
OA
AI
DOI：10.1016/j.coisb.2026.100610.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Whole-cell models aim to predict cellular phenotypes from genotype and environment by integrating mechanistic, genome-scale information about biochemical processes into a single computational framework. Despite their conceptual promise and demonstrated utility in exposing knowledge gaps, guiding experiments, and enabling mechanistic insight, progress has been arguably slow, with only a few published models to date. One major reason is efficiency—substantial computational and software-related challenges that grow with biological scale and complexity. In this review, we examine the current landscape of whole-cell modeling through the lens of computational efficiency, organizing challenges and recent advances across four interconnected areas: biological data/knowledge organization, model construction, simulation and analysis, and dissemination/long-term model evolution. We discuss issues surrounding data availability, interoperability, and aggregation; limitations of existing model construction workflows and formats; algorithmic and performance bottlenecks in hybrid, multi-scale simulation; and persistent difficulties in parameter estimation, validation, and reproducibility. We highlight emerging solutions, including software-agnostic model formats, rule-based and database-driven model generation, compiled and parallel simulation engines, and standardized frameworks for model–data comparison. Finally, we discuss challenges in usability, sustainability, and community-based development, emphasizing the need for professional software engineering practices and collaborative infrastructures to support long-lived, extensible whole-cell models, gleaning lessons from other similar efforts. These analyses suggest actionable directions for developing scalable, reusable, and community-driven computational frameworks to advance whole-cell modeling towards broad basic and translational impact.
Journal
IF:
2.2
Papers:
238
Citations:
1.1K
Organization
Cited Papers
No cited papers available