New mathematical approaches to learn the equations of life from noisy data

从噪声数据中学习生命方程的新数学方法

• 批准号: DP230100025
• 负责人: Prof Matthew Simpson
• 金额: $ 41.79万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230100025
• 关键词: New; mathematical; approaches; learn; equations

New mathematical models and mathematical modelling methods must be continually developed to interpret emerging biotechnology experiments. Contemporary research in tissue engineering involves growing tissues on 3d-printed scaffolds to mimic constrained in vivo geometries. Previous mathematical models of tissue growth focus on computationally expensive discrete mathematical models that are poorly suited for parameter inference and experimental design. This project will deliver and deploy high-fidelity, computationally efficient moving boundary continuum mathematical models that will: (i) predict/interpret new experiments, (ii) provide quantitative insight into biological mechanisms, and (iii) enable reproducible experimental design.

新的数学模型和数学建模方法必须不断发展，以解释新兴的生物技术实验。组织工程的当代研究涉及在3d打印支架上生长组织，以模仿体内受限的几何形状。 以前的组织生长的数学模型集中在计算昂贵的离散数学模型，不适合参数推断和实验设计。 该项目将提供和部署高保真，计算效率高的移动边界连续数学模型，这些模型将：（i）预测/解释新实验，（ii）提供对生物机制的定量见解，以及（iii）实现可重复的实验设计。