Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
Methods Mol Biol. 2022;2399:343-365. doi: 10.1007/978-1-0716-1831-8_14.
Extracting mechanistic knowledge from the spatial and temporal phenotypes of morphogenesis is a current challenge due to the complexity of biological regulation and their feedback loops. Furthermore, these regulatory interactions are also linked to the biophysical forces that shape a developing tissue, creating complex interactions responsible for emergent patterns and forms. Here we show how a computational systems biology approach can aid in the understanding of morphogenesis from a mechanistic perspective. This methodology integrates the modeling of tissues and whole-embryos with dynamical systems, the reverse engineering of parameters or even whole equations with machine learning, and the generation of precise computational predictions that can be tested at the bench. To implement and perform the computational steps in the methodology, we present user-friendly tools, computer code, and guidelines. The principles of this methodology are general and can be adapted to other model organisms to extract mechanistic knowledge of their morphogenesis.
由于生物调节及其反馈回路的复杂性，从形态发生的时空表型中提取机制知识是当前的一个挑战。此外，这些调节相互作用也与塑造发育组织的生物物理力有关，这些力会产生复杂的相互作用，从而导致出现新的模式和形态。在这里，我们展示了计算系统生物学方法如何从机制角度帮助理解形态发生。该方法将组织和整个胚胎的建模与动态系统、使用机器学习反向工程参数甚至整个方程以及生成可在实验台上进行测试的精确计算预测结合起来。为了实现和执行该方法中的计算步骤，我们提供了用户友好的工具、计算机代码和指南。该方法的原理具有通用性，可以适应其他模式生物，以提取其形态发生的机制知识。
