Department of Computational Biology, Lund University, Lund, Sweden.
Wiley Interdiscip Rev Syst Biol Med. 2019 Jan;11(1):e1424. doi: 10.1002/wsbm.1424. Epub 2018 Apr 16.
As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.
随着细胞和分子生物学变得越来越定量，人们对不同分辨率的机械建模产生了浓厚的兴趣。这些模型主要涉及动力学，包括基因和蛋白质相互作用以及细胞群体动力学。这些模型的最终目标是提供实验预测，现在这一目标正在实现。然而，即使没有成熟的预测，动力学模型也可以将大量的实验结果压缩成一种可以支持数据解释的形式，并且重要的是，通过在计算机上“转动旋钮”来提出新的实验。一旦制定了动力学模型，就可以根据浓度的分子速率方程或仅涉及有限数量副本的随机模拟来执行这些模型。发育过程，特别是干细胞和祖细胞的分化过程，不仅是热门话题，而且由于细胞决策中涉及的关键基因数量有限，特别适合动力学建模。在过去的十年中，干细胞和祖细胞的分化过程一直是实验研究的重点，其中一些研究集中在胚胎和造血干细胞上。控制这些过程的基因和蛋白质相互作用可以通过二进制布尔规则或具有交互作用的连续值模型来建模，交互作用由结合强度确定。从这些动力学模型中已经出现了概念上的见解和经过测试的预测。在这里，我们回顾了应用于干细胞发育系统的动力学建模工作，重点是造血。我们强调了未来的挑战，包括整合细胞动力学和转录模型的多尺度模型。本文属于以下类别：系统性质和过程的模型 > 机械模型 发育生物学 > 干细胞生物学和再生。
