Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Sci China Life Sci. 2017 Jun;60(6):627-646. doi: 10.1007/s11427-017-9059-y. Epub 2017 May 29.
A decade ago mainstream molecular biologists regarded it impossible or biologically ill-motivated to understand the dynamics of complex biological phenomena, such as cancer genesis and progression, from a network perspective. Indeed, there are numerical difficulties even for those who were determined to explore along this direction. Undeterred, seven years ago a group of Chinese scientists started a program aiming to obtain quantitative connections between tumors and network dynamics. Many interesting results have been obtained. In this paper we wish to test such idea from a different angle: the connection between a normal biological process and the network dynamics. We have taken early myelopoiesis as our biological model. A standard roadmap for the cell-fate diversification during hematopoiesis has already been well established experimentally, yet little was known for its underpinning dynamical mechanisms. Compounding this difficulty there were additional experimental challenges, such as the seemingly conflicting hematopoietic roadmaps and the cell-fate inter-conversion events. With early myeloid cell-fate determination in mind, we constructed a core molecular endogenous network from well-documented gene regulation and signal transduction knowledge. Turning the network into a set of dynamical equations, we found computationally several structurally robust states. Those states nicely correspond to known cell phenotypes. We also found the states connecting those stable states. They reveal the developmental routes-how one stable state would most likely turn into another stable state. Such interconnected network among stable states enabled a natural organization of cell-fates into a multi-stable state landscape. Accordingly, both the myeloid cell phenotypes and the standard roadmap were explained mechanistically in a straightforward manner. Furthermore, recent challenging observations were also explained naturally. Moreover, the landscape visually enables a prediction of a pool of additional cell states and developmental routes, including the non-sequential and cross-branch transitions, which are testable by future experiments. In summary, the endogenous network dynamics provide an integrated quantitative framework to understand the heterogeneity and lineage commitment in myeloid progenitors.
十年前，主流的分子生物学家认为，从网络的角度来理解复杂的生物学现象，如癌症的发生和发展，是不可能的或者在生物学上是没有动机的。事实上，即使是那些决心沿着这条方向探索的人，也存在数值上的困难。尽管如此，七年前，一群中国科学家开始了一个旨在获得肿瘤和网络动力学之间定量联系的项目。他们已经取得了许多有趣的成果。在本文中，我们希望从一个不同的角度来检验这个想法：正常的生物学过程与网络动力学之间的联系。我们选择了早期骨髓细胞生成作为我们的生物学模型。一个标准的造血细胞命运多样化的路线图已经在实验中得到了很好的建立，但对于其基础动力学机制却知之甚少。更糟糕的是，还有其他实验方面的挑战，如看似矛盾的造血路线图和细胞命运的相互转换事件。考虑到早期髓样细胞命运的决定，我们从有充分文献记载的基因调控和信号转导知识中构建了一个核心分子内源性网络。将网络转化为一组动力学方程，我们通过计算发现了几个结构上稳定的状态。这些状态与已知的细胞表型很好地对应。我们还发现了连接这些稳定状态的状态。它们揭示了发育途径——一个稳定状态如何最有可能转变成另一个稳定状态。这些稳定状态之间的互联网络使细胞命运自然地组织成一个多稳定状态的景观。因此，骨髓细胞表型和标准路线图都以一种直接的方式从机制上得到了解释。此外，最近的挑战性观察结果也得到了自然的解释。此外，该景观直观地预测了一个额外的细胞状态和发育途径的池，包括非连续和跨分支的转变，这些可以通过未来的实验来验证。总之，内源性网络动力学为理解髓系祖细胞的异质性和谱系决定提供了一个综合的定量框架。
