Chickarmane Vijay, Olariu Victor, Peterson Carsten
Computational Biology & Biological Physics, Lund University, Lund, Sweden.
BMC Syst Biol. 2012 Aug 13;6:98. doi: 10.1186/1752-0509-6-98.
Embryonic stem cells (ESC) have the capacity to self-renew and remain pluripotent, while continuously providing a source of a variety of differentiated cell types. Understanding what governs these properties at the molecular level is crucial for stem cell biology and its application to regenerative medicine. Of particular relevance is to elucidate those molecular interactions which govern the reprogramming of somatic cells into ESC. A computational approach can be used as a framework to explore the dynamics of a simplified network of the ESC with the aim to understand how stem cells differentiate and also how they can be reprogrammed from somatic cells.
We propose a computational model of the embryonic stem cell network, in which a core set of transcription factors (TFs) interact with each other and are induced by external factors. A stochastic treatment of the network dynamics suggests that NANOG heterogeneity is the deciding factor for the stem cell fate. In particular, our results show that the decision of staying in the ground state or commitment to a differentiated state is fundamentally stochastic, and can be modulated by the addition of external factors (2i/3i media), which have the effect of reducing fluctuations in NANOG expression. Our model also hosts reprogramming of a committed cell into an ESC by over-expressing OCT4. In this context, we recapitulate the important experimental result that reprogramming efficiency peaks when OCT4 is over-expressed within a specific range of values.
We have demonstrated how a stochastic computational model based upon a simplified network of TFs in ESCs can elucidate several key observed dynamical features. It accounts for (i) the observed heterogeneity of key regulators, (ii) characterizes the ESC under certain external stimuli conditions and (iii) describes the occurrence of transitions from the ESC to the differentiated state. Furthermore, the model (iv) provides a framework for reprogramming from somatic cells and conveys an understanding of reprogramming efficiency as a function of OCT4 over-expression.
胚胎干细胞（ESC）具有自我更新能力并保持多能性，同时持续提供各种分化细胞类型的来源。在分子水平上理解调控这些特性的因素对于干细胞生物学及其在再生医学中的应用至关重要。特别相关的是阐明那些调控体细胞重编程为ESC的分子相互作用。一种计算方法可以用作框架来探索ESC简化网络的动态，旨在理解干细胞如何分化以及它们如何从体细胞重编程而来。
我们提出了一个胚胎干细胞网络的计算模型，其中一组核心转录因子（TFs）相互作用并受外部因素诱导。对网络动态的随机处理表明，NANOG的异质性是干细胞命运的决定因素。特别是，我们的结果表明，维持基态或进入分化状态的决定从根本上说是随机的，并且可以通过添加外部因素（2i/3i培养基）来调节，这些因素具有减少NANOG表达波动的作用。我们的模型还通过过表达OCT4将已分化细胞重编程为ESC。在这种情况下，我们重现了重要的实验结果，即当OCT4在特定值范围内过表达时，重编程效率达到峰值。
我们已经证明了基于ESC中TFs简化网络的随机计算模型如何能够阐明几个关键的观察到的动态特征。它解释了（i）关键调节因子观察到的异质性，（ii）在某些外部刺激条件下对ESC进行了表征，以及（iii）描述了从ESC到分化状态的转变的发生。此外，该模型（iv）提供了一个从体细胞重编程的框架，并传达了对作为OCT4过表达函数的重编程效率的理解。
