A quantised cyclin-based cell cycle model
A quantised cyclin-based cell cycle model
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DOI：
10.1101/2020.07.13.200303
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
C. Emerson;Lindsey Bennie;D. Green;F. Currell;J. Coulter
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文献类型：
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作者：
C. Emerson;Lindsey Bennie;D. Green;F. Currell;J. Coulter
计算建模是一种重要的研究工具，有助于预测拟议治疗计划的结果或阐明肿瘤生长的机制。计算机模拟已用于癌症研究的各个方面，从DNA损伤和修复，肿瘤生长，药物/肿瘤相互作用和突变状态。事实上，建模甚至有可能在单细胞基础上理解单个蛋白质之间的相互作用。在这里，我们提出了一个计算模型的细胞周期蛋白家族（细胞周期蛋白A，B，D和E）的细胞周期网络。该模型已经使用来自同步化HUVEC系的蛋白质印迹和流式细胞术数据进行了定量，以能够确定每个细胞的细胞周期蛋白分子的绝对数量。这种量化允许模型对转换之间的阈值进行严格控制。结果表明，四种细胞周期蛋白的峰值相似，细胞周期蛋白B的峰值为5×106至9×106个分子/细胞。将该值与肌动蛋白的数量5E 8进行比较，表明尽管细胞周期蛋白家族蛋白的重要性，但其水平大约低2个数量级。所提出的模型的效率也将允许其用作更复杂模型（例如肿瘤生长模型）的内部组件，其中每个单个细胞将具有独立于相邻细胞计算的自己的细胞周期。此外，该模型还可用于帮助了解新型治疗干预对细胞周期进展的影响。蛋白质和基因网络控制着细胞的每一个生理行为，细胞周期由促进蛋白质细胞周期蛋白家族的基因网络控制。这些网络是创建准确和相关生物模型的关键。通常，这些模型呈现相对蛋白质浓度，而没有任何真实的世界对应物与其输出对应。内提出的模型通过计算一个细胞中每种细胞周期蛋白在细胞周期中的绝对浓度来显示这种方法的进步。该模型采用布尔变量来表示基因网络，无论基因是活跃的还是不活跃的，并且采用连续变量来表示蛋白质的浓度。这种杂交方法允许快速计算蛋白质浓度和细胞周期进程，从而允许可以容易地并入更大的肿瘤模型的模型，允许跟踪肿瘤内的离散细胞。
Computational modelling is an important research tool, helping predict the outcome of proposed treatment plans or to illuminate the mechanics of tumour growth. In silico modelling has been used in every aspect of cancer research from DNA damage and repair, tumour growth, drug/tumour interactions, and mutational status. Indeed, modelling even holds potential in understanding the interactions between individual proteins on a single cell basis. Here, we present a computational model of the cell cycle network of the cyclin family of proteins (cyclin A, B, D and E). This model has been quantised using western blot and flow cytometry data from a synchronised HUVEC line to enable the determination of the absolute number of cyclin protein molecules per cell. This quantification allows the model to have stringent controls over the thresholds between transitions. The results show that the peak values obtained for the four cyclins are similar with cyclin B having a peak values of 5×106 to 9×106 molecules per cell. Comparing this value with the number of actin proteins, 5E8, shows that despite their importance, the level of cyclin family proteins are approximately 2 orders of magnitude lower. The efficiency of the model presented would also allow for its use as an internal component in more complex models such as a tumour growth model, in which each individual cell would have its own cell cycle calculated independently from neighbouring cells. Additionally, the model can also be used to help understand the impact of novel therapeutic interventions on cell cycle progression. Author Summary Protein and gene networks control every physiological behaviour of cells, with the cell cycle being controlled by the network of genes that promote the cyclin family of proteins. These networks hold the key to creating accurate and relevant biological models. Normally these models are presented with relative protein concentrations without any real world counterpart to their outputs. The model presented within shows and advancement of this approach by calculating the absolute concentration of each cyclin protein in one cell as it progresses through the cell cycle. This model employs Boolean variables to represent the genetic network, either the gene is active or not, and continuous variables to represent the concentrations of the proteins. This hybridised approach allows for rapid calculations of the protein concentrations and of the cell cycle progression allowing for a model that could be easily incorporated into larger tumour models, allowing for the tracking of discrete cells within the tumour.