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首页> 外文会议>Computational intelligence methods for bioinformatics and biostatistics >Simulations of the EGFR - KRAS - MAPK Signalling Network in Colon Cancer. Virtual Mutations and Virtual Treatments with Inhibitors Have More Important Effects Than a 10 Times Range of Normal Parameters and Rates Fluctuations
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Simulations of the EGFR - KRAS - MAPK Signalling Network in Colon Cancer. Virtual Mutations and Virtual Treatments with Inhibitors Have More Important Effects Than a 10 Times Range of Normal Parameters and Rates Fluctuations

机译：结肠癌中EGFR-KRAS-MAPK信号网络的模拟。使用抑制剂进行虚拟突变和虚拟处理的影响比正常参数和速率波动的10倍范围更为重要

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The fragment of the signaling network we have considered was formally described as a sort of circuit diagram, a Molecular Interaction Map (MIM). We have mostly followed the syntactic rules proposed by Kurt W. Kohn [1,10,11]. In our MIM we drew 19 basic species. Our dynamic simulations involve 46 modified species and complexes, 50 forward reactions, 50 backward reactions, 17 catalytic activities. A significant amount of parameters concerning molecular concentrations, association rates, dissociation rates and turnover numbers, are known for this intensively studied neighborhood of the signaling network. In other cases, molecular, cellular and even clinical data generate additional indirect constraints. Some unknown parameters have been adjusted to satisfy these indirect constraints. In order to avoid hidden bugs in writing the software we have used two independent approaches: a) a more classic approach using Ordinary Differential Equations (ODEs); b) a stochas tic simulation engine, written in Java, based on the Gillespie algorithm: we obtained overlapping results. For a quiescent and EGF stimulated network we have obtained a behavior in good agreement with what is experimentally known. We have introduced virtual mutations (excess of function) for EGFR, KRAS and BRAF onco-proteins. We have also con sidered virtual inhibitions induced from different EGFR, KRAS, BRAF and MEKPP inhibitors. Drugs of this kind are already in the phase of preclinical and clinical studies. The major results of our work are the following: 3.16x or 3.16/ fluctuations of total concentrations of independent molecular species or fluctuations of rates, were introduced systematically. We examined the effects on the plateau levels of the 61 parameters representing all molecular species / complexes. Fluctuations of concentrations generated scores of deviation from the normal reference situation with median = 5, Ist-IIIrd quartile = 1-9. Fluctuations of rates generated scores of deviation with median and Ist-IIIrd quartile = 0. In the case of virtual mutations the deviation from the normal reference situation generated scores in the range 33-115, well above the fluctuation range. The addition of a target-specific virtual inhibitor to its respective virtual mutation reduced the deviation scores by 64% (KRAS), 67% (BRAF), 97% (EGFR mutation) and 90% (EGFR strong stimulation). A double alteration (EGFR & KRAS) could be best inhibited by the association of the two corresponding inhibitors. In conclusion, the effects of virtual mutations and virtual inhibitors seem definitely more important than noise random fluctuations in concentrations and rates.
机译：我们已经将信号网络的片段正式描述为一种电路图，即分子相互作用图（MIM）。我们主要遵循了Kurt W. Kohn [1,10,11]提出的句法规则。在我们的MIM中，我们绘制了19种基本物种。我们的动态模拟涉及46个修饰物种和复合物，50个正向反应，50个向后反应，17个催化活性。对于信号网络的这一深入研究的邻域，涉及分子浓度，缔合速率，解离速率和周转数的大量参数是已知的。在其他情况下，分子，细胞甚至临床数据会产生其他间接约束。已调整一些未知参数以满足这些间接约束。为了避免编写软件时出现隐藏的错误，我们使用了两种独立的方法：a）使用常微分方程（ODE）的更为经典的方法； b）一个基于Gillespie算法的用Java编写的随机仿真引擎：我们获得了重叠的结果。对于静态和EGF刺激的网络，我们获得的行为与实验已知的行为非常吻合。我们已经为EGFR，KRAS和BRAF癌蛋白引入了虚拟突变（功能过度）。我们还考虑了由不同的EGFR，KRAS，BRAF和MEKPP抑制剂诱导的虚拟抑制作用。这种药物已经处于临床前和临床研究阶段。我们工作的主要结果如下：系统地介绍了独立分子物种的总浓度的3.16倍或3.16 /的波动或速率的波动。我们检查了代表所有分子种类/复合物的61个参数对平台水平的影响。浓度的波动产生了偏离正常参考情况的分数，中位数= 5，Ist-IIIrd四分位数= 1-9。比率的波动会产生中位数和Ist-IIIrd四分位数= 0的偏差得分。在虚拟突变的情况下，与正常参考情况的偏差会在33-115范围内产生得分，远高于波动范围。在其各自的虚拟突变体中添加靶标特异性虚拟抑制剂后，偏差得分分别降低了64％（KRAS），67％（BRAF），97％（EGFR突变）和90％（EGFR强刺激）。两种相应抑制剂的结合可以最好地抑制双重改变（EGFR和KRAS）。总之，虚拟突变和虚拟抑制剂的作用显然比浓度和速率的噪声随机波动更为重要。

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  • 来源
  • 会议地点 Genoa(IT);Genoa(IT)
  • 作者

    Nicoletta Castagnino; Lorenzo Tortolina; Roberto Montagna; Raffaele Pesenti; Anahi Balbi; Silvio Parodi;

  • 作者单位

    Department of Oncology, Biology and Genetics, University of Genoa National Cancer Institute of Genoa, Largo R. Benzi 10, 16132, Genova, Italy;

    Department of Oncology, Biology and Genetics, University of Genoa National Cancer Institute of Genoa, Largo R. Benzi 10, 16132, Genova, Italy;

    Department of Informatics and Information Sciences, University of Genoa, Via Dodecaneso 35, 16146, Genova, Italy;

    Department of Applied Mathematics, University of Ca' Foscari of Venice, Dorsoduro 3825-30123, Venezia, Italy;

    Advanced Biotechnology Center of Genoa, Largo R. Benzi 10, 16132, Genova, Italy;

    Department of Oncology, Biology and Genetics, University of Genoa National Cancer Institute of Genoa, Largo R. Benzi 10, 16132, Genova, Italy;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物工程学（生物技术）;人工智能理论;
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  • 入库时间 2022-08-26 14:04:26

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