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Thermodynamically constrained averaging theory for cancer growth modelling * * Horizon 2020 MSCA grant agreement No 642295 www.melplex.eu

机译:用于癌症生长建模的热力学平均算法 * * Horizo​​n 2020 MSCA授予协议编号642295 www.melplex.eu

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Abstract: In Systems Biology, network models are often used to describe intracellular mechanisms at the cellular level. The obtained results are difficult to translate into three dimensional biological systems of higher order. The multiplicity and time dependency of cellular system boundaries, mechanical phenomena and spatial concentration gradients affect the intercellular relations and communication of biochemical networks. These environmental effects can be integrated with our promising cancer modelling environment, that is based on thermodynamically constrained averaging theory (TCAT). Especially, the TCAT parameter viscosity can be used as critical player in tumour evolution. Strong cell-cell contacts and a high degree of differentiation make cancer cells viscous and support compact tumour growth with high tumour cell density and accompanied displacement of the extracellular material. In contrast, dedifferentiation and losing of cell-cell contacts make cancer cells more fluid and lead to an infiltrating tumour growth behaviour without resistance due to the ECM. The fast expanding tumour front of the invasive type consumes oxygen and the limited oxygen availability behind the invasive front results automatically in a much smaller average tumour cell density in the tumour core. The proposed modelling technique is most suitable for tumour growth phenomena in stiff tissues like skin or bone with high content of extracellular matrix.
机译:摘要:在系统生物学中,网络模型通常用于描述细胞水平的细胞内机制。所获得的结果难以转化为更高阶的三维生物系统。细胞系统边界,机械现象和空间浓度梯度的多样性和时间依赖性会影响细胞间的关系和生化网络的通讯。这些环境影响可以与我们有前途的癌症建模环境(基于热力学约束平均理论(TCAT))整合在一起。尤其是,TCAT参数粘度可以用作肿瘤发展的关键因素。牢固的细胞间接触和高度分化使癌细胞具有粘性,并以高肿瘤细胞密度和伴随细胞外物质的移位支持紧密的肿瘤生长。相反,细胞与细胞间接触的去分化和丧失使癌细胞更易流动,并导致浸润性肿瘤生长行为,而没有由于ECM引起的耐药性。侵袭性类型的快速扩展的肿瘤前沿消耗氧气,而侵袭性前沿后面有限的氧气供应会自动导致肿瘤核心中的平均肿瘤细胞密度小得多。所提出的建模技术最适合于具有大量细胞外基质的刚性组织(如皮肤或骨骼)中的肿瘤生长现象。

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