在差异粘附假设下估计细胞-细胞相互作用强度的统计方法。

Q1 Mathematics
Mathieu Emily, Olivier François
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引用次数: 5

摘要

背景:差异粘附假说(DAH)是组织内细胞组织的一种理论,已被几个生物学实验验证,并针对几种替代计算模型进行了测试。结果:在本研究中,开发了一种用于估计粘附强度的统计方法,将早期的离散晶格模型纳入连续标记点过程框架。该框架允许描述遍历马尔可夫链蒙特卡罗算法,该算法可以模拟模型并重现经验生物模式。用模拟验证了基于伪似然近似的估计过程,并给出了对β -连环蛋白标记物染色的成神经管细胞瘤的简要应用。结论:我们的模型包括细胞-细胞粘附强度作为统计参数。该参数的估计过程与实验数据一致,可用于高通量癌症研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A statistical approach to estimating the strength of cell-cell interactions under the differential adhesion hypothesis.

A statistical approach to estimating the strength of cell-cell interactions under the differential adhesion hypothesis.

A statistical approach to estimating the strength of cell-cell interactions under the differential adhesion hypothesis.

A statistical approach to estimating the strength of cell-cell interactions under the differential adhesion hypothesis.

Background: The Differential Adhesion Hypothesis (DAH) is a theory of the organization of cells within a tissue which has been validated by several biological experiments and tested against several alternative computational models.

Results: In this study, a statistical approach was developed for the estimation of the strength of adhesion, incorporating earlier discrete lattice models into a continuous marked point process framework. This framework allows to describe an ergodic Markov Chain Monte Carlo algorithm that can simulate the model and reproduce empirical biological patterns. The estimation procedure, based on a pseudo-likelihood approximation, is validated with simulations, and a brief application to medulloblastoma stained by beta-catenin markers is given.

Conclusion: Our model includes the strength of cell-cell adhesion as a statistical parameter. The estimation procedure for this parameter is consistent with experimental data and would be useful for high-throughput cancer studies.

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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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