Modeling cell population dynamics.

Q2 Medicine In Silico Biology Pub Date : 2019-01-01 DOI:10.3233/ISB-180470
Daniel A Charlebois, Gábor Balázsi
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引用次数: 44

Abstract

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

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细胞群体动力学建模。
由于数学模型和计算机模拟能够产生见解并预测生命系统的行为,定量建模正迅速成为生物学不可或缺的一部分。单细胞模型可能无法或误导推断种群动态,因为它们没有考虑细胞之间通过代谢物或物理接触的相互作用,也没有考虑对营养物质或空间等有限资源的竞争。在这里,我们研究了通常用于建模和模拟细胞群体的方法。首先,我们介绍了可以获得分析解决方案的简单模型,然后转到需要计算方法的更复杂的场景。总的来说,我们总结了用于描述细胞群体动力学的数学模型,这可能有助于未来的模型开发,并强调了群体建模在生物学中的重要性。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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