Yuman Wang , Shuli Chen , Zhaolian Lu , Yu Liu , Jie Hu , Da Zhou
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Inferring absolute cell numbers from relative proportion in stochastic models with cell plasticity
Quantifying dynamic changes in cell populations is crucial for a comprehensive understanding of biological processes such as cell proliferation, injury repair, and disease progression. However, compared to directly measuring the absolute cell numbers of specific subpopulations, relative proportion data demonstrate greater reproducibility and yield more stable, reliable outcomes. Therefore, inferring absolute cell numbers from relative proportion data may present a novel approach for effectively predicting changes in cell population sizes. To address this, we establish two mathematical mappings between cell proportions and population sizes using moment equations derived from stochastic cell-plasticity models. Notably, our findings indicate that one of these mappings does not require prior knowledge of the initial population size, highlighting the value of incorporating variance information into cell proportion data. We evaluated the robustness of our methods from multiple perspectives and extended their application to various biological mechanisms within the context of cell plasticity models. These methods help mitigate the limitations associated with the direct measurement of absolute cell counts through experimental techniques. Moreover, they provide new insights into leveraging the stochastic dynamics of cell populations to quantify interactions between different biomasses within the system.
期刊介绍:
The Journal of Theoretical Biology is the leading forum for theoretical perspectives that give insight into biological processes. It covers a very wide range of topics and is of interest to biologists in many areas of research, including:
• Brain and Neuroscience
• Cancer Growth and Treatment
• Cell Biology
• Developmental Biology
• Ecology
• Evolution
• Immunology,
• Infectious and non-infectious Diseases,
• Mathematical, Computational, Biophysical and Statistical Modeling
• Microbiology, Molecular Biology, and Biochemistry
• Networks and Complex Systems
• Physiology
• Pharmacodynamics
• Animal Behavior and Game Theory
Acceptable papers are those that bear significant importance on the biology per se being presented, and not on the mathematical analysis. Papers that include some data or experimental material bearing on theory will be considered, including those that contain comparative study, statistical data analysis, mathematical proof, computer simulations, experiments, field observations, or even philosophical arguments, which are all methods to support or reject theoretical ideas. However, there should be a concerted effort to make papers intelligible to biologists in the chosen field.