Other What Are the Research Topics in Statistical Physics Applied to Biology?

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The discussion highlights the application of statistical physics in biological contexts, particularly focusing on protein folding and cellular structures. Key areas of research include the use of statistical mechanics to explore cell morphology and multicellular structures, exemplified by the Cellular Potts model. Additionally, the study of polymers through statistical physics is crucial for understanding DNA packaging within the nucleus, with ongoing research aimed at modeling this complex process. The conversation also emphasizes the potential of statistical physics approaches to enhance understanding of cellular differentiation, especially in light of the rapid data generation in biology that outpaces analysis capabilities. The need for improved theoretical models to address complex biological phenomena, such as stem cell differentiation, is underscored.
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I'm undergrad physics student and I have read some statistical physics like equilibrium statistical physics, Langevin model and Fokker-Planck equation. I have developed interest in application of statistical physics in biology like protein folding. So what are the other research topics that lie in domain of application of statistical physics to solve biological problems ?
 
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There has been some work in applying statistical mechanics to understand cell morphology and multi-cellular structures, for example: https://en.wikipedia.org/wiki/Cellular_Potts_model

Statistical physics, especially when applied to the study of polymers, is not only useful for proteins, but for understanding DNA as well. There is a huge amount of interest currently in determining the three dimensional structure of how DNA gets packaged into the nucleus, and various groups are trying to develop models for how this might occur. See for example:
http://www.aidenlab.org/papers/Science.Genome.Folding.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722799/
http://www.nature.com/nature/journal/v529/n7586/full/nature16496.html

Others have argued that approaches inspired by statistical physics may be helpful for understanding cellular differentiation. Biology is certainly entering an era where we can generate data faster than we can analyze it, and better theoretical models are required for understanding complex processes like stem cell differentiation:
http://www.sciencedirect.com/science/article/pii/S0092867413008957
 
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Hey, I am Andreas from Germany. I am currently 35 years old and I want to relearn math and physics. This is not one of these regular questions when it comes to this matter. So... I am very realistic about it. I know that there are severe contraints when it comes to selfstudy compared to a regular school and/or university (structure, peers, teachers, learning groups, tests, access to papers and so on) . I will never get a job in this field and I will never be taken serious by "real"...
Yesterday, 9/5/2025, when I was surfing, I found an article The Schwarzschild solution contains three problems, which can be easily solved - Journal of King Saud University - Science ABUNDANCE ESTIMATION IN AN ARID ENVIRONMENT https://jksus.org/the-schwarzschild-solution-contains-three-problems-which-can-be-easily-solved/ that has the derivation of a line element as a corrected version of the Schwarzschild solution to Einstein’s field equation. This article's date received is 2022-11-15...

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