Sattari Sulimon, S Basak Udoy, Mohiuddin M, Toda Mikito, Komatsuzaki Tamiki
Research Institute for Electronic Science, Hokkaido University, Sapporo, Hokkaido 001‑0020, Japan.
Pabna University of Science and Technology, Pabna 6600, Bangladesh.
Biophys Physicobiol. 2024 Mar 22;21(Supplemental):e211014. doi: 10.2142/biophysico.bppb-v21.s014. eCollection 2024.
In collective systems, influence of individuals can permeate an entire group through indirect interactionscom-plicating any scheme to understand individual roles from observations. A typical approach to understand an individuals influence on another involves consideration of confounding factors, for example, by conditioning on other individuals outside of the pair. This becomes unfeasible in many cases as the number of individuals increases. In this article, we review some of the unforeseen problems that arise in understanding individual influence in a collective such as single cells, as well as some of the recent works which address these issues using tools from information theory.
在集体系统中，个体的影响可以通过间接相互作用渗透到整个群体，这使得从观察中理解个体角色的任何方案都变得复杂。理解一个个体对另一个个体影响的典型方法涉及考虑混杂因素，例如，通过以该对个体之外的其他个体为条件。随着个体数量的增加，在许多情况下这变得不可行。在本文中，我们回顾了在理解诸如单细胞等集体中个体影响时出现的一些意外问题，以及一些使用信息论工具解决这些问题的近期研究。
