paper-with-me

Papers

Self-Organized Networks with Long-Range Interactions: Tandem Darwinian Evolution of $α$ and $β$ Tubulin

2020-08-19 · J. C. Phillips

Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality (self-organized criticality). Our theoretical analysis uses hydropathic waves to identify and contrast the functional differences between the polymerizing $\alpha$ and $\beta$ tubulin monomers, which are similar in length and secondary structures, as well as having indistinguishable phylogenetic trees. We show how evolution has improved water-driven flexibility especially for $\alpha$ tubulin, and thus facilitated heterodimer microtubule assembly, in agreement with recent atomistic simulations and topological models. We conclude that the failure of phylogenetic analysis to identify functionally specific positive Darwinian evolution has been caused by 20th century technical limitations. These are overcome using 21st century quantitative mathematical methods based on thermodynamic scaling and hydropathic modular averaging. Our most surprising result is the identification of large level sets, especially in hydrophobic extrema, with both thermodynamically first- and second-order scaled water waves. Our calculations include explicitly long-range water-protein interactions described by fractals. We also suggest a much-needed corrective for large protein drug development costs.

📄 PDF Abstract BibTeX arXiv:2008.08668

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

A geometric attractor mechanism for self-organization of entorhinal grid modules

2019-03-11

Grid cells in the medial entorhinal cortex (MEC) respond when an animal occupies a periodic lattice of "grid fields" in the environment. The grids are organized in modules with spatial periods, or scales, clustered aroun…

Self-organized criticality in neural networks

2021-07-07 · Mikhail I. Katsnelson, Vitaly Vanchurin, Tom Westerhout

We demonstrate, both analytically and numerically, that learning dynamics of neural networks is generically attracted towards a self-organized critical state. The effect can be modeled with quartic interactions between n…

Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks

2022-07-05 · Simon Ohler, Daniel Brady, Winfried Lötzsch, Michael Fleischhauer 외

Self-Organized Criticality (SOC) is a ubiquitous dynamical phenomenon believed to be responsible for the emergence of universal scale-invariant behavior in many, seemingly unrelated systems, such as forest fires, virus s…

Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns

2023-11-13 · Wensi Hu, Quan-Xing Liu, Bo wang, Nuo Xu 외

In harsh environments, organisms may self-organize into spatially patterned systems in various ways. So far, studies of ecosystem spatial self-organization have primarily focused on apparent orders reflected by regular p…

Learning locally dominant force balances in active particle systems

2023-07-27 · Dominik Sturm, Suryanarayana Maddu, Ivo F. Sbalzarini

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. T…