Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2109.01826

Natural selection from the perspective of mathematical and physical laws

Kai Xu
View original source ↗Read source document ↗Export metadata ↓

Abstract

The theory of evolution by natural selection cannot be used to evaluate the truth value of the following proposition: Through evolution, there exists at least one species that can adapt to any one given environment. To address this issue, this study attempted to define natural selection from the perspective of mathematical and physical laws. This study roughly classified biological activities into molecular, cellular, individual, ecological, and biogeochemical (atomic) levels according to scale and complexity, and selected typical phenomena from each level to analyze the relationship between adaptive evolution and several laws of mathematics and physics. Then, we proposed that natural selection favors heritable variations that allows organisms to better use and/or hide the laws in a certain environment. Reproductive advantage is by far the most obvious consequence of natural selection. Moreover, adaptive evolution can lead to the emergence of laws that ensures that each law only controls some properties of organisms. This study found that organisms significantly influence themselves in all five levels of biological activities, and the whole biosphere can be considered as a huge and evolution-driven feedback loop. Organisms can carry more laws than non-living matter, but the carrying capacity is limited. Therefore, this study's findings suggest that adaptive evolution makes organisms subject to more laws of mathematics and physics until the highest carrying capacity is reached.

Explore related subjects

physics.bio-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kai Xu. 2022-08-16. Natural selection from the perspective of mathematical and physical laws. https://arxiv.org/abs/2109.01826

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models

We present a machine learning approach for predicting the organisation of corneal, glial and fibroblast cells in 3D cultures used for tissue engineering. Our machine-learning-based method uses a generative adversarial network architecture called pix2pix, which we train using results from biophysical contractile network dipole orientation (CONDOR) simulations. In the following, we refer to the machine learning method as the RAPTOR (RApid Prediction of Tissue ORganisation) approach. A training data set containing a range of CONDOR simulations is created, covering a range of underlying model parameters. Predictions of the trained neural network are compared with cultured glial, corneal, and fibroblast tissues, with good agreements for both CONDOR and RAPTOR approaches. An approach is developed to determine CONDOR model parameters for specific tissues using both RAPTOR and CONDOR fits to tissue properties. RAPTOR outputs a variety of tissue properties, including cell densities, cell alignments and tension. RAPTOR yields predictions of tissue properties within fractions of a second. This speed makes it valuable for the design of tethered moulds for tissue growth.

physics.bio-ph↗
arXiv2026

How do incorrect ligands help detect a correct ligand?

Intrigued by the response of T cell receptors to the presence of a few agonist ligands, we propose a minimal model that can achieve similar performance. The model consists of a small cluster of immobile receptors that bind reversibly to two types (correct/incorrect) of ligands in the environment, with slightly weaker binding strength for the incorrect one. It features binding-state coupling between nearest-neighbor receptors, and receptors in the bound/free states are activated/deactivated by specific enzymes, with rates that allow kinetic proofreading. It is found that, for a range of binding-state coupling strength, incorrect ligands alone cannot activate the receptors, but the binding of merely one correct ligand to a receptor is sufficient to promote the activation of other receptors via induced binding to incorrect ligands. Both response time and signal amplification increase as the receptor binding-state coupling strength increases until it reaches an optimal range to achieve the most rapid and sensitive response. These results suggest a possible mechanism for a speedy and specific response of receptors to very few correct ligands in biological and artificial systems at the subcellular scale.

physics.bio-ph↗
arXiv2026

Spatially Resolved Nucleated Polymerization: A Free-Boundary Model of Protein Aggregation in Concentrated Solutions

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

physics.bio-ph↗