1.
Mamontov, Eugen, 1955, et al.
(författare)
The minimal, phase-transition model for the cell-number maintenance by the hyperplasia-extended homeorhesis
2006
Ingår i: Acta Biotheoretica. - : Springer Science and Business Media LLC. - 0001-5342 .- 1572-8358. ; 54:2, s. 61-101
Tidskriftsartikel (refereegranskat) abstract
Oncogenic hyperplasia is the first and inevitable stage of formation of a (solid) tumor. This stage is also the core of many other proliferative diseases. The present work proposes the first minimal model that combines homeorhesis with oncogenic hyperplasia where the latter is regarded as a genotoxically activated homeorhetic dysfunction. This dysfunction is specified as the transitions of the fluid of cells from a fluid, homeorhetic state to a solid, hyperplastic-tumor state, and back. The key part of the model is a nonlinear reaction-diffusion equation (RDE) where the biochemical-reaction rate is generalized to the one in the well-known Schlögl physical theory of the non-equilibrium phase transitions. A rigorous analysis of the stability and qualitative aspects of the model, where possible, are presented in detail. This is related to the spatially homogeneous case, i.e. when the above RDE is reduced to a nonlinear ordinary differential equation. The mentioned genotoxic activation is treated as a prevention of the quiescent G0-stage of the cell cycle implemented with the threshold mechanism that employs the critical concentration of the cellular fluid and the nonquiescent-cell-duplication time. The continuous tumor morphogeny is described by a time-space-dependent cellular-fluid concentration. There are no sharp boundaries (i.e. no concentration jumps exist) between the domains of the homeorhesis- and tumor-cell populations. No presumption on the shape of a tumor is used. To estimate a tumor in specific quantities, the model provides the time-dependent tumor locus, volume, and boundary that also points out the tumor shape and size. The above features are indispensable in the quantitative development of antiproliferative drugs or therapies and strategies to prevent oncogenic hyperplasia in cancer and other proliferative diseases. The work proposes an analytical-numerical method for solving the aforementioned RDE. A few topics for future research are suggested.
2.
3.
Sukhovey, Yury, et al.
(författare)
Psycho-immune Partnership in the Dynamic Responses of Living Systems
2014
Ingår i: International Journal of Life Science and Medical Research. - 2226-4566. ; 4:5, s. 57-50
Tidskriftsartikel (refereegranskat) abstract
Present paper outlines certain possibilities for a new approach to the generalized modeling of the dynamics of living organism reaction to the changes in its environment. It is shown that there exists common functional “alignment” of the immune and psychic responses in the living organism reaction to the changes in the environment. This is manifested in the experimentally recorded matching of the response scenarios and activities in the immune and psychic domains. Thus, one may conclude that there exist two functionally matched systems (psychic and immune) tracking the external influences and responding to them. On the other hand it is feasible that one is actually dealing with a complex inseparable psycho-immune system and experimentally monitors different aspects of its activity describing these from two different points of view. Given analysis of available experimental data seems to support both of the above alternatives without giving undoubted preferences to any of them. However treating the situation form the formal logic point of view certain preferences can be given to the concept of an inseparable psycho-immune system modeled in two different ways. This approach can also resolve certain collisions happening with the attempts to describe direct interrelations between psychic and immune domains, and can lead the way to higher-level functionality models of the dynamic responses of the living organisms.
4.
Mamontov, Eugen, 1955, et al.
(författare)
What stochastic mechanics are relevant to the study of living systems?
2005
Ingår i: Proceedings of the Latvian Academy of Sciences. Section B: Natural, Exact and Applied Sciences. - Riga, Latvia : Latvian Academy of Sciences. - 1407-009X. ; 59:6, s. 255-262
Tidskriftsartikel (refereegranskat) abstract
Biologists have identified many features of living systems which cannot be studied by application of fundamental statistical mechanics (FSM). The present work focuses on some of these features. By discussing all the basic approaches of FSM, the work formulates the extension of the kinetic-theory paradigm (based on the reduced one-particle distribution function) that possesses all the considered properties of the living systems. This extension appears to be a model within the generalized-kinetic theory developed by N. Bellomo and his co-authors. In connection with this model, the work also stresses some other features necessary for making the model relevant to living systems. An example is discussed, which is a generalized kinetic equation coupled with the probability-density equation which represents the varying component content of a living system. The work also suggests directions for future research.
5.
Oskolkov, Nikolay, et al.
(författare)
High-throughput muscle fiber typing from RNA sequencing data
2022
Ingår i: Skeletal Muscle. - : Springer Science and Business Media LLC. - 2044-5040. ; 12, s. 1-9
Tidskriftsartikel (refereegranskat) abstract
Background: Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. Methods: By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). Results: The correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13–0.67], [95% CI], and rmyosin = 0.83 [0.61–0.93], with p = 5.70 × 10–3 and 2.00 × 10–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads. Conclusions: This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.
