Physical biology最新文献

{"title":"Cyclic-polymer grafted colloids in spherical confinement: insights for interphase chromosome organization.","authors":"Jarosław Paturej, Aykut Erbaş","doi":"10.1088/1478-3975/ace750","DOIUrl":"10.1088/1478-3975/ace750","url":null,"abstract":"<p><p>Interphase chromosomes are known to organize non-randomly in the micron-sized eukaryotic cell nucleus and occupy certain fraction of nuclear volume, often without mixing. Using extensive coarse-grained simulations, we model such chromosome structures as colloidal particles whose surfaces are grafted by cyclic polymers. This model system is known as Rosetta. The cyclic polymers, with varying polymerization degrees, mimic chromatin loops present in interphase chromosomes, while the rigid core models the chromocenter section of the chromosome. Our simulations show that the colloidal chromosome model provides a well-separated particle distribution without specific attraction between the chain monomers. As the polymerization degree of the grafted cyclic chains decreases while maintaining the total chromosomal length (e.g. the more potent activity of condensin-family proteins), the average chromosomal volume becomes smaller, inter-chromosomal contacts decrease, and chromocenters organize in a quasi-crystalline order reminiscent of a glassy state. This order weakens for polymer chains with a characteristic size on the order of the confinement radius. Notably, linear-polymer grafted particles also provide the same chromocenter organization scheme. However, unlike linear chains, cyclic chains result in less contact between the polymer layers of neighboring chromosome particles, demonstrating the effect of DNA breaks in altering genome-wide contacts. Our simulations show that polymer-grafted colloidal systems could help decipher 3D genome architecture along with the fractal globular and loop-extrusion models.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9876494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven discovery of stochastic dynamical equations of collective motion.","authors":"Arshed Nabeel, Vivek Jadhav, Danny Raj M, Clément Sire, Guy Theraulaz, Ramón Escobedo, Srikanth K Iyer, Vishwesha Guttal","doi":"10.1088/1478-3975/ace22d","DOIUrl":"10.1088/1478-3975/ace22d","url":null,"abstract":"<p><p>Coarse-grained descriptions of collective motion of flocking systems are often derived for the macroscopic or the thermodynamic limit. However, the size of many real flocks falls within 'mesoscopic' scales (10 to 100 individuals), where stochasticity arising from the finite flock sizes is important. Previous studies on mesoscopic models have typically focused on non-spatial models. Developing mesoscopic scale equations, typically in the form of stochastic differential equations, can be challenging even for the simplest of the collective motion models that explicitly account for space. To address this gap, here, we take a novel data-driven equation learning approach to construct the stochastic mesoscopic descriptions of a simple, spatial, self-propelled particle (SPP) model of collective motion. In the spatial model, a focal individual can interact with<i>k</i>randomly chosen neighbours within an interaction radius. We consider<i>k</i> = 1 (called stochastic pairwise interactions),<i>k</i> = 2 (stochastic ternary interactions), and<i>k</i>equalling all available neighbours within the interaction radius (equivalent to Vicsek-like local averaging). For the stochastic pairwise interaction model, the data-driven mesoscopic equations reveal that the collective order is driven by a multiplicative noise term (hence termed, noise-induced flocking). In contrast, for higher order interactions (<i>k</i> > 1), including Vicsek-like averaging interactions, models yield collective order driven by a combination of deterministic and stochastic forces. We find that the relation between the parameters of the mesoscopic equations describing the dynamics and the population size are sensitive to the density and to the interaction radius, exhibiting deviations from mean-field theoretical expectations. We provide semi-analytic arguments potentially explaining these observed deviations. In summary, our study emphasises the importance of mesoscopic descriptions of flocking systems and demonstrates the potential of the data-driven equation discovery methods for complex systems studies.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9835059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitochondrial networks through the lens of mathematics.","authors":"Greyson R Lewis, Wallace F Marshall","doi":"10.1088/1478-3975/acdcdb","DOIUrl":"10.1088/1478-3975/acdcdb","url":null,"abstract":"<p><p>Mitochondria serve a wide range of functions within cells, most notably via their production of ATP. Although their morphology is commonly described as bean-like, mitochondria often form interconnected networks within cells that exhibit dynamic restructuring through a variety of physical changes. Further, though relationships between form and function in biology are well established, the extant toolkit for understanding mitochondrial morphology is limited. Here, we emphasize new and established methods for quantitatively describing mitochondrial networks, ranging from unweighted graph-theoretic representations to multi-scale approaches from applied topology, in particular persistent homology. We also show fundamental relationships between mitochondrial networks, mathematics, and physics, using ideas of graph planarity and statistical mechanics to better understand the full possible morphological space of mitochondrial network structures. Lastly, we provide suggestions for how examination of mitochondrial network form through the language of mathematics can inform biological understanding, and vice versa.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9797316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian filtering for model predictive control of stochastic gene expression in single cells.","authors":"Zachary Fox,&nbsp;Gregory Batt,&nbsp;Jakob Ruess","doi":"10.1088/1478-3975/ace094","DOIUrl":"https://doi.org/10.1088/1478-3975/ace094","url":null,"abstract":"<p><p>This study describes a method for controlling the production of protein in individual cells using stochastic models of gene expression. By combining modern microscopy platforms with optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which can induce protein production. Here we use a finite state projection based stochastic model of gene expression, along with Bayesian state estimation to control protein copy numbers within individual cells. We compare this method to previous methods that use population based approaches. We also demonstrate the ability of this control strategy to ameliorate discrepancies between the predictions of a deterministic model and stochastic switching system.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10156988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation, response and entropy approaches to allosteric behaviors: a critical comparison on the ubiquitin case.","authors":"Fabio Cecconi,&nbsp;Giulio Costantini,&nbsp;Carlo Guardiani,&nbsp;Marco Baldovin,&nbsp;Angelo Vulpiani","doi":"10.1088/1478-3975/ace1c5","DOIUrl":"https://doi.org/10.1088/1478-3975/ace1c5","url":null,"abstract":"<p><p>Correlation analysis and its close variant principal component analysis are tools widely applied to predict the biological functions of macromolecules in terms of the relationship between fluctuation dynamics and structural properties. However, since this kind of analysis does not necessarily imply causation links among the elements of the system, its results run the risk of being biologically misinterpreted. By using as a benchmark the structure of ubiquitin, we report a critical comparison of correlation-based analysis with the analysis performed using two other indicators, response function and transfer entropy, that quantify the causal dependence. The use of ubiquitin stems from its simple structure and from recent experimental evidence of an allosteric control of its binding to target substrates. We discuss the ability of correlation, response and transfer-entropy analysis in detecting the role of the residues involved in the allosteric mechanism of ubiquitin as deduced by experiments. To maintain the comparison as much as free from the complexity of the modeling approach and the quality of time series, we describe the fluctuations of ubiquitin native state by the Gaussian network model which, being fully solvable, allows one to derive analytical expressions of the observables of interest. Our comparison suggests that a good strategy consists in combining correlation, response and transfer entropy, such that the preliminary information extracted from correlation analysis is validated by the two other indicators in order to discard those spurious correlations not associated with true causal dependencies.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9878066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approximate simulation of cortical microtubule models using dynamical graph grammars.","authors":"Eric Medwedeff, Eric Mjolsness","doi":"10.1088/1478-3975/acdbfb","DOIUrl":"10.1088/1478-3975/acdbfb","url":null,"abstract":"<p><p>Dynamical graph grammars (DGGs) are capable of modeling and simulating the dynamics of the cortical microtubule array (CMA) in plant cells by using an exact simulation algorithm derived from a master equation; however, the exact method is slow for large systems. We present preliminary work on an approximate simulation algorithm that is compatible with the DGG formalism. The approximate simulation algorithm uses a spatial decomposition of the domain at the level of the system's time-evolution operator, to gain efficiency at the cost of some reactions firing out of order, which may introduce errors. The decomposition is more coarsely partitioned by effective dimension (<i>d</i>= 0 to 2 or 0 to 3), to promote exact parallelism between different subdomains within a dimension, where most computing will happen, and to confine errors to the interactions between adjacent subdomains of different effective dimensions. To demonstrate these principles we implement a prototype simulator, and run three simple experiments using a DGG for testing the viability of simulating the CMA. We find evidence indicating the initial formulation of the approximate algorithm is substantially faster than the exact algorithm, and one experiment leads to network formation in the long-time behavior, whereas another leads to a long-time behavior of local alignment.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11216692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9908222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infiltration of tumor spheroids by activated immune cells.","authors":"Mrinmoy Mukherjee,&nbsp;Oleksandr Chepizhko,&nbsp;Maria Chiara Lionetti,&nbsp;Stefano Zapperi,&nbsp;Caterina A M La Porta,&nbsp;Herbert Levine","doi":"10.1088/1478-3975/ace0ee","DOIUrl":"https://doi.org/10.1088/1478-3975/ace0ee","url":null,"abstract":"<p><p>Recent years have seen a tremendous growth of interest in understanding the role that the adaptive immune system could play in interdicting tumor progression. In this context, it has been shown that the density of adaptive immune cells inside a solid tumor serves as a favorable prognostic marker across different types of cancer. The exact mechanisms underlying the degree of immune cell infiltration is largely unknown. Here, we quantify the temporal dynamics of the density profile of activated immune cells around a solid tumor spheroid. We propose a computational model incorporating immune cells with active, persistent movement and a proliferation rate that depends on the presence of cancer cells, and show that the model able to reproduce semi-quantitatively the experimentally measured infiltration profile. Studying the density distribution of immune cells inside a solid tumor can help us better understand immune trafficking in the tumor micro-environment, hopefully leading towards novel immunotherapeutic strategies.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9753121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The emergence of lines of hierarchy in collective motion of biological systems.","authors":"James M Greene,&nbsp;Eitan Tadmor,&nbsp;Ming Zhong","doi":"10.1088/1478-3975/acdc79","DOIUrl":"https://doi.org/10.1088/1478-3975/acdc79","url":null,"abstract":"<p><p>The emergence of large-scale structures in biological systems, and in particular the formation of lines of hierarchy, is observed at many scales, from collections of cells to groups of insects to herds of animals. Motivated by phenomena in chemotaxis and phototaxis, we present a new class of alignment models that exhibit alignment into lines. The spontaneous formation of such 'fingers' can be interpreted as the emergence of leaders and followers in a system of identically interacting agents. Various numerical examples are provided, which demonstrate emergent behaviors similar to the 'fingering' phenomenon observed in some phototaxis and chemotaxis experiments; this phenomenon is generally known to be a challenging pattern for existing models to capture. A novel protocol for pairwise interactions provides a fundamental alignment mechanism by which agents may form lines of hierarchy across a wide range of biological systems.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10094918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Allometry of<i>Escherichia coli</i>surface area with volume: effect of size variability, filamentation and division dynamics.","authors":"Tanvi Kale,&nbsp;Dhruv Khatri,&nbsp;Chaitanya A Athale","doi":"10.1088/1478-3975/acdcda","DOIUrl":"https://doi.org/10.1088/1478-3975/acdcda","url":null,"abstract":"<p><p>The cell surface area (SA) increase with volume (V) is determined by growth and regulation of size and shape. Most studies of the rod-shaped model bacterium<i>Escherichia coli</i>have focussed on the phenomenology or molecular mechanisms governing such scaling. Here, we proceed to examine the role of population statistics and cell division dynamics in such scaling by a combination of microscopy, image analysis and statistical simulations. We find that while the SA of cells sampled from mid-log cultures scales with V by a scaling exponent 2/3, i.e. the geometric law SA ∼V2/3, filamentous cells have higher exponent values. We modulate the growth rate to change the proportion of filamentous cells, and find SA-V scales with an exponent>2/3, exceeding that predicted by the geometric scaling law. However, since increasing growth rates alter the mean and spread of population cell size distributions, we use statistical modeling to disambiguate between the effect of the mean size and variability. Simulating (i) increasing mean cell length with a constant standard deviation (s.d.), (ii) a constant mean length with increasing s.d. and (iii) varying both simultaneously, results in scaling exponents that exceed the 2/3 geometric law, when population variability is included, with the s.d. having a stronger effect. In order to overcome possible effects of statistical sampling of unsynchronized cell populations, we 'virtually synchronized' time-series of cells by using the frames between birth and division identified by the image-analysis pipeline and divided them into four equally spaced phases-B, C1, C2 and D. Phase-specific scaling exponents estimated from these time series and the cell length variability were both found to decrease with the successive stages of birth (B), C1, C2 and division (D). These results point to a need to consider population statistics and a role for cell growth and division when estimating SA-V scaling of bacterial cells.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9688316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians.","authors":"Petro Sarkanych,&nbsp;Mariana Krasnytska,&nbsp;Luis Gómez-Nava,&nbsp;Pawel Romanczuk,&nbsp;Yurij Holovatch","doi":"10.1088/1478-3975/acd6ce","DOIUrl":"https://doi.org/10.1088/1478-3975/acd6ce","url":null,"abstract":"<p><p>In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms (Hartnett<i>et al</i>2016<i>Phys. Rev. Lett.</i><b>116</b>038701). Within the model, the state of an agent<i>i</i>is described by the pair of variables corresponding to its opinionSi=±1and a bias<i>ω</i>_<i>i</i>toward any of the opposing values of<i>S</i>_<i>i</i>. Collective decision making is interpreted as an approach to the equilibrium state within the nonlinear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such a physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that, depending on the assumptions about the nature of social interactions, two different Hamiltonians can be formulated, which can be solved using different methods. In such an interpretation the temperature serves as a measure of fluctuations, not considered before in the original model. We find exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are confirmed using individual-based simulations. The simulations also allow us to study the impact of system size and initial conditions on the collective decision making in finite-sized systems, in particular, with respect to convergence to metastable states.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":"20 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9993454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}