Physical biology最新文献

Microbial communities are composed of functionally integrated taxa, and identifying which taxa contribute to a given ecosystem function is essential for predicting community behaviors. This study compares the effectiveness of a previously proposed method for identifying 'functional taxa,' ensemble quotient optimization (EQO), to a potentially simpler approach based on the least absolute shrinkage and selection operator (LASSO). In contrast to LASSO, EQO uses a binary prior on coefficients, assuming uniform contribution strength across taxa. Using synthetic datasets with increasingly realistic structure, we demonstrate that EQO's strong prior enables it to perform better in low-data regime. However, LASSO's flexibility and efficiency can make it preferable as data complexity increases. Our results detail the favorable conditions for EQO and emphasize LASSO as a viable alternative.

Membrane tension plays an important role in various aspects of the dynamics and functions of cells. Here, we review recent studies of the effect of membrane tension on pore formation in lipid bilayers and pore formation induced by membrane-active peptides (MAPs) including antimicrobial peptides (AMPs). For this purpose, the micropipette aspiration method using a patch of cell membrane/lipid bilayers and a giant unilamellar vesicle (GUV)/a total cell, and the application of osmotic pressure (Π) to suspensions of large unilamellar vesicles (LUVs) have been used. However, these conventional methods have some drawbacks for the investigation of the effect of membrane tension on the actions of MAPs such as AMPs. Recently, to overcome these drawbacks, a new Π method using GUVs has been developed. Here, we focus on this Π method as a new technique for revealing the effect of membrane tension on the MAPs-induced pore formation. Firstly, we review studies of the effect of membrane tension on pore formation in lipid bilayers as determined by conventional methods. Secondly, after a brief review of studies of the effect of Π on LUVs, we describe the estimation of membrane tension in GUVs induced by Π and the Π-induced pore formation. Thirdly, after a review of the effect of membrane tension on the MAPs-induced pore formation as obtained by the conventional methods, we describe an application of the Π method to studies of the effect of membrane tension on AMP-induced pore formation. Finally, we discuss the advantages of the Π method over conventional methods and consider future perspectives.

The morphology and morphodynamics of cells as important biomarkers of the cellular state are widely appreciated in both fundamental research and clinical applications. Quantification of cell morphology often requires a large number of geometric measures that form a high-dimensional feature vector. This mathematical representation creates barriers to communicating, interpreting, and visualizing data. Here, we develop a deep learning-based algorithm to project 13-dimensional (13D) morphological feature vectors into 2-dimensional (2D) morphological latent space (MLS). We show that the projection has less than 5% information loss and separates the different migration phenotypes of metastatic breast cancer cells. Using the projection, we demonstrate the phenotype-dependent motility of breast cancer cells in the 3D extracellular matrix, and the continuous cell state change upon drug treatment. We also find that dynamics in the 2D MLS quantitatively agrees with the morphodynamics of cells in the 13D feature space, preserving the diffusive power and the Lyapunov exponent of cell shape fluctuations even though the dimensional reduction projection is highly nonlinear. Our results suggest that MLS is a powerful tool to represent and understand the cell morphology and morphodynamics.

This study explores the relationship between residue fluctuations and molecular communication in proteins, emphasizing the role of these dynamics in allosteric regulation. We employ computational tools including the Gaussian network model, mutual information, and interaction information, to analyze how stochastic interactions among residues contribute to functional interactions while also introducing noise. Our approach is based on the postulate that residues experience continuous stochastic bombardment from impulses generated by their neighbors, forming a complex network characterized by small-world scaling topology. By mapping these interactions through the Kirchhoff matrix framework, we demonstrate how conserved correlations enhance signaling pathways and provide stability against noise-like fluctuations. Notably, we highlight the importance of selecting relevant eigenvalues to optimize the signal-to-noise ratio in our analyses, a topic that has yet to be thoroughly investigated in the context of residue fluctuations. This work underscores the significance of viewing proteins as adaptive information processing systems, and emphasizes the fundamental mechanisms of biological information processing. The basic idea of this paper is the following: given two interacting residues on an allosteric path, what are the contributions of the remaining residues on this interaction. This naturally leads to the concept of synergy, redundancy and noise in proteins, which we analyze in detail for three proteins CheY, tyrosine phosphatase andβ-lactoglobulin.

Tracking and motion analyses of semi-flexible biopolymer networks from time-lapse microscopy images are important tools that enable quantitative measurements to unravel the dynamic and mechanical properties of biopolymers in living tissues, crucial for understanding their organization and function. Biopolymer networks are challenging to track due to continuous stochastic transitions, such as merges and splits, which cause local neighborhood rearrangements over short time and length scales. To address this, we propose the Spatio Temporal Information on Pixel Subsets algorithm to track these events by creating pixel subsets that link trajectories across frames. Using this method, we analyzed actin-enriched protrusions, or 'microridges,' which form dynamic labyrinthine patterns on squamous cell epithelial surfaces, mimicking 'active Turing-patterns.' Our results reveal two distinct actomyosin-based rhythmic dynamics in neighboring cells: a common pulsatile mechanism between 2 and 6.25 min period governing both fusion and fission events contributing to pattern maintenance, and cell area pulses predominantly exhibiting 10 min period.

The electric potential across the inner mitochondrial membrane must be maintained within certain bounds for the proper functioning of the cell. A feedback control mechanism for the homeostasis of this membrane potential is proposed whereby an increase in the electric field decreases the rate-limiting steps of the electron transport chain (ETC). An increase in trans-membrane electric field limits the rate of proton pumping to the inter-membrane gap by slowing the ETC reactions and by intrinsically induced electroporation that depolarizes the inner membrane. The proposed feedback mechanism is akin to a Le Chatelier's-type principle of trans-membrane potential feedback control.

Ion channels are protein structures that facilitate the selective passage of ions across the membrane cells of living organisms. They are known for their high conductance and high selectivity. The precise mechanism between these two seemingly contradicting features is not yet firmly established. One possible candidate is the quantum coherence. In this work we study the quantum model of the soft knock-on conduction using the Lindblad equation taking into account the non-hermiticity of the model. We show that the model exhibits a regime in which high conductance coexists with high coherence. Our findings second the role of quantum effects in the transport properties of the ion channels.

Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors (TFs). Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression (symmetric and asymmetric) patterns of the two genes, and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the TFs influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of TF binding affinities.

How much information does a cell inherit from its ancestors beyond its genetic sequence? What are the epigenetic mechanisms that allow this? Despite the rise in available epigenetic data, how such information is inherited through the cell cycle is still not fully understood. Often, epigenetic marks can display bistable behaviour and their bistable state is transmitted to daughter cells through the cell cycle, providing the cell with a form of memory. However, loss-of-memory events also take place, where a daughter cell switches epigenetic state (with respect to the mother cell). Here, we develop a framework to compute these epigenetic switching rates, for the case when they are driven by DNA replication, i.e. the frequency of loss-of-memory events due to replication. We consider the dynamics of histone modifications during the cell cycle deterministically, except at DNA replication, where nucleosomes are randomly distributed between the two daughter DNA strands, which is therefore implemented stochastically. This hybrid stochastic-deterministic approach enables an analytic derivation of the replication-driven switching rate. While retaining great simplicity, this framework can explain experimental switching rate data, establishing its biological importance as a framework to quantitatively study epigenetic inheritance.

In this paper, we analyze the role of fear in a three-species non-delayed ecological model that examines the interactions among susceptible prey, infectious (diseased) prey, and predators within a food web. The prey population grows in a logistic manner until it achieves a carrying capacity, reflecting common population dynamics in the absence of predators. Diseased prey is assumed to transmit infection to healthful prey by the use of a Holling type II reaction. Predators, alternatively, are modeled to consume their prey using Beddington-DeAngelis and Crowley-Martin response features. This evaluation specializes in ensuring the non-negativity of solutions, practical constraints on population dynamics, and long-term stability of the system. Each biologically possible equilibrium point is tested to understand the environmental stable states. Local stability is assessed through eigenvalue analysis, while global stability of positive equilibria is evaluated by the use of Lyapunov features to determine the overall stability of the model. Furthermore, Hopf bifurcation is explored primarily based on infection rateɛ. Numerical simulations are carried out to validate the theoretical effects and offer practical insights into the model behaviour under specific conditions.