Bacteria utilize cell-cell signaling to coordinate gene expression in populations of cells. Bacterial signal exchange was originally interpreted as a mechanism bacteria use to regulate gene expression in response to changes in cell density, denoted as quorum sensing. Bacterial communication is now known to encompass the exchange of multiple chemical signals between different species of bacteria. Such signal crosstalk within communities of bacteria can have unexpected consequences. Some bacterial species even utilize more than one orthogonal signaling molecule, enabling such species to simultaneously communicate within distinct subsets of species. Such cells utilizing two sets of signals act as a bridge to link gene expression states within the community. Here, a mathematical model was implemented to investigate the consequences of multi-signal communication within heterogeneous bacterial communities. The model was inspired by simple neural networks, with nodes representing bacterial species and directed weights between nodes accounting for the impacts of inter-species signal exchange on gene expression. The activity state of such a network is defined as the gene expression state of each species within the community. Using the model, the stability of the activity states of such networks to changes in signal concentration and population size were quantified. Networks exchanging one set of signals were compared to network exchanging two orthogonal sets of signals. A multilayer neural network model was developed to analyze such networks exchanging orthogonal sets of signals. The model reveals that signal crosstalk increased the activity of the network. These networks were largely resilient to perturbation, however networks were more sensitive to perturbations of the largest population size. Bacterial species utilizing two orthogonal signals, within multilayer networks, had the potential to couple activity states of species that cannot directly communicate. These results give insight into strategies for manipulating signal exchange to predict and control gene expression within bacterial communities.

The Gaussian network model (GNM) has been successful in explaining protein dynamics by modeling proteins as elastic networks of alpha carbons connected by harmonic springs. However, its uniform interaction assumption and neglect of higher-order correlations limit its accuracy in predicting experimental B-factors and residue cross-correlations critical for understanding allostery and information transfer. This study introduces an information-theoretic enhancement to the GNM, incorporating mutual information-based corrections to the Kirchhoff matrix to account for multi-body interactions and contextual residue dynamics. By iteratively optimizing B-factor predictions and applying a Monte Carlo-driven maximum entropy approach to refine covariances, our method achieves significant improvements, reducing RMSDs between predicted and experimental B-factors by 26%-46% across nine representative proteins. The model contextualizes residue assignments based on local density, solvent exposure, and allosteric roles, showing complex dynamic patterns beyond simple neighbor counts. Enhanced predictions of mutual information and entropy perturbations in proteins like KRAS improve the identification of spanning trees containing key residues, which may correspond to allosteric communication pathways. This evolvable framework, capable of incorporating additional effects and utilizing contextual residue assignments, enables precise studies of mutation effects on protein dynamics, with improved cross-correlation predictions potentially increasing accuracy in drug design and function prediction.

Weakly electric fish sense their environment in the dark using a self-generated electric field. Perturbations in the field caused by different objects are encoded by an array of sensors on their skin. The information content in these perturbations is not entirely clear. Previous work has focused on the so-called electric image (or field perturbation), which is the difference in the field at the skin surface, with and without the object present. Various features of the electric image have been shown to provide information about an object, including location. However, electric image based algorithms require information about the electric field under two qualitatively distinct conditions, and in many situations, prior information about the unperturbed field is not available. Here, we consider the more general problem of object localization with electric sensing when only instantaneous measures of the electric field are available. We show that this problem is solvable when field measurements for two slightly different object locations are considered (such as those occurring during relative motion). In doing so, we provide a direct link between sensory flow (i.e. the moment-to-moment fluctuations in raw sensory input) and electrosensory-based object localization.

Despite decades of research, cancer remains one of the biggest health challenges. Due to intricate interplay between multiple factors and different cancer types, it is still impossible to pinpoint a common cause to all forms of cancer. Computational modeling can be helpful in integrating discrete information to derive comprehensive information about malignancy. We describe a discrete dynamic network model of MAP Kinase pathway consisting of 66 nodes and 95 edges. The network consists of five input signals (Fas Ligand, DNA damage, Insulin, TNFa and TGFb) and three output nodes (Proliferation, Apoptosis and Growth Arrest). Using random asynchronous update method and in silico node perturbations, the accuracy of the model was ensured. The results of simulations and perturbations were in agreement with the gene knockout and constitutive expression studies reported in literature, underscoring the high precision of the deduced comprehensive network. The fidelity of our model makes it useful to understand the etiology of malignancy. Both anti-cancer and pro-cancer roles have been attributed to DUSP1 in different forms of cancers and in our model DUSP1 knockout under 'Insulin and DNA damage' signaling was found to universally enhance the proportion of cells undergoing apoptosis (i.e. a pro-cancerous role). Thus highlighting its potential in designing novel therapeutic interventions. Moreover, though MYC is a well-known oncogene we found that MYC's overexpression can activate p53, a prominent anti-growth agent, through p14 and MDM2 pathway. Implications: Our findings suggest a novel role of DUSP1 and MYC gene in regulating cell proliferation. .

Phenotypic plasticity-the reversible switching of cell-states-is a central tenet of development, regeneration, and cancer progression. These transitions are governed by gene regulatory networks (GRNs), whose topological features strongly influence their dynamics. While toggle switches (mutually inhibitory feedback loops between two transcription factors) are a common motif observed for binary cell-fate decisions, GRNs across diverse contexts often exhibit a more general structure: two mutually inhibiting teams of nodes. Here, we investigate the teams of nodes as a potential topological design principle of GRNs. We first analyze GRNs from the Cell Collective database and introduce a metric, impurity, which quantifies the fraction of edges inconsistent with an idealized two-team architecture. Impurity correlates strongly with statistical properties of GRN phenotypic landscapes, highlighting its predictive value. To further probe this relationship, we simulate artificial two-team networks (TTNs) using both continuous (RACIPE) and discrete (Boolean) formalisms across varying impurity, density, and network size values. TTNs exhibit toggle-switch-like robustness under perturbations and enable accurate prediction of dynamical features such as inter-team correlations and steady-state entropy. Together, our findings establish the teams paradigm as a unifying principle linking GRN topology to dynamics, with broad implications for inferring coarse-grained network properties from high-throughput sequencing data.

The engagement of protein and ribonucleic acid (RNA)/deoxyribonucleic acid (DNA) is examined in three varied conformations of protein molecules and two different configurations of RNA/DNA, namely finite and cyclic. This analysis emphasizes density of states (DOS) and band structures by making use of a tight-binding Hamiltonian in combination with Green's function techniques. At a steady temperature and a defined quantity of building blocks in the RNA and DNA strands, the spectral diagrams show flat energy curves for both RNA and DNA molecules, showcasing characteristics akin to those found in semiconductors. The key distinctions between the cyclic configuration and the finite case lie in the peak height and the arrangement of the peaks in the DOS, as well as the shifts in band positions. The coupling of protein molecules with the RNA and DNA models yields a reduction of the energy gap in the protein-RNA system and a progression from semiconductor properties to metallic ones in the protein-DNA structure. Furthermore, the role of temperature in determining the DOS leads to changes in the peak levels and their respective positions. It is expected that the coupling of protein and RNA/DNA will directly exert a straightforward influence on the electronic attributes of RNA/DNA, which differ among diverse protein structures, thus creating opportunities for newly conducted research with significant biological implications.

This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using standardized preprocessing techniques, and fractal measures were computed to capture complexity and connectivity properties within breast tissue structures. We introduced the succolarity reservoir as a novel parameter accounting for tissues' latent connectivity. Fractal dimension, multifractality strength, and succolarity reservoir were found to effectively characterize specific features of mammographic texture in contrast to lacunarity and Rényi dimensions; however, their incorporation into machine learning models yielded moderate discriminatory performance between categories. In addition, while succolarity reservoir exhibits conceptual potential for differentiating Luminal B from other molecular subtypes, its overall discriminative power remains limited. This proof-of-concept study underscores the exploratory potential of fractal-based texture analysis as a non-invasive biomarker in breast carcinoma diagnosis.

Themultigene family, comprising approximately 60 members, encodes for variants oferythrocyte membrane protein or PfEMP1, a surface antigen which is crucial for parasite blood stage virulence.genes are expressed in a mutually exclusive fashion and to evade immune detection,transcriptionally switches from one variant to another. It has been proposed that a biased hierarchical switching pattern optimizes the growth and survival ofinside the human host. However, the need to establish a particular hierarchy is not well explored, since the growth advantage to the parasite remains the same even if gene identities are shuffled. Our theoretical analysis based on a Markov chain model, coupled with single cell RNA-seq data analysis, RT-qPCR and RNA-seq measurements, establishes a hierarchicalgene expression pattern underlying the biased switching pattern. Further, inclusion of host immune response in the model suggests that the observed switching hierarchy is beneficial when cells expressing different variants are cleared at variable rates by the immune response. For instance, PfEMP1 variants that are cleared more efficiently by the immune system are expressed stably and at a higher level in the population compared to variants that are cleared slowly by the immune system, with parasites quickly turning off the expression of the slowly cleared variant. Consistent with these findings, analysis of published experimental data showed that stable variants exhibit greater binding affinities to IgM. Taken together, our study provides a mechanistic basis for the hierarchical switching pattern ofgenes observed during infection.

The ryanodine receptor isoform-1 (RyR1) is a large intracellular calcium release channel essential for skeletal muscle contraction. While cryo-electron microscopy has revealed structural snapshots of RyR1 in closed and open states, the dynamic features associated with calcium-dependent gating remain incompletely understood. In this study, we integrated all-atom molecular dynamics (MD) simulations with domain-level bioinformatics analyses to characterize and compare the structural dynamics of RyR1 in its closed and open conformations. Our simulations revealed distinct structural differences, including domain flexibility patterns, solvent accessibility, and hydrogen bonding networks, between the closed and open states. The closed state exhibited more extensive inter-subunit contacts and stable hydrogen-bonding networks, supporting a compact architecture characterized by inter-subunit domain engagement and intra-subunit domain loosening. In contrast, the open state showed increased solvent exposure and reduced inter-subunit interactions, reflecting inter-subunit domain loosening coupled with intra-subunit domain engagement, particularly in regions connecting the cytoplasmic and pore-forming domains. The comparative approach provides structural perspectives on how calcium binding may contribute to RyR1's conformational organization relevant to gating function. Our findings highlight the utility of integrating MD simulations with domain-scale analyses to investigate large protein complexes and generate hypotheses for future experimental validation.

Growth in bacterial populations generally depends on the environment (availability and quality of nutrients, presence of a toxic inhibitor, product inhibition..). Here, we build a minimal model to describe the action of a bacteriostatic antibiotic, assuming that this drug inhibits an essential autocatalytic cycle of the cell metabolism. The model recovers known growth laws, can describe various types of antibiotics and confirms the existence of two distinct regimes of growth-dependent susceptibility, previously identified only for ribosome targeting antibiotics. We introduce a proxy for cell risk, which proves useful to compare the effects of various types of antibiotics. We also develop extensions of our model to describe the effect of combining two antibiotics targeting two different autocatalytic cycles or a regime where cell growth is inhibited by a waste product.

Gene regulatory networks with negative feedback play a crucial role in conferring robustness and evolutionary resilience to biological systems. However, the discrete nature of molecular components and probabilistic interactions in these networks are inherently subject to fluctuations, which pose challenges for stability analysis. Traditional analysis methods for stochastic systems, like the Langevin equation and the Fokker-Planck equation, are widely used. However, these methods primarily provide approximations of system behavior and may not be suitable for systems that exhibit non-mass-action kinetics, such as those described by Hill functions. In this study, we employed a second-moment approach to analyze the stability of a gene regulatory network with negative feedback under intrinsic fluctuations. By transforming the stochastic system into a set of ordinary differential equations for the mean concentration and second central moment, we performed a stability analysis similar to that used in deterministic models, where there are no fluctuations. Our results show that the incorporation of the second central moment introduces two additional negative eigenvalues, indicating that the system remains stable under intrinsic fluctuations. Furthermore, the stability of the second central moment suggests that the fluctuations do not induce instability in the system. The stationary values of the mean concentrations were found to be the same as those in the deterministic case, indicating that fluctuations did not influence stationary mean concentrations. This framework provides a practical and insightful method for analyzing the stability of stochastic systems and can be extended to other biochemical networks with regulatory feedback and intrinsic fluctuations through a framework of ordinary differential equations.

Metastatic glioblastoma multiforme (GBM) is known for its dismal prognosis due to the dissemination of single cells throughout the brain parenchyma and along white matter tracts, resulting in heightened resistance to therapies. Understanding the intricate relationship between cell migration, physical confinement, and chemotherapeutic resistance in GBM is imperative for advancing treatment strategies. In this study, we employed G55, a representative migratory GBM cell line, to investigate this phenomenon. We generated three distinct cell populations: (1) cells migrating without confinement, assessed via the Scratch assay; (2) cells migrating a short distance (10m) under confinement, examined through the Transwell assay; and (3) cells migrating long distances (>100m) under confinement, studied using the Microchannel assay. Comparative analyses of protein expression profiles and chemotherapy sensitivity among these groups revealed that migration combined with physical confinement plays a pivotal role in augmenting chemotherapeutic resistance in interstitial invasive cancer cells. Moreover, we demonstrate the utility of the microchannel device, which facilitates controlled cell migration under physical confinement, as an effectivetool for investigating metastatic cancer and associated treatment resistance. This study sheds light on the mechanisms underlying GBM progression and highlights potential avenues for therapeutic intervention.

Understanding the link between structure and function in proteins is fundamental in molecular biology and proteomics. A central question in this context is whether allostery-where the binding of a molecule at one site affects the activity of a distant site-emerges as a further manifestation of the intricate interplay between structure, function, and intrinsic dynamics. This study explores how allosteric regulation is modified when intrinsic protein dynamics operates under out-of-equilibrium conditions. To this purpose, we introduce a simple non-equilibrium model of protein dynamics, inspired by active matter systems, by generalizing the widely employed Gaussian network model to incorporate non-thermal effects. Our approach underscores the advantage of framing allostery as a causal process by using, as a benchmark system, the second PDZ domain of the human phosphatase human Protein Tyrosine Phosphatase 1E that mediates protein-protein interactions. We employ causal indicators, such as response functions and transfer entropy, to identify the network of PDZ2 residues through which the allosteric signal propagates across the protein structure. These indicators reveal specific regions that align well with experimental observations. Furthermore, our results suggest that deviations from purely thermal fluctuations can significantly influence allosteric communication by introducing distinct timescales and memory effects. This influence is particularly relevant when the allosteric response unfolds on timescales incompatible with relaxation to equilibrium. Accordingly, non-thermal fluctuations may become essential for accurately describing protein responses to ligand binding and developing a comprehensive understanding of allosteric regulation.

Prolyl oligopeptidase (PREP) has gained attention for its role in neurodegenerative diseases, particularly through protein-protein interactions with amyloid proteins such as alpha-synuclein and Tau. Although significant research has focused on PPIs, the substrate-binding dynamics within the catalytic pocket of PREP is less understood. This study combines molecular docking and molecular dynamics simulations to investigate the behavior of known PREP substrates, including thyrotropin-releasing hormone. Our simulations reveal that TRH transitions between three preferred regions within the binding pocket, one of which is favorable for catalytic activity. The absence of a single fixed binding site near the catalytic triad region may suggest a dynamic substrate-processing mechanism. Additionally, the potential of the TRH precursor as a substrate is evaluated. Our findings highlight the utility of computational methods in the analysis of protein dynamics and enzymatic mechanisms, offering insights into the functional versatility of PREP.

An enormous amount of gene expression data is currently available online in repositories for several organisms. Microarray data can be used to identify co-expressed genes that may be involved in the same biological process. Therefore, the analysis and interpretation of this information could help organize and understand the knowledge it contains, representing a major challenge in the post-genomic era. Here, we grouped genes ofK-12 using expression data to infer meaningful transcriptional regulatory information. Our method assumes that co-expressed genes reflect functional units, as evidenced by their genetic structure, including gene arrangement, regulation, and participation in defined biological processes. These functionally linked clusters were validated with curated transcriptional regulatory information from RegulonDB. From 907 growth conditions, 420 clusters were formed involving 1674 genes. Clusters contained from 2 to 64 genes. We found that co-expressed genes participate in related metabolic pathways and share similar types of regulation (through transcription factors,-factors, allosteric regulation, or micro-RNA regulation). This study is helpful for identifying novel transcriptional regulatory interactions.

Many biological systems regulate phenotypic heterogeneity as a fitness-maximising strategy in uncertain and dynamic environments. Analysis of such strategies is typically confined both to a discrete set of environmental conditions, and to a discrete (often binary) set of phenotypes specialised to each condition. In this work, we extend theory on both fronts to encapsulate a potentially continuous spectrum of phenotypes arising in response to environmental fluctuations that drive changes in the phenotype-dependent growth rate. We consider two broad classes of stochastic environment: those that are temporally uncorrelated (modelled by white-noise processes), and those that are correlated (modelled by Poisson and Ornstein-Uhlenbeck processes). For tractability, we restrict analysis to an exponential growth model, and consider biologically relevant simplifications that pertain to the timescale of phenotype switching relative to fluctuations in the environment. These assumptions yield a series of analytical and semi-analytical expressions that reveal environments in which phenotypic heterogeneity is evolutionarily advantageous.

Organelle abundance in cells is tightly regulated in response to external stimuli, but the underlying mechanisms remain poorly understood. Time-lapse imaging of fluorescently labelled organelles enables single-cell measurements of organelle copy numbers, revealing the time evolution of their distribution across a cell population. Building on a recently proposed kinetic model of organelle biogenesis, which incorporates de novo synthesis, fission, fusion, and degradation, we explore the time-dependent dynamics of organelle abundance. While previous studies focused on steady-state properties, here we calculate the first two moments of: 1) organelle copy numbers over time, and 2) first passage times to reach a specified organelle count. We show that these two moments provide a powerful means to discriminate between different mechanisms of organelle biogenesis. Notably, the time-dependent behaviour of organelle biogenesis reveals richer dynamics compared to the steady-state scenario. Our findings shed light on how cells attain steady-state organelle abundance after cell division and environmental perturbation.

Bacterial biofilms, surface-attached microbial communities, grow horizontally across surfaces and vertically above them. Although a simple heuristic model for vertical growth was experimentally shown to accurately describe the behavior of diverse microbial species, the biophysical implications and theoretical basis for this empirical model were unclear. Here, we demonstrate that this heuristic model emerges naturally from fundamental principles of active fluid dynamics. By analytically deriving solutions for an active fluid model of vertical biofilm growth, we show that the governing equations reduce to the same form as the empirical model in both early- and late-stage growth regimes. Our analysis reveals that cell death and decay rates likely play key roles in determining the characteristic parameters of vertical growth. The active fluid model produces a single, simple equation governing growth at all heights that is surprisingly simpler than the heuristic model. With this theoretical basis, we explain why the vertical growth rate reaches a maximum at a height greater than the previously identified characteristic length scale. These results provide a theoretical foundation for a simple mathematical model of vertical growth, enabling deeper understanding of how biological and biophysical factors interact during biofilm development.

Although various empirical studies have reported a positive correlation between the specific growth rate and cell size across bacteria, it is currently unclear what causes this relationship. We conjecture that such scaling occurs because smaller cells have a larger surface-to-volume ratio and thus have to allocate a greater fraction of the total resources to the production of the cell envelope, leaving fewer resources for other biosynthetic processes. To test this theory, we developed a coarse-grained model of bacterial physiology composed of the proteome that converts nutrients into biomass, with the cell envelope acting as a resource sink. Assuming resources are partitioned to maximize the growth rate, the model predicts that the growth rate and ribosomal mass fraction scale negatively, while the mass fraction of envelope-producing enzymes scales positively with surface-to-volume. These relationships are compatible with growth measurements and quantitative proteomics data reported in the literature.

Magnetotactic bacteria (MTB) are microorganisms that biomineralize intracellular magnetic nanoparticles inside a membrane vesicle/invagination. The set membrana + magnetic nanoparticle is known as magnetosome and generally magnetosomes are organized in linear chains in the cytoplasm, conferring a magnetic moment to the MTB. Due to their magnetic properties, MTB swim by following local magnetic field lines. This property makes MTB a suitable model to study bacterial movement. There are theoretical models to analyze the swimming of MTB, but the majority consider monotrichous bacteria. Only one model is related to the swimming of bilophotrichous bacteria, but they do not report the resultant trajectory parameters as a function of the magnetic field. Also, the literature lacks an experimental analysis of the trajectories of bilophotrichous MTB. The present study analyzes the movement of the bilphotrichous MTBstrain IT-1 exposed to different magnetic field intensities. The trajectories are composed of two oscillations, one of low frequency and large amplitude and another of high frequency and small amplitude. The amplitudes show a magnetic field dependency, and the frequencies show to be magnetic field independent. The analysis of the trajectory orientation relative to the magnetic field direction shows that magnetotaxis offor low magnetic fields is not as efficient as expected, perhaps due to living in a liquid culture medium rich in nutrients. As far as we know, this is the first time that these movement data have been obtained, and they will be important to validate future theoretical models of movement for bilophotrichous MTB.

Unlike flocks of birds and schools of fish that show net motion and synchronized motion, insect mating swarms are stationary and lack velocity ordering. Their collective nature when unperturbed is instead evident in their spatial statistics. In stark contrast with bird flocks, wherein the number density can fluctuate enormously from flock to flock, the number density of individuals in laboratory swarms of the midgeis approximately constant. Nonetheless, as swarms grow more populous, individuals cluster more and more. Here with the aid of stochastic trajectory models I show that these two seemingly contradictory behaviours can be attributed to the presence of multiplicative noise. The modelling also predicts that swarms are most stable when they are asymptotically large.