The emerging field of epigenetics has recently unveiled a dynamic landscape in which gene expression is not determined solely by genetic sequences but also by intricate regulatory mechanisms. This review examines the interactions between these regulatory mechanisms, including DNA methylation and non-coding RNAs (ncRNAs), that orchestrate gene expression fine-tuning for cellular homeostasis and the pathogenesis of a multitude of diseases. We explore long non-coding RNAs (lncRNAs) such as telomeric repeat-containing RNA (TERRA) and Fendrr, highlighting their role in protein regulation to ensure proper gene activation or silencing. Additionally, we explain the therapeutic potential of brain-derived neurotrophic factor (BDNF)-related microRNA 132, which has shown promise in treating chronic illnesses by restoring BDNF levels. Finally, this review covers the role of DNA methyltransferases and ncRNAs in cancer, focusing on how lncRNAs contribute to X chromosome inactivation and interact with chromatin-modifying complexes and DNA methyltransferase inhibitors to reduce cancer cell aggressiveness. By amalgamating the wide array of research in this field, we aim to provide glimpses into the complex entangling of genetics and environment as they control gene expressions.

Stochastic models of gene expression are typically formulated using the chemical master equation, which can be solved exactly or approximately using a repertoire of analytical methods. Here, we provide a tutorial review of an alternative approach based on queueing theory that has rarely been used in the literature of gene expression. We discuss the interpretation of six types of infinite server queues from the angle of stochastic single-cell biology and provide analytical expressions for the stationary and non-stationary distributions and/or moments of mRNA/protein numbers, and bounds on the Fano factor. This approach may enable the solution of complex models which have hitherto evaded analytical solution.

During photoheterotrophic growth on organic substrates, purple nonsulfur photosynthetic bacteria like Rhodospirillum rubrum can acquire electrons by multiple means, including oxidation of organic substrates, oxidation of inorganic electron donors (e.g. H$_2$), and by reverse electron flow from the photosynthetic electron transport chain. These electrons are stored in the form of reduced electron-carrying cofactors (e.g. NAD(P)H and ferredoxin). The ratio of oxidized to reduced redox cofactors (e.g. ratio of NAD(P)+:NAD(P)H), or 'redox poise` is difficult to understand or predict, as are the the cellular processes for dissipating these reducing equivalents. Using physics-based models that capture mass action kinetics consistent with the thermodynamics of reactions and pathways, a range of redox conditions for heterophototrophic growth are evaluated, from conditions in which the NADP+/NADPH levels approached thermodynamic equilibrium to conditions in which the NADP+/NADPH ratio is far above the typical physiological values. Modeling results together with experimental measurements of macro molecule levels (DNA, RNA, proteins and fatty acids) indicate that the redox poise of the cell results in large-scale changes in the activity of biosynthetic pathways. Phototrophic growth is less coupled than expected to producing reductant, NAD(P)H, by reverse electron flow from the quinone pool. Instead, it primarily functions for ATP production (photophosphorylation), which drives reduction even when NADPH levels are relatively low compared to NADP+. The model, in agreement with experimental measurements of macromolecule ratios of cells growing on different carbon substrates, indicate that the dynamics of nucleotide versus lipid and protein production is likely a significant mechanism of balancing oxidation and reduction in the cell.

In higher organisms, all cells share the same genome, but every cell expresses only a limited and specific set of genes that defines the cell type. During cell division, not only the genome, but also the cell type is inherited by the daughter cells. This intriguing phenomenon is achieved by a variety of processes that have been collectively termed epigenetics: the stable and inheritable changes in gene expression patterns. This article reviews the extremely rich and exquisitely multi-scale physical mechanisms that govern the biological processes behind the initiation, spreading and inheritance of epigenetic states. These include not only the changes in the molecular properties associated with the chemical modifications of DNA and histone proteins, such as methylation and acetylation, but also less conventional ones, such as the physics that governs the three-dimensional organization of the genome in cell nuclei. Strikingly, to achieve stability and heritability of epigenetic states, cells take advantage of many different physical principles, such as the universal behavior of polymers and copolymers, the general features of non-equilibrium dynamical systems, and the electrostatic and mechanical properties related to chemical modifications of DNA and histones. By putting the complex biological literature under this new light, the emerging picture is that a limited set of general physical rules play a key role in initiating, shaping and transmitting this crucial "epigenetic landscape". This new perspective not only allows to rationalize the normal cellular functions, but also helps to understand the emergence of pathological states, in which the epigenetic landscape becomes dysfunctional.

The immune response to an acute primary infection is a coupled process of antigen proliferation, molecular recognition by naive B cells, and their subsequent proliferation and antibody shedding. This process contains a fundamental problem: the recognition of an exponentially time-dependent antigen signal. Here we show that B cells can efficiently recognise new antigens by a tuned kinetic proofreading mechanism, where the molecular recognition machinery is adapted to the complexity of the immune repertoire. This process produces potent, specific and fast recognition of antigens, maintaining a spectrum of genetically distinct B cell lineages as input for affinity maturation. We show that the proliferation-recognition dynamics of a primary infection is a generalised Luria-Delbr\"uck process, akin to the dynamics of the classic fluctuation experiment. This map establishes a link between signal recognition dynamics and evolution. We derive the resulting statistics of the activated immune repertoire: antigen binding affinity, expected size, and frequency of active B cell clones are related by power laws, which define the class of generalised Luria-Delbr\"uck processes. Their exponents depend on the antigen and B cell proliferation rate, the number of proofreading steps, and the lineage density of the naive repertoire. We extend the model to include spatio-temporal processes, including the diffusion-recognition dynamics of a vaccination. Empirical data of activated mouse immune repertoires are found to be consistent with activation involving about three proofreading steps. The model predicts key clinical characteristics of acute infections and vaccinations, including the emergence of elite neutralisers and the effects of immune ageing. More broadly, our results establish infections and vaccinations as a new probe into the global architecture and functional principles of immune repertoires.

In this paper we present a biologically detailed mathematical model of tripartite synapses, where astrocytes modulate short-term synaptic plasticity. The model consists of a pre-synaptic bouton, a post-synaptic dendritic spine-head, a synaptic cleft and a peri-synaptic astrocyte controlling Ca2+ dynamics inside the synaptic bouton. This in turn controls glutamate release dynamics in the cleft. As a consequence of this, glutamate concentration in the cleft has been modeled, in which glutamate reuptake by astrocytes has also been incorporated. Finally, dendritic spine-head dynamics has been modeled. As an application, this model clearly shows synaptic potentiation in the hippocampal region, i.e., astrocyte Ca2+ mediates synaptic plasticity, which is in conformity with the majority of the recent findings (Perea & Araque, 2007; Henneberger et al., 2010; Navarrete et al., 2012).

A new general route for siRNA delivery is presented combining porous core-shell silica nanocarriers with a modularly designed multifunctional block copolymer. Specifically, the internal storage and release of siRNA from mesoporous silica nanoparticles (MSN) with orthogonal core-shell surface chemistry was investigated as a function of pore-size, pore morphology, surface properties and pH. Very high siRNA loading capacities of up to 380 microg/mg MSN were obtained with charge-matched amino-functionalized mesoporous cores, and release profiles show up to 80% siRNA elution after 24 h. We demonstrate that adsorption and desorption of siRNA is mainly driven by electrostatic interactions, which allow for high loading capacities even in medium-sized mesopores with pore diameters down to 4 nm in a stellate pore morphology. The negatively charged MSN shell enabled the association with a block copolymer containing positively charged artificial amino acids and oleic acid blocks, which acts simultaneously as capping function and endosomal release agent. The potential of this multifunctional delivery platform is demonstrated by highly effective cell transfection and siRNA delivery into KB-cells. A luciferase reporter gene knock-down of up to 90% was possible using extremely low cell exposures with only 2.5 microg MSN containing 32 pM siRNA per 100 microL well.

We report a first in modeling and simulation of the effects of the HIV proteins on the (caspase dependent) apoptotic pathway in infected cells. This work is novel and is an extension on the recent reports and clarifications on the FAS apoptotic pathway from the literature. We have gathered most of the reaction rates and initial conditions from the literature, the rest of the constants have been computed by fitting our model to the experimental results reported. Using the model obtained we have then run the simulations for the infected memory T cells, called also latent T cells, which, at the moment, represent the major obstacle to finding a cure for HIV. We can now report that the infected latent T cells have an estimated lifetime of about 42 hours from the moment they are re-activated. As far as we know this is the first result of this type obtained for the infected memory T cells.

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (A$\beta$) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of A$\beta$ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.

Recent advancements in immune sequencing and experimental techniques are generating extensive T cell receptor (TCR) repertoire data, enabling the development of models to predict TCR binding specificity. Despite the computational challenges due to the vast diversity of TCRs and epitopes, significant progress has been made. This paper discusses the evolution of the computational models developed for this task, with a focus on machine learning efforts, including the early unsupervised clustering approaches, supervised models, and the more recent applications of Protein Language Models (PLMs). We critically assess the most prominent models in each category, and discuss recurrent challenges, such as the lack of generalization to new epitopes, dataset biases, and biases in the validation design of the models. Furthermore, our paper discusses the transformative role of transformer-based protein models in bioinformatics. These models, pretrained on extensive collections of unlabeled protein sequences, can convert amino acid sequences into vectorized embeddings that capture important biological properties. We discuss recent attempts to leverage PLMs to deliver very competitive performances in TCR-related tasks. Finally, we address the pressing need for improved interpretability in these often opaque models, proposing strategies to amplify their impact in the field.

The emerging era of personalized medicine relies on medical decisions, practices, and products being tailored to the individual patient. Point-of-care systems, at the heart of this model, play two important roles. First, they are required for identifying subjects for optimal therapies based on their genetic make-up and epigenetic profile. Second, they will be used for assessing the progression of such therapies. Central to this vision is designing systems that, with minimal user-intervention, can transduce complex signals from biosystems in complement with clinical information to inform medical decision within point-of-care settings. To reach our ultimate goal of developing point-of-care systems and realizing personalized medicine, we are taking a multistep systems-level approach towards understanding cellular processes and biomolecular profiles, to quantify disease states and external interventions.

Infection by many viruses begins with fusion of viral and cellular lipid membranes, followed by entry of viral contents into the target cell and ultimately, after many biochemical steps, integration of viral DNA into that of the host cell. The early steps of membrane fusion and viral capsid entry are mediated by adsorption to the cell surface, and receptor and coreceptor binding. HIV-1 specifically targets CD4+ helper T-cells of the human immune system and binds to the receptor CD4 and coreceptor CCR5 before fusion is initiated. Previous experiments have been performed using a cell line (293-Affinofile) in which the expression of CD4 and CCR5 concentration were independently controlled. After exposure to HIV-1 of various strains, the resulting infectivity was measured through the fraction of infected cells. To design and evaluate the effectiveness of drug therapies that target the inhibition of the entry processes, an accurate functional relationship between the CD4/CCR5 concentrations and infectivity is desired in order to more quantitatively analyze experimental data. We propose three kinetic models describing the possible mechanistic processes involved in HIV entry and fit their predictions to infectivity measurements, contrasting and comparing different outcomes. Our approach allows interpretation of the clustering of infectivity of different strains of HIV-1 in the space of mechanistic kinetic parameters. Our model fitting also allows inference of nontrivial stoichiometries of receptor and coreceptor binding and provides a framework through which to quantitatively investigate the effectiveness of fusion inhibitors and neutralizing antibodies.

The migratory dynamics of cells can be influenced by the complex micro-environment through which they move. It remains unclear how the motility machinery of confined cells responds and adapts to their micro-environment. Here, we propose a biophysical mechanism for a geometry-dependent coupling between cellular protrusions and the nucleus that leads to directed migration. We apply our model to geometry-guided cell migration to obtain insights into the origin of directed migration on asymmetric adhesive micro-patterns and the polarization enhancement of cells observed under strong confinement. Remarkably, for cells that can choose between channels of different size, our model predicts an intricate dependence for cellular decision making as a function of the two channel widths, which we confirm experimentally.

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded the ability of Cell Painting to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Membraneless droplets formed through liquid-liquid phase separation (LLPS) play a crucial role in mRNA storage, enabling organisms to swiftly respond to environmental changes. However, the mechanisms underlying mRNA integration and protection within droplets remain unclear. Here, we unravel the role of bacterial aggresomes as stress granules (SGs) in safeguarding mRNA during stress. We discovered that upon stress onset, mobile mRNA molecules selectively incorporate into individual proteinaceous SGs based on length-dependent enthalpic gain over entropic loss. As stress prolongs, SGs undergo compaction facilitated by stronger non-specific RNA-protein interactions, thereby promoting recruitment of shorter RNA chains. Remarkably, mRNA ribonucleases are repelled from bacterial SGs, due to the influence of protein surface charge. This exclusion mechanism ensures the integrity and preservation of mRNA within SGs during stress conditions, explaining how mRNA can be stored and protected from degradation. Following stress removal, SGs facilitate mRNA translation, thereby enhancing cell fitness in changing environments. These droplets maintain mRNA physiological activity during storage, making them an intriguing new candidate for mRNA therapeutics manufacturing.

Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.

How cells regulate the number of organelles is a fundamental question in cell biology. While decades of experimental work have uncovered four fundamental processes that regulate organelle biogenesis, namely, de novo synthesis, fission, fusion and decay, a comprehensive understanding of how these processes together control organelle abundance remains elusive. Recent fluorescence microscopy experiments allow for the counting of organelles at the single-cell level. These measurements provide information about the cell-to-cell variability in organelle abundance in addition to the mean level. Motivated by such measurements, we build upon a recent study and analyze a general stochastic model of organelle biogenesis. We compute the exact analytical expressions for the probability distribution of organelle numbers, their mean, and variance across a population of single cells. It is shown that different mechanisms of organelle biogenesis lead to distinct signatures in the distribution of organelle numbers which allows us to discriminate between these various mechanisms. By comparing our theory against published data for peroxisome abundance measurements in yeast, we show that a widely believed model of peroxisome biogenesis that involves de novo synthesis, fission, and decay is inadequate in explaining the data. Also, our theory predicts bimodality in certain limits of the model. Overall, the framework developed here can be harnessed to gain mechanistic insights into the process of organelle biogenesis.

Cell growth and gene expression, essential elements of all living systems, have long been the focus of biophysical interrogation. Advances in single-cell methods have invigorated theoretical studies into these processes. However, until recently, there was little dialog between the two areas of study. Most theoretical models for gene regulation assumed gene activity to be oblivious to the progression of the cell cycle between birth and division. But there are numerous ways in which the periodic character of all cellular observables can modulate gene expression. The molecular factors required for transcription and translation increase in number during the cell cycle, but are also diluted due to the continuous increase in cell volume. The replication of the genome changes the dosage of those same cellular players but also provides competing targets for regulatory binding. Finally, cell division reduces their number again, and so forth. Stochasticity is inherent to all these biological processes, manifested in fluctuations in the synthesis and degradation of new cellular components as well as the random partitioning of molecules at each cell division. The notion of gene expression as stationary is thus hard to justify. In this review, we survey the emerging paradigm of cell-cycle regulated gene expression, with an emphasis on the global expression patterns rather than gene-specific regulation. We discuss recent experimental reports where cell growth and gene expression were simultaneously measured in individual cells, providing first glimpses into the coupling between the two. While the experimental findings, not surprisingly, differ among genes and organisms, several theoretical models have emerged that attempt to reconcile these differences and form a unifying framework for understanding gene expression in growing cells.

Biological activity gives rise to non-equilibrium fluctuations in the cytoplasm of cells; however, there are few methods to directly measure these fluctuations. Using a reconstituted actin cytoskeleton, we show that the bending dynamics of embedded microtubules can be used to probe local stress fluctuations. We add myosin motors that drive the network out of equilibrium, resulting in an increased amplitude and modified time-dependence of microtubule bending fluctuations. We show that this behavior results from step-like forces on the order of 10 pN driven by collective motor dynamics.

We analyse mobile-immobile transport of particles that switch between the mobile and immobile phases with finite rates. Despite this seemingly simple assumption of Poissonian switching we unveil a rich transport dynamics including significant transient anomalous diffusion and non-Gaussian displacement distributions. Our discussion is based on experimental parameters for tau proteins in neuronal cells, but the results obtained here are expected to be of relevance for a broad class of processes in complex systems. Concretely, we obtain that when the mean binding time is significantly longer than the mean mobile time, transient anomalous diffusion is observed at short and intermediate time scales, with a strong dependence on the fraction of initially mobile and immobile particles. We unveil a Laplace distribution of particle displacements at relevant intermediate time scales. For any initial fraction of mobile particles, the respective mean squared displacement displays a plateau. Moreover, we demonstrate a short-time cubic time dependence of the mean squared displacement for immobile tracers when initially all particles are immobile.