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Statistical mechanics of biomolecular condensates via cavity methods

Physical mechanisms of phase separation in living systems can play key physiological roles and have recently been the focus of intensive studies. The strongly heterogeneous and disordered nature of such phenomena in the biological domain poses difficult modeling challenges that require going beyond mean field approaches based on postulating a free energy landscape. The alternative pathway we take in this work is to tackle the full statistical mechanics problem of calculating the partition function in these systems, starting from microscopic interactions, by means of cavity methods. We illustrate the procedure first on the simple binary case, and we then apply it successfully to ternary systems, in which the naive mean field approximations are proved inadequate. We then demonstrate the agreement with lattice model simulations, to finally contrast our theory also with experiments of coacervate formation by associative de-mixing of nucleotides and poly-lysine in aqueous solution. In this way, different types of evidence are provided to support cavity methods as ideal tools for quantitative modeling of biomolecular condensation, giving an optimal balance between the accurate consideration of spatial aspects of the microscopic dynamics and the fast computational results rooted in their analytical tractability.

cond-mat.stat-mech↗

Modeling bacterial flagellar motor with new structure information: Rotational dynamics of two interacting protein nano-rings

In this article, we develop a mathematical model for the rotary bacterial flagellar motor (BFM) based on the recently discovered structure of the stator complex (MotA$_5$MotB$_2$). The structure suggested that the stator also rotates. The BFM is modeled as two rotating nano-rings that interact with each other. Specifically, translocation of protons through the stator complex drives rotation of the MotA pentamer ring, which in turn drives rotation of the FliG ring in the rotor via interactions between the MotA ring of the stator and the FliG ring of the rotor. Preliminary results from the structure-informed model are consistent with the observed torque-speed relation. More importantly, the model predicts distinctive rotor and stator dynamics and their load dependence, which may be tested by future experiments. Possible approaches to verify and improve the model to further understanding of the molecular mechanism for torque generation in BFM are also discussed.

cond-mat.soft↗

On the ideas of the origin of eukaryotes: a critical review

The origin and early evolution of eukaryotes are one of the major transitions in the evolution of life on earth. One of its most interesting aspects is the emergence of cellular organelles, their dynamics, their functions, and their divergence. Cell compartmentalization and architecture in prokaryotes is a less understood complex property. In eukaryotes it is related to cell size, specific genomic architecture, evolution of cell cycles, biogenesis of membranes and endosymbiotic processes. Explaining cell evolution through form and function demands an interdisciplinary approach focused on microbial diversity, phylogenetic and functional cell biology. Two centuries of views on eukaryotic origin have completed the disciplinary tools necessarily to answer these questions. We have moved from Haeckel SCALA NATURAE to the un-rooted tree of life. However, the major relations among cell domains are still elusive and keep the nature of eukaryotic ancestor enigmatic. Here we present a review on state of art views of eukaryogenesis; the background and perspectives of different disciplines involved in this topic

q-bio.PE↗

Control of protein-based pattern formation via guiding cues

Proteins control many vital functions in living cells, such as cell growth and cell division. Reliable coordination of these functions requires the spatial and temporal organizaton of proteins inside cells, which encodes information about the cell's geometry and the cell-cycle stage. Such protein patterns arise from protein transport and reaction kinetics, and they can be controlled by various guiding cues within the cell. Here, we review how protein patterns are guided by cell size and shape, by other protein patterns that act as templates, and by the mechanical properties of the cell. The basic mechanisms of guided pattern formation are elucidated with reference to recent observations in various biological model organisms. We posit that understanding the controlled formation of protein patterns in cells will be an essential part of understanding information processing in living systems.

physics.bio-ph↗

The Cell Physiome: What do we need in a computational physiology framework for predicting single cell biology?

Modern biology and biomedicine are undergoing a big-data explosion needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome: a framework and approach for creating, sharing, and using biophysics-based computational models of single cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date towards creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and re-usability of computational models of cells at scale. We conclude with a look forward into steps that will help fully realize this exciting new field of mechanistic biomedical data science.

q-bio.CB↗

libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library

Motivation: This paper presents libRoadRunner 2.0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language SBML). Results: libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python or Julia interface. libRoadRunner uses a custom Just-In-Time JIT compiler built on the widely-used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a large variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and nonlinear algebraic equations) and including several SBML extensions such as composition and distributions. It offers multiple deterministic and stochastic integrators, as well as tools for steady-state, sensitivity, stability analysis, and structural analysis of the stoichiometric matrix. Availability: libRoadRunner binary distributions are available for Mac OS X, Linux, and Windows. The library is licensed under the Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi and can in principle be compiled on any system supported by LLVM-13. http://sys-bio.github.io/roadrunner/index.html provides online documentation, full build instructions, binaries, and a git source repository.

q-bio.QM↗

Apparent anomalous diffusion and non-Gaussian distributions in a simple mobile-immobile transport model with Poissonian switching

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.

cond-mat.stat-mech↗

Network-based community detection of comorbidities and their association with SARS-CoV-2 virus during COVID-19 pathogenesis

Recent studies emphasized the necessity to identify key (human) biological processes and pathways targeted by the Coronaviridae family of viruses, especially SARS-CoV-2. COVID-19 caused up to 33-55\% death rates in COVID-19 patients with malignant neoplasms and Alzheimer's disease. Given this scenario, we identified biological processes and pathways which are most likely affected by COVID-19. The associations between various diseases and human genes known to interact with viruses from Coronaviridae family were obtained from the IntAct COVID-19 data set annotated with DisGeNET data. We constructed the disease-gene network to identify genes that are involved in various comorbid diseased states. Communities from the disease-gene network through Louvain method were identified and functional enrichment through over-representation analysis methodology was used to discover significant biological processes and pathways shared between COVID-19 and other diseases. The IntAct COVID-19 data set comprised of 828 human genes and 10,473 diseases that together constituted nodes in the disease-gene network. Each of the 70,210 edges connects a human gene with an associated disease. The top 10 genes linked to most number of diseases were VEGFA, BCL2, CTNNB1, ALB, COX2, AGT, HLA-A, HMOX1, FGT2 and COMT. The most vulnerable group of patients thus discovered had comorbid conditions such as carcinomas, malignant neoplasms and Alzheimer's disease. Finally, we identified 37 potentially useful biological processes and pathways for improved therapies.

q-bio.MN↗

The End-to-End Molecular Communication Model of Extracellular Vesicle-based Drug Delivery

A closer look at nature has recently brought more interest in exploring and utilizing intra-body communication networks composed of cells as intrinsic, perfectly biocompatible infrastructures to deliver therapeutics. Naturally occurring cell-to-cell communication systems are being manipulated to release, navigate, and take up soluble cell-derived messengers that are either therapeutic by nature or carry therapeutic molecular cargo in their structures. One example of such structures is extracellular vesicles (EVs) which have been recently proven to have favorable pharmacokinetic properties, opening new avenues for developing the next generation biotherapeutics. In this paper, we study theoretical aspects of the EV transfer within heart tissue as a case study by utilizing an information and communication technology-like approach in analyzing molecular communication systems. Our modeling implies the abstraction of the EV releasing cells as transmitters, the extracellular matrix as the channel, and the EV receiving cells as receivers. Our results, derived from the developed analytical models, indicate that the release can be modulated using external forces such as electrical signals, and the transfer and reception can be affected by the extracellular matrix and plasma membrane properties, respectively. The results can predict the EV biodistributions and contribute to avoiding unplanned administration, often resulting in side- and adverse effects.

eess.SP↗

Deep learning-based identification of sub-nuclear structures in FIB-SEM images

Three-dimensional volumetric imaging of cells allows for in situ visualization, thus preserving contextual insights into cellular processes. Despite recent advances in machine learning methods, morphological analysis of sub-nuclear structures have proven challenging due to both the shallow contrast profile and the technical limitation in feature detection. Here, we present a convolutional neural network, supervised deep learning-based approach which can identify sub-nuclear structures with 90% accuracy. We develop and apply this model to C. elegans gonads imaged using focused ion beam milling combined with scanning electron microscopy resulting in the accurate identification and segmentation of all sub-nuclear structures including entire chromosomes. We discuss in depth the architecture, parameterization, and optimization of the deep learning model, as well as provide evaluation metrics to assess the quality of the network prediction. Lastly, we highlight specific aspects of the model that can be optimized for its broad application to other volumetric imaging data as well as in situ cryo-electron tomography.

q-bio.QM↗

Applications of PDEs and Stochastic Modeling to Protein Transport in Cell Biology

Intracellular transport processes are essential to the healthy development of many organisms as well as more generally to healthy cellular function. The complex dynamics and interactions between protein molecules and filaments on different time and spatial scales generate many opportunities for mathematical modeling and analysis that can provide insights into protein sorting and overall cellular organization. Systems of advection-reaction-diffusion partial differential equations and stochastic state-switching particle models are especially useful in understanding spatiotemporal protein dynamics inside cells.

math.DS↗

Network strategies to study Epstein-Barr virus associated carcinomas and potential etiological mechanisms for oncogenesis

Diseased conditions are a consequence of some abnormality that are associated with clinical conditions in numerous cells and tissues affecting various organs. The common role of EBV (Epstein-Barr virus) in causing infectious mononucleosis (IM) affecting B-cells and epithelial cells and the development of EBV-associated cancers has been an area of active research. Investigating such significant interactions may help discover new therapeutic targets for certain EBV-associated lymphoproliferative (Burkitt's Lymphoma and Hodgkin's Lymphoma) and non-lymphoproliferative diseases (Gastric cancer and Nasopharyngeal cancer). Based on the DisGeNET (v7.0) data set, we constructed a disease-gene network bipartite graph to identify genes that are involved in various carcinomas namely, gastric cancer (GC), nasopharyngeal cancer (NPC), Hodgkin's lymphoma (HL) and Burkitt's lymphoma (BL). Using the community detection algorithm (Louvain method), we identified communities followed by functional enrichment using over-representation analysis methodology. In this study, we identified the modular communities to explore the relation of this common causative pathogen (EBV) with different carcinomas such as GC, NPC, HL and BL. We could identify the top 10 genes as CASP10, BRAF, NFKBIA, IFNA2, GSTP1, CSF3, GATA3, UBR5, AXIN2 and POLE based on their degree of distribution. Further over-representation analysis showed that the ABL1 gene was significantly over-represented in 3 out of 9 critical biological processes. As a result, we can infer that the EBV pathogen is selective in targeting critical pathways to bring about cellular growth arrest/apoptosis and interfering with vital biological processes, including the TP53 network of genes that leads to further proliferation of damage to vital cellular activities.

q-bio.MN↗

Theoretical analysis of cargo transport by catch bonded motors in optical trapping assays

Dynein motors exhibit catch bonding, where the unbinding rate of the motors from microtubule filaments decreases with increasing opposing load. The implications of this catch bond on the transport properties of dynein-driven cargo are yet to be fully understood. In this context, optical trapping assays constitute an important means of accurately measuring the forces generated by molecular motor proteins. We investigate, using theory and stochastic simulations, the transport properties of cargo transported by catch bonded dynein molecular motors - both singly and in teams - in a harmonic potential, which mimics the variable force experienced by cargo in an optical trap. We estimate the biologically relevant measures of first passage time - the time during which the cargo remains bound to the microtubule and detachment force -the force at which the cargo unbinds from the microtubule, using both two-dimensional and one-dimensional force balance frameworks. Our results suggest that even for cargo transported by a single motor, catch bonding may play a role depending on the force scale which marks the onset of the catch bond. By comparing with experimental measurements on single dynein-driven transport, we estimate realistic bounds of this catch bond force scale. Generically, catch bonding results in increased persistent motion, and can also generate non-monotonic behaviour of first passage times. For cargo transported by multiple motors, emergent collective effects due to catch bonding can result in non-trivial re-entrant phenomena wherein average first passage times and detachment forces exhibit non-monotonic behaviour as a function of the stall force and the motor velocity.

physics.bio-ph↗

Conditional Probability as Found in Nature: Facilitated Diffusion

Transcription Factors (TFs) are proteins that regulate gene expression. The regulation mechanism is via the binding of a TF to a specific part of the gene associated with it, the TF's target. The target of a specific TF corresponds to a vanishingly small part of the entire DNA, where at the same time the search must end in a matter of tens of seconds at most for its biological purpose to be fulfilled - this makes the search a problem of high interest. Facilitated Diffusion is a mechanism used in nature for a robust and efficient search process. This mechanism combines 1D diffusion along the DNA and "excursions" of diffusion in 3D that help the TF to quickly arrive at distant parts of the DNA. In this paper we provide a derivation concerning this mechanism that links this search process to fundamental concepts in probability theory (conditional probability).

cond-mat.stat-mech↗

Molecular motors enhance microtubule lattice plasticity

Microtubules are key structural elements of living cells that are crucial for cell division, intracellular transport and motility. Recent experiments have shown that microtubule severing proteins and molecular motors stimulate the direct and localized incorporation of free tubulin into the shaft. However, a mechanistic picture how microtubule associated proteins affect the lattice is completely missing. Here we theoretically explore a potential mechanism of lattice turnover stimulated by processive molecular motors in which a weak transient destabilization of the lattice by the motor stepping promotes the formation of mobile vacancies. In the absence of free tubulin the defect rapidly propagates leading to a complete fracture. In the presence of free tubulin, the motor walk induces a vacancy drift in the direction opposite of the motor walk. The drift is accompanied by the direct and localized incorporation of free tubulin along the trajectory of the vacancy. Our results are consistent with experiments and strongly suggest that a weak lattice-motor interaction is responsible for an augmented microtubule shaft plasticity.

cond-mat.soft↗

Thermodynamic force thresholds biomolecular behavior

In living systems, collective molecular behavior is driven by thermodynamic forces in the form of chemical gradients. Leveraging recent advances in the field of nonequilibrium physics, I show that increasing the thermodynamic force alone can induce qualitatively new behavior. To demonstrate this principle, general equations governing kinetic proofreading and microtubule assembly are derived. These equations show that new capabilities, including catalytic regulation of steady-state behavior and exponential enhancement of molecular discrimination, are only possible if the system is driven sufficiently far from equilibrium, and can emerge sharply at a threshold force. Regardless of design parameters, these results reveal that the thermodynamic force sets fundamental performance limits on tuning sensitivity, error, and waste. Experimental data show that these biomolecular processes operate at the limits allowed by theory.

q-bio.SC↗

Quantitative elemental imaging in eukaryotic algae

All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved with how these elements are utilized, for what purpose and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active centers of essential enzymes, can determine the state of metabolism, the nutritional status or the developmental stage of an organism. Photosynthetic eukaryotes, especially algae, are excellent subjects for quantitative analysis of elemental distribution. These microbes utilize unique metabolic pathways that require various trace nutrients at their core to enable its operation. Photosynthetic microbes also have important environmental roles as primary producers in habitats with limited nutrient supply or toxin contaminations. Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution. A number of diverse applications were developed for elemental imaging allowing subcellular resolution, with X-ray fluorescence microscopy (XFM) being at the forefront, enabling quantitative descriptions of intact cells in a non-destructive method. This Tutorial Review summarizes the workflow of a quantitative, single-cell elemental distribution analysis of a eukaryotic alga using XFM.

q-bio.SC↗

Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion

Morphogenesis, the establishment and repair of emergent complex anatomy by groups of cells, is a fascinating and biomedically-relevant problem. One of its most fascinating aspects is that a developing embryo can reliably recover from disturbances, such as splitting into twins. While this reliability implies some type of goal-seeking error minimization over a morphogenic field, there are many gaps with respect to detailed, constructive models of such a process being used to implement the collective intelligence of cellular swarms. We describe a closed-loop negative-feedback system for creating reaction-diffusion (RD) patterns with high reliability. It uses a cellular automaton to characterize a morphogen pattern, then compares it to a goal and adjusts accordingly, providing a framework for modeling anatomical homeostasis and robust generation of target morphologies. Specifically, we create a RD pattern with N repetitions, where N is easily changeable. Furthermore, the individual repetitions of the RD pattern can be easily stretched or shrunk under genetic control to create, e.g., some morphological features larger than others. Finally, the cellular automaton uses a computation wave that scans the morphogen pattern unidirectionally to characterize the features that the negative feedback then controls. By taking advantage of a prior process asymmetrically establishing planar polarity (e.g., head vs. tail), our automaton is greatly simplified. This work contributes to the exciting effort of understanding design principles of morphological computation, which can be used to understand evolved developmental mechanisms, manipulate them in regenerative medicine settings, or embed a degree of synthetic intelligence into novel bioengineered constructs.

q-bio.MN↗