Cellular decision-making under stress involves rapid pathway selection despite energy scarcity. Here we demonstrate that thermodynamic constraints actively drive energy-efficient sporulation, where continuous metabolic sources enable system robustness through dynamic energy management. Using hybrid Petri nets (stochastic transitions with continuous sources) to model Bacillus subtilis sporulation, we show that stress conditions (ATP = 300 mM, 94% depletion) enable sporulation completion with extreme energy efficiency: 0.73 mM ATP per mature spore versus 11.6 mM ATP under normal conditions--a 16-fold efficiency gain. Despite ATP dropping to 1 mM (99.7% depletion) during the crisis, continuous ATP regeneration rescues the system, producing 67 mM mature spores (89% of normal yield) with only 49 mM total ATP consumption. This efficiency emerges from the interplay between stochastic regulatory transitions and continuous metabolic sources, where GTP accumulation (+4974 mM, 166% increase) provides an energy buffer while ATP regeneration (+240 mM) prevents complete depletion. The hybrid Petri net formalism--combining stochastic transitions for regulatory events with continuous sources for metabolic flux--extended with thermodynamic constraints through inhibitor arcs and energy-coupled rate functions, provides the mathematical foundation enabling this discovery by integrating discrete regulatory logic with continuous energy dynamics in a resource-aware concurrency model.
William R. Cannon, Ethan King, Katherine A. Huening
et al.
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.
Stress generation by the actin cytoskeleton shapes cells and tissues. Despite impressive progress in live imaging and quantitative physical descriptions of cytoskeletal network dynamics, the connection between processes at molecular scales and cell-scale spatio-temporal patterns is still unclear. Here we review studies reporting acto-myosin clusters of micrometer size and with lifetimes of several minutes in a large number of organisms ranging from fission yeast to humans. Such structures have also been found in reconstituted systems in vitro and in theoretical analysis of cytoskeletal dynamics. We propose that tracking these clusters can serve as a simple readout for characterising living matter. Spatio-temporal patterns of clusters could serve as determinants of morphogenetic processes that play similar roles in diverse organisms.
Metabolic pathways are fundamental maps in biochemistry that detail how molecules are transformed through various reactions. The complexity of metabolic network, where a single compound can play a part in multiple pathways, poses a challenge in inferring metabolic balance changes over time or after different treatments. Isotopic labeling experiment is the standard method to infer metabolic flux, which is currently defined as the flow of a single metabolite through a given pathway over time. However, there is still no way to accurately infer the metabolic balance changes after different treatments in an experiment. This study introduces a different concept: molecular weight distribution, which is the empirical distribution of the molecular weights of all metabolites of interest. By estimating the differences of the location and scale estimates of these distributions, it becomes possible to quantitatively infer the metabolic balance changes even without requiring knowledge of the exact chemical structures of these compounds and their related pathways. This research article provides a mathematical framing for a classic biological concept.
Connor Beveridge, Sanjay Iyer, Caitlin E. Randolph
et al.
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.
Ciaran Murphy-Royal, ShiNung Ching, Thomas Papouin
The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitability. This fostered conceptual advances centered on the notion of reciprocal interactions between neurons and astrocytes, which permitted a critical leap forward in uncovering many roles of astrocytes in brain circuits, and signaled the rise of a major new force in neuroscience: that of glial biology. In the past decade, a multitude of unconventional and disparate functions of astrocytes have been documented that are not predicted by these canonical models and that are challenging to piece together into a holistic and parsimonious picture. This highlights a disconnect between the rapidly evolving field of astrocyte biology and the conceptual frameworks guiding it, and emphasizes the need for a careful reconsideration of how we theorize the functional position of astrocytes in brain circuitry. Here, we propose a unifying, highly transferable, data-driven, and computationally-relevant conceptual framework for astrocyte biology, which we coin contextual guidance. It describes astrocytes as contextual gates that decode multiple environmental factors to shape neural circuitry in an adaptive, state-dependent fashion. This paradigm is organically inclusive of all fundamental features of astrocytes, many of which have remained unaccounted for in previous theories. We find that this new concept provides an intuitive and powerful theoretical space to improve our understanding of brain function and computational models thereof across scales because it depicts astrocytes as a hub for circumstantial inputs into relevant specialized circuits that permits adaptive behaviors at the network and organism level.
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.
Brian CH Cheung, Louis Hodgson, Jeffrey E Segall
et al.
One of the hallmarks of cancer cells is their exceptional ability to migrate within the extracellular matrix (ECM) for gaining access to the circulatory system, a critical step of cancer metastasis. RhoA, a small GTPase, is known to be a key molecular switch that toggles between actomyosin contractility and lamellipodial protrusion during cell migration. Current understanding of RhoA activity in cell migration has been largely derived from studies of cells plated on a two-dimensional (2D) substrate using a FRET biosensor. There has been increasing evidence that cells behave differently in a more physiologically relevant three-dimensional (3D) environment, however, studies of RhoA activities in 3D have been hindered by low signal-to-noise ratio in fluorescence imaging. In this paper, we present a machine learning-assisted FRET technique to follow the spatiotemporal dynamics of RhoA activities of single breast tumor cells (MDA-MB-231) migrating in a 3D as well as a 2D environment using a RhoA biosensor. We found that RhoA activity is more polarized along the long axis of the cell for single cells migrating on 2D fibronectin-coated glass versus those embedded in 3D collagen matrices. In particular, RhoA activities of cells in 2D exhibit a distinct front-to-back and back-to-front movement during migration in contrast to those in 3D. Finally, regardless of dimensionality, RhoA polarization is found to be correlated with cell shape.
Mona K Tonn, Philipp Thomas, Mauricio Barahona
et al.
Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.
AbstractThe iron antimonide FeSb2 possesses an extraordinarily high thermoelectric power factor at low temperature, making it a leading candidate for cryogenic thermoelectric cooling devices. However, the origin of this unusual behavior is controversial, having been variously attributed to electronic correlations as well as the phonon-drag effect. The optical properties of a material provide information on both the electronic and vibrational properties. The optical conductivity reveals an anisotropic response at room temperature; the low-frequency optical conductivity decreases rapidly with temperature, signalling a metal-insulator transition. One-dimensional semiconducting behavior is observed along the b axis at low temperature, in agreement with first-principle calculations. The infrared-active lattice vibrations are also symmetric and extremely narrow, indicating long phonon relaxation times and a lack of electron-phonon coupling. Surprisingly, there are more lattice modes along the a axis than are predicted from group theory; several of these modes undergo significant changes below about 100 K, hinting at a weak structural distortion or phase transition. While the extremely narrow phonon line shapes favor the phonon-drag effect, the one-dimensional behavior of this system at low temperature may also contribute to the extraordinarily high thermopower observed in this material.
Wei-Xiang Chew, Kazunari Kaizu, Masaki Watabe
et al.
Lattice-based stochastic simulators are commonly used to study biological reaction-diffusion processes. Some of these schemes that are based on the reaction-diffusion master equation (RDME), can simulate for extended spatial and temporal scales but cannot directly account for the microscopic effects in the cell such as volume exclusion and diffusion-influenced reactions. Nonetheless, schemes based on the high-resolution microscopic lattice method (MLM) can directly simulate these effects by representing each finite-sized molecule explicitly as a random walker on fine lattice voxels. The theory and consistency of MLM in simulating diffusion-influenced reactions have not been clarified in detail. Here, we examine MLM in solving diffusion-influenced reactions in 3D space by employing the Spatiocyte simulation scheme. Applying the random walk theory, we construct the general theoretical framework underlying the method and obtain analytical expressions for the total rebinding probability and the effective reaction rate. By matching Collins-Kimball and lattice-based rate constants, we obtained the exact expressions to determine the reaction acceptance probability and voxel size. We found that the size of voxel should be about 2% larger than the molecule. MLM is validated by numerical simulations, showing good agreement with the off-lattice particle-based method, eGFRD. MLM run time is more than an order of magnitude faster than eGFRD when diffusing macromolecules with typical concentrations in the cell. MLM also showed good agreements with eGFRD and mean-field models in case studies of two basic motifs of intracellular signaling, the protein production-degradation process and the dual phosphorylation cycle. Moreover, when a reaction compartment is populated with volume-excluding obstacles, MLM captures the non-classical reaction kinetics caused by anomalous diffusion of reacting molecules.
Élie Besserer-Offroy, Rebecca L Brouillette, Sandrine Lavenus
et al.
The human neurotensin 1 receptor (hNTS1) is a G protein-coupled receptor involved in many physiological functions, including analgesia, hypothermia, and hypotension. To gain a better understanding of which signaling pathways or combination of pathways are linked to NTS1 activation and function, we investigated the ability of activated hNTS1, which was stably expressed by CHO-K1 cells, to directly engage G proteins, activate second messenger cascades and recruit \b{eta}-arrestins. Using BRET-based biosensors, we found that neurotensin (NT), NT(8-13) and neuromedin N (NN) activated the Gαq-, Gαi1-, GαoA-, and Gα13-protein signaling pathways as well as the recruitment of \b{eta}-arrestins 1 and 2. Using pharmacological inhibitors, we further demonstrated that all three ligands stimulated the production of inositol phosphate and modulation of cAMP accumulation along with ERK1/2 activation. Interestingly, despite the functional coupling to Gαi1 and GαoA, NT was found to produce higher levels of cAMP in the presence of pertussis toxin, supporting that hNTS1 activation leads to cAMP accumulation in a Gαs-dependent manner. Additionally, we demonstrated that the full activation of ERK1/2 required signaling through both a PTX-sensitive Gi/o-c-Src signaling pathway and PLCb-DAG-PKC-Raf-1- dependent pathway downstream of Gq. Finally, the whole-cell integrated signatures monitored by the cell-based surface plasmon resonance and changes in the electrical impedance of a confluent cell monolayer led to identical phenotypic responses between the three ligands. The characterization of the hNTS1-mediated cellular signaling network will be helpful to accelerate the validation of potential NTS1 biased ligands with an improved therapeutic/adverse effect profile.
We present the results of the tree-level calculation of deep-inelastic leptoproduction, including polarization of target hadron and produced hadron. We also discuss the dependence on transverse momenta of the quarks, which leads to azimuthal asymmetries for the produced hadrons.
We present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. More importantly, we derive a number of consequences from the model that are independent of parameter values. First, that the ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties invariant across perfusion systems. This conclusion is robust even in the presence of multi-stability, which is explained in our model by the negative feedback loop on cell growth due to toxic byproduct accumulation. Moreover, a complex landscape of steady states in continuous cell culture emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. Thus, in order to actually reflect the expected behavior in perfusion, performance benchmarks of cell-lines and culture media should be carried out in a chemostat.