Associating with kin selects for disease resistance and against tolerance.

Behavioural and physiological resistance are key to slowing epidemic spread. We explore the evolutionary and epidemic consequences of their different costs for the evolution of tolerance that trades off with resistance. Behavioural resistance affects social cohesion, with associated group-level costs, while the cost of physiological resistance accrues only to the individual. Further, resistance, and the associated reduction in transmission, benefit susceptible hosts directly, whereas infected hosts only benefit indirectly, by reducing transmission to kin. We therefore model the coevolution of transmission-reducing resistance expressed in susceptible hosts with resistance expressed in infected hosts, as a function of kin association, and analyse the effect on population-level outcomes. Using parameter values for guppies, Poecilia reticulata, and their gyrodactylid parasites, we find that: (1) either susceptible or infected hosts should invest heavily in resistance, but not both; (2) kin association drives investment in physiological resistance more strongly than in behavioural resistance; and (3) even weak levels of kin association can favour altruistic infected hosts that invest heavily in resistance (versus selfish tolerance), eliminating parasites. Overall, our finding that weak kin association affects the coevolution of infected and susceptible investment in both behavioural and physiological resistance suggests that kin selection may affect disease dynamics across systems.

Self-generating autocatalytic networks: structural results, algorithms and their relevance to early biochemistry.

The concept of an autocatalytic network of reactions that can form and persist, starting from just an available food source, has been formalized by the notion of a reflexively autocatalytic and food-generated (RAF) set. The theory and algorithmic results concerning RAFs have been applied to a range of settings, from metabolic questions arising at the origin of life, to ecological networks, and cognitive models in cultural evolution. In this article, we present new structural and algorithmic results concerning RAF sets, by studying more complex modes of catalysis that allow certain reactions to require multiple catalysts (or to not require catalysis at all), and discuss the differing ways catalysis has been viewed in the literature. We also focus on the structure and analysis of minimal RAFs and derive structural results and polynomial-time algorithms. We then apply these new methods to a large metabolic network to gain insights into possible biochemical scenarios near the origin of life.

Confinement induces internal flows in adherent cell aggregates.

During mesenchymal migration, F-actin protrusion at the leading edge and actomyosin contraction determine the retrograde flow of F-actin within the lamella. The coupling of this flow to integrin-based adhesions determines the force transmitted to the extracellular matrix and the net motion of the cell. In tissues, motion may also arise from convection, driven by gradients in tissue-scale surface tensions and pressures. However, how migration coordinates with convection to determine the net motion of cellular ensembles is unclear. To explore this, we study the spreading of cell aggregates on adhesive micropatterns on compliant substrates. During spreading, a cell monolayer expands from the aggregate towards the adhesive boundary. However, cells are unable to stabilize the protrusion beyond the adhesive boundary, resulting in retraction of the protrusion and detachment of cells from the matrix. Subsequently, the cells move upwards and rearwards, yielding a bulk convective flow towards the centre of the aggregate. The process is cyclic, yielding a steady-state balance between outward (protrusive) migration along the surface, and 'retrograde' (contractile) flows above the surface. Modelling the cell aggregates as confined active droplets, we demonstrate that the interplay between surface tension-driven flows within the aggregate, radially outward monolayer flow and conservation of mass leads to an internal circulation.

The effect of muscle ultrastructure on the force, displacement and work capacity of skeletal muscle.

Skeletal muscle powers animal movement through interactions between the contractile proteins, actin and myosin. Structural variation contributes greatly to the variation in mechanical performance observed across muscles. In vertebrates, gross structural variation occurs in the form of changes in the muscle cross-sectional area : fibre length ratio. This results in a trade-off between force and displacement capacity, leaving work capacity unaltered. Consequently, the maximum work per unit volume-the work density-is considered constant. Invertebrate muscle also varies in muscle ultrastructure, i.e. actin and myosin filament lengths. Increasing actin and myosin filament lengths increases force capacity, but the effect on muscle fibre displacement, and thus work, capacity is unclear. We use a sliding-filament muscle model to predict the effect of actin and myosin filament lengths on these mechanical parameters for both idealized sarcomeres with fixed actin : myosin length ratios, and for real sarcomeres with known filament lengths. Increasing actin and myosin filament lengths increases stress without reducing strain capacity. A muscle with longer actin and myosin filaments can generate larger force over the same displacement and has a higher work density, so seemingly bypassing an established trade-off. However, real sarcomeres deviate from the idealized length ratio suggesting unidentified constraints or selective pressures.

Effect of fluid elasticity on the emergence of oscillations in an active elastic filament.

Many microorganisms propel themselves through complex media by deforming their flagella. The beat is thought to emerge from interactions between forces of the surrounding fluid, the passive elastic response from deformations of the flagellum and active forces from internal molecular motors. The beat varies in response to changes in the fluid rheology, including elasticity, but there are limited data on how systematic changes in elasticity alter the beat. This work analyses a related problem with fixed-strength driving force: the emergence of beating of an elastic planar filament driven by a follower force at the tip of a viscoelastic fluid. This analysis examines how the onset of oscillations depends on the strength of the force and viscoelastic parameters. Compared to a Newtonian fluid, it takes more force to induce the instability in viscoelastic fluids, and the frequency of the oscillation is higher. The linear analysis predicts that the frequency increases with the fluid relaxation time. Using numerical simulations, the model predictions are compared with experimental data on frequency changes in the bi-flagellated alga Chlamydomonas reinhardtii. The model shows the same trends in response to changes in both fluid viscosity and Deborah number and thus provides a possible mechanistic understanding of the experimental observations.

A Model of Gastric Mucosal pH Regulation: Extending Sensitivity Analysis Using Sobol' Indices to Understand Higher Moments.

Several recent theoretical studies have indicated that a relatively simple secretion control mechanism in the epithelial cells lining the stomach may be responsible for maintaining a neutral (healthy) pH adjacent to the stomach wall, even in the face of enormous electrodiffusive acid transport from the interior of the stomach. Subsequent work used Sobol' Indices (SIs) to quantify the degree to which this secretion mechanism is "self-regulating" i.e. the degree to which the wall pH is held neutral as mathematical parameters vary. However, questions remain regarding the nature of the control that specific parameters exert over the maintenance of a healthy stomach wall pH. Studying the sensitivity of higher moments of the statistical distribution of a model output can provide useful information, for example, how one parameter may skew the distribution towards or away from a physiologically advantageous regime. In this work, we prove a relationship between SIs and the higher moments and show how it can potentially reduce the cost of computing sensitivity of said moments. We define γ -indices to quantify sensitivity of variance, skewness, and kurtosis to the choice of value of a parameter, and we propose an efficient strategy that uses both SIs and γ -indices for a more comprehensive sensitivity analysis. Our analysis uncovers a control parameter which governs the "tightness of control" that the secretion mechanism exerts on wall pH. Finally, we discuss how uncertainty in this parameter can be reduced using expert information about higher moments, and speculate about the physiological advantage conferred by this control mechanism.

Assessing the hydromechanical control of plant growth.

Multicellular organisms grow and acquire their shapes through the differential expansion and deformation of their cells. Recent research has addressed the role of cell and tissue mechanical properties in these processes. In plants, it is believed that growth rate is a function of the mechanical stress exerted on the cell wall, the thin polymeric layer surrounding cells, involving an effective viscosity. Nevertheless, recent studies have questioned this view, suggesting that cell wall elasticity sets the growth rate or that uptake of water is limiting for plant growth. To assess these issues, we developed a microfluidic device to quantify the growth rates, elastic properties and hydraulic conductivity of individual Marchantia polymorpha plants in a controlled environment with a high throughput. We characterized the effect of osmotic treatment and abscisic acid on growth and hydromechanical properties. Overall, the instantaneous growth rate of individuals is correlated with both bulk elastic modulus and hydraulic conductivity. Our results are consistent with a framework in which the growth rate is determined primarily by the elasticity of the wall and its remodelling, and secondarily by hydraulic conductivity. Accordingly, the coupling between the chemistry of the cell wall and the hydromechanics of the cell appears as key to set growth patterns during morphogenesis.

Short-range correlation of stress chains near solid-to-liquid transition in active monolayers.

Using a three-dimensional model of cell monolayers, we study the spatial organization of active stress chains as the monolayer transitions from a solid to a liquid state. The critical exponents that characterize this transition map the isotropic stress percolation onto the two-dimensional random percolation universality class, suggesting short-range stress correlations near this transition. This mapping is achieved via two distinct, independent pathways: (i) cell-cell adhesion and (ii) active traction forces. We unify our findings by linking the nature of this transition to high-stress fluctuations, distinctly linked to each pathway. The results elevate the importance of the transmission of mechanical information in dense active matter and provide a new context for understanding the non-equilibrium statistical physics of phase transition in active systems.

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25-60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.

Modeling airway persistent infection of Moraxella catarrhalis and nontypeable Haemophilus influenzae by using human in vitro models.

Non-typeable Haemophilus influenzae (NTHi) and Moraxella catarrhalis (Mcat) are two common respiratory tract pathogens often associated with acute exacerbations in Chronic Obstructive Pulmonary Disease (COPD) as well as with otitis media (OM) in children. Although there is evidence that these pathogens can adopt persistence mechanisms such as biofilm formation, the precise means through which they contribute to disease severity and chronicity remains incompletely understood, posing challenges for their effective eradication. The identification of potential vaccine candidates frequently entails the characterization of the host-pathogen interplay in vitro even though this approach is limited by the fact that conventional models do not permit long term bacterial infections. In the present work, by using air-liquid-interface (ALI) human airway in vitro models, we aimed to recreate COPD-related persistent bacterial infections. In particular, we explored an alternative use of the ALI system consisting in the assembly of an inverted epithelium grown on the basal part of a transwell membrane with the aim to enable the functionality of natural defense mechanisms such as mucociliary clearance and cellular extrusion that are usually hampered during conventional ALI infection experiments. The inversion of the epithelium did not affect tissue differentiation and considerably delayed NTHi or Mcat infection progression, allowing one to monitor host-pathogen interactions for up to three weeks. Notably, the use of these models, coupled with confocal and transmission electron microscopy, revealed unique features associated with NTHi and Mcat infection, highlighting persistence strategies including the formation of intracellular bacterial communities (IBCs) and surface-associated biofilm-like structures. Overall, this study demonstrates the possibility to perform long term host-pathogen investigations in vitro with the aim to define persistence mechanisms adopted by respiratory pathogens and individuate potential new vaccine targets.

How total mRNA influences cell growth.

Experimental observations tracing back to the 1960s imply that ribosome quantities play a prominent role in determining a cell's growth. Nevertheless, in biologically relevant scenarios, growth can also be influenced by the levels of mRNA and RNA polymerase. Here, we construct a quantitative model of biosynthesis providing testable scenarios for these situations. The model explores a theoretically motivated regime where RNA polymerases compete for genes and ribosomes for transcripts and gives general expressions relating growth rate, mRNA concentrations, ribosome, and RNA polymerase levels. On general grounds, the model predicts how the fraction of ribosomes in the proteome depends on total mRNA concentration and inspects an underexplored regime in which the trade-off between transcript levels and ribosome abundances sets the cellular growth rate. In particular, we show that the model predicts and clarifies three important experimental observations, in budding yeast and Escherichia coli bacteria: i) that the growth-rate cost of unneeded protein expression can be affected by mRNA levels, ii) that resource optimization leads to decreasing trends in mRNA levels at slow growth, and iii) that ribosome allocation may increase, stay constant, or decrease, in response to transcription-inhibiting antibiotics. Since the data indicate that a regime of joint limitation may apply in physiological conditions and not only to perturbations, we speculate that this regime is likely self-imposed.

Stability of a biomembrane tube covered with proteins.

Membrane tubes are essential structural features in cells that facilitate biomaterial transport and inter- and intracellular signaling. The shape of these tubes can be regulated by the proteins that surround and adhere to them. We study the stability of a biomembrane tube coated with proteins by combining linear stability analysis, out-of-equilibrium hydrodynamic calculations, and numerical solutions of a Helfrich-like membrane model. Our analysis demonstrates that both long- and short-wavelength perturbations can destabilize the tubes. Numerical simulations confirm the derived linear stability criteria and yield the nonlinearly perturbed vesicle shapes. Our study highlights the interplay between membrane shape and protein density, where the shape instability concurs with a redistribution of proteins into a banded pattern.

Evidence of scale-free clusters of vegetation in tropical rainforests.

Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's "green lungs."

Role of tumbling in bacterial scattering at convex obstacles.

Active propulsion, as performed by bacteria and Janus particles, in combination with hydrodynamic interaction results in the accumulation of bacteria at a flat wall. However, in microfluidic devices with cylindrical pillars of sufficiently small radius, self-propelled particles can slide along and scatter off the surface of a pillar, without becoming trapped over long times. This nonequilibrium scattering process has been predicted to result in large diffusivities, even at high obstacle density, unlike particles that undergo classical specular reflection. Here, we test this prediction by experimentally studying the nonequilibrium scattering of pusherlike swimmers in microfluidic obstacle lattices. To explore the role of tumbles in the scattering process, we microscopically tracked wild-type (run and tumble) and smooth-swimming (run only) mutants of the bacterium Escherichia coli scattering off microfluidic pillars. We quantified key scattering parameters and related them to previously proposed models that included a prediction for the diffusivity, discussing their relevance. Finally, we discuss potential interpretations of the role of tumbles in the scattering process and connect our work to the broader study of swimmers in porous media.

Morphological Stability for in silico Models of Avascular Tumors.

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principle in silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework's flexibility, such additions can be readily included whenever the relevant data become available.

Sex in cardiovascular disease: Why this biological variable should be considered in in vitro models.

Cardiovascular disease (CVD), the world's leading cause of death, exhibits notable epidemiological, clinical, and pathophysiological differences between sexes. Many such differences can be linked back to cardiovascular sexual dimorphism, yet sex-specific in vitro models are still not the norm. A lack of sex reporting and apparent male bias raises the question of whether in vitro CVD models faithfully recapitulate the biology of intended treatment recipients. To ensure equitable treatment for the overlooked female patient population, sex as a biological variable (SABV) inclusion must become commonplace in CVD preclinical research. Here, we discuss the role of sex in CVD and underlying cardiovascular (patho)physiology. We review shortcomings in current SABV practices, describe the relevance of sex, and highlight emerging strategies for SABV inclusion in three major in vitro model types: primary cell, stem cell, and three-dimensional models. Last, we identify key barriers to inclusive design and suggest techniques for overcoming them.

The Warburg Effect Reinterpreted 100 yr on: A First-Principles Stoichiometric Analysis and Interpretation from the Perspective of ATP Metabolism in Cancer Cells.

The Warburg Effect is a longstanding enigma in cancer biology. Despite the passage of 100 yr since its discovery, and the accumulation of a vast body of research on the subject, no convincing biochemical explanation has been given for the original observations of aerobic glycolysis in cancer cell metabolism. Here, we have worked out a first-principles quantitative analysis of the problem from the principles of stoichiometry and available electron balance. The results have been interpreted using Nath's unified theory of energy coupling and adenosine triphosphate (ATP) synthesis, and the original data of Warburg and colleagues have been analyzed from this new perspective. Use of the biomass yield based on ATP per unit substrate consumed, [Formula: see text], or the Nath-Warburg number, NaWa has been shown to excellently model the original data on the Warburg Effect with very small standard deviation values, and without employing additional fitted or adjustable parameters. Based on the results of the quantitative analysis, a novel conservative mechanism of synthesis, utilization, and recycling of ATP and other key metabolites (eg, lactate) is proposed. The mechanism offers fresh insights into metabolic symbiosis and coupling within and/or among proliferating cells. The fundamental understanding gained using our approach should help in catalyzing the development of more efficient metabolism-targeting anticancer drugs.

A mathematical model on the propagation of tau pathology in neurodegenerative diseases.

A system of partial differential equations is developed to study the spreading of tau pathology in the brain for Alzheimer's and other neurodegenerative diseases. Two cases are considered with one assuming intracellular diffusion through synaptic activities or the nanotubes that connect the adjacent cells. The other, in addition to intracellular spreading, takes into account of the secretion of the tau species which are able to diffuse, move with the interstitial fluid flow and subsequently taken up by the surrounding cells providing an alternative pathway for disease spreading. Cross membrane transport of the tau species are considered enabling us to examine the role of extracellular clearance of tau protein on the disease status. Bifurcation analysis is carried out for the steady states of the spatially homogeneous system yielding the results that fast cross-membrane transport combined with effective extracellular clearance is key to maintain the brain's healthy status. Numerical simulations of the first case exhibit solutions of travelling wave form describing the gradual outward spreading of the pathology; whereas the second case shows faster spreading with the buildup of neurofibrillary tangles quickly elevated throughout. Our investigation thus indicates that the gradual progression of the intracellular spreading case is more consistent with the clinical observations of the development of Alzheimer's disease.

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kcat data.

BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study. RESULTS: Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based kcat data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct kcat collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted kcat values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila. CONCLUSIONS: In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based kcat data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism.

A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.

Stomata are pores on plant aerial surfaces, each bordered by a pair of guard cells. They control gas exchange vital for plant survival. Understanding how guard cells respond to environmental signals such as atmospheric carbon dioxide (CO2) levels is not only insightful to fundamental biology but also relevant to real-world issues of crop productivity under global climate change. In the past decade, multiple important signaling elements for stomatal closure induced by elevated CO2 have been identified. Yet, there is no comprehensive understanding of high CO2-induced stomatal closure. In this work, we assemble a cellular signaling network underlying high CO2-induced stomatal closure by integrating evidence from a comprehensive literature analysis. We further construct a Boolean dynamic model of the network, which allows in silico simulation of the stomatal closure response to high CO2 in wild-type Arabidopsis thaliana plants and in cases of pharmacological or genetic manipulation of network nodes. Our model has a 91% accuracy in capturing known experimental observations. We perform network-based logical analysis and reveal a feedback core of the network, which dictates cellular decisions in closure response to high CO2. Based on these analyses, we predict and experimentally confirm that applying nitric oxide (NO) induces stomatal closure in ambient CO2 and causes hypersensitivity to elevated CO2. Moreover, we predict a negative regulatory relationship between NO and the protein phosphatase ABI2 and find experimentally that NO inhibits ABI2 phosphatase activity. The experimental validation of these model predictions demonstrates the effectiveness of network-based modeling and highlights the decision-making role of the feedback core of the network in signal transduction. We further explore the model's potential in predicting targets of signaling elements not yet connected to the CO2 network. Our combination of network science, in silico model simulation, and experimental assays demonstrates an effective interdisciplinary approach to understanding system-level biology.