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The distribution and abundance of foraging resources are key determinants of animal habitat use and persistence. Decades of agricultural expansion and intensification, along with the introduction of exotic species, have dramatically altered resource distributions in space and time. The nature of contemporary landscapes requires new approaches to understand how mobile organisms utilize the resulting highly fragmented, heterogeneous resources. We used colonies of the native bumble bee (Bombus vosnesenskii) deployed among habitat types and a land use gradient to characterize how resource availability and use change as a function of landscape composition throughout the season in a diverse agricultural region of Northern California. We employ a novel probabilistic framework to identify the spatiotemporal patterns of bumble bee resource use in different habitats. Bumble bee resource preference (i.e., pollen foraging) and availability (i.e., flowering plant abundance) are driven by the composition of the surrounding landscape and the time of year. Bumble bees strongly preferred pollen from native plants, which was overrepresented in samples across the season relative to its estimated availability. Our probabilistic model framework also revealed a strong reliance on seminatural habitat in the landscape (e.g., oak savannahs, chapparal, and riparian corridors)-features that are increasingly rare in anthropogenically dominated landscapes. In fact, pollen resource use by colonies even in the most intensive landscapes was largely limited to interstitial habitat (e.g., field and road edges) despite available mass-flowering crops. Our results highlight the importance of mosaic landscapes (i.e., landscape heterogeneity) in allowing bumble bees to link resources through the season. The framework we develop also serves to enhance predictions of insect resource use within fragmented landscapes.

Forecasting emergent pest spread is paramount to mitigating their impacts. For host-specialized pests, epidemiological models of spread through a single host population are well developed. However, most pests attack multiple host species; the challenge is predicting which communities are most vulnerable to infestation. Here, we develop a phylogenetically-informed approach to predict establishment of emergent multi-host pests across heterogeneous landscapes. We model a beetle-pathogen symbiotic complex on trees, introduced from Southeast Asia to California. The phyloEpi model for likelihood of establishment was predicted from the phylogenetic composition of woody species in the invaded community and the influence of temperature on beetle reproduction. Plant communities dominated by close relatives of known epidemiologically critical hosts were four times more likely to become infested than communities with more distantly related species. Where microclimate favored beetle reproduction, pest establishment was greater than expected based only on species composition. We applied this phyloEpi model to predict infestation risk in California using weather data and complete tree inventories from 9262 1-km2 grids in 170 cities. Regions in the state predicted with low likelihood of infestation were confirmed by independent monitoring. Analysts can adapt these phylogenetic ecology tools to predict spread of any multi-host pest in novel habitats.

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.

Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, and they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool to connect these emerging theories. The assembly graph visually represents assembly dynamics, where nodes symbolise species combinations and edges represent transitions driven by species introductions. Through the lens of assembly graphs, I review how ecological processes reduce uncertainty in random species arrivals (informational approach), identify graphical properties that guarantee species coexistence and examine how the class of dynamical models constrain the topology of assembly graphs (dynamical approach), and quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, I also review methods to decompose complex assembly graphs into smaller, measurable components, as well as computational tools for deriving empirical assembly graphs. In sum, this math-light review of theoretical progress aims to catalyse empirical research towards a predictive understanding of ecological assembly.

Climate change can impact marine ecosystems through many biological and ecological processes. Ecosystem models are one tool that can be used to simulate how the complex impacts of climate change may manifest in a warming world. In this study, we used an end-to-end Atlantis ecosystem model to compare and contrast the effects of climate-driven species redistribution and projected temperature from three separate climate models on species of key commercial importance in the California Current Ecosystem. Adopting a scenario analysis approach, we used Atlantis to measure differences in the biomass, abundance, and weight at age of pelagic and demersal species among six simulations for the years 2013-2100 and tracked the implications of those changes for spatially defined California Current fishing fleets. The simulations varied in their use of forced climate-driven species distribution shifts, time-varying projections of ocean warming, or both. In general, the abundance and biomass of coastal pelagic species like Pacific sardine (Sardinops sagax) and northern anchovy (Engraulis mordax) were more sensitive to projected climate change, while demersal groups like Dover sole (Microstomus pacificus) experienced smaller changes due to counteracting effects of spatial distribution change and metabolic effects of warming. Climate-driven species distribution shifts and the resulting changes in food web interactions were more influential than warming on end-of-century biomass and abundance patterns. Spatial projections of changes in fisheries catch did not always align with changes in abundance of their targeted species. This mismatch is likely due to species distribution shifts into or out of fishing areas and emphasizes the importance of a spatially explicit understanding of both climate change effects and fishing dynamics. We illuminate important biological and ecological pathways through which climate change acts in an ecosystem context and end with a discussion of potential management implications and future directions for climate change research using ecosystem models.

The denitrifying bacterium Thauera sp. MZ1T, a common member of microbial communities in wastewater treatment facilities, can produce different compounds from a range of carbon (C) and nitrogen (N) sources under aerobic and anaerobic conditions. In these different conditions, Thauera modifies its metabolism to produce different compounds that influence the microbial community. In particular, Thauera sp. MZ1T produces different exopolysaccharides with floc-forming properties, impacting the physical disposition of wastewater consortia and the efficiency of nutrient assimilation by the microbial community. Under N-limiting conditions, Thauera sp. MZ1T decreases its growth rate and accelerates the accumulation of polyhydroxyalkanoate-related (PHA) compounds including polyhydroxybutyrate (PHB), which plays a fundamental role as C and energy storage in this β-proteobacterium. However, the metabolic mechanisms employed by Thauera sp. MZ1T to assimilate and catabolize many of the different C and N sources under aerobic and anaerobic conditions remain unknown. Systems biology approaches such as genome-scale metabolic modeling have been successfully used to unveil complex metabolic mechanisms for various microorganisms. Here, we developed a comprehensive metabolic model (M-model) for Thauera sp. MZ1T (iThauera861), consisting of 1,744 metabolites, 2,384 reactions, and 861 genes. We validated the model experimentally using over 70 different C and N sources under both aerobic and anaerobic conditions. iThauera861 achieved a prediction accuracy of 95% for growth on various C and N sources and close to 85% for assimilation of aromatic compounds under denitrifying conditions. The M-model was subsequently deployed to determine the effects of substrates, oxygen presence, and the C:N ratio on the production of PHB and exopolysaccharides (EPS), showing the highest polymer yields are achieved with nucleotides and amino acids under aerobic conditions. This comprehensive M-model will help reveal the metabolic processes by which this ubiquitous species influences communities in wastewater treatment systems and natural environments.

Whether large-scale variation in lineage diversification rates can be predicted by species properties at the population level is a key unresolved question at the interface between micro- and macroevolution. All else being equal, species with biological attributes that confer metapopulation stability should persist more often at timescales relevant to speciation and so give rise to new (incipient) forms that share these biological traits. Here, we develop a framework for testing the relationship between metapopulation properties related to persistence and phylogenetic speciation rates. We illustrate this conceptual approach by applying it to a long-term dataset on demersal fish communities from the North American continental shelf region. We find that one index of metapopulation persistence has phylogenetic signal, suggesting that traits are connected with range-wide demographic patterns. However, there is no relationship between demographic properties and speciation rate. These findings suggest a decoupling between ecological dynamics at decadal timescales and million-year clade dynamics, raising questions about the extent to which population-level processes observable over ecological timescales can be extrapolated to infer biodiversity dynamics more generally.

The [PSI+] prion phenotype in yeast manifests as a white, pink, or red color pigment. Experimental manipulations destabilize prion phenotypes, and allow colonies to exhibit [psi-] (red) sectored phenotypes within otherwise completely white colonies. Further investigation of the size and frequency of sectors that emerge as a result of experimental manipulation is capable of providing critical information on mechanisms of prion curing, but we lack a way to reliably extract this information. Images of experimental colonies exhibiting sectored phenotypes offer an abundance of data to help uncover molecular mechanisms of sectoring, yet the structure of sectored colonies is ignored in traditional biological pipelines. In this study, we present [PSI]-CIC, the first computational pipeline designed to identify and characterize features of sectored yeast colonies. To overcome the barrier of a lack of manually annotated data of colonies, we develop a neural network architecture that we train on synthetic images of colonies and apply to real images of [PSI+] , [psi-] , and sectored colonies. In hand-annotated experimental images, our pipeline correctly predicts the state of approximately 95% of colonies detected and frequency of sectors in approximately 89.5% of colonies detected. The scope of our pipeline could be extended to categorizing colonies grown under different experimental conditions, allowing for more meaningful and detailed comparisons between experiments. Our approach streamlines the analysis of sectored yeast colonies providing a rich set of quantitative metrics and provides insight into mechanisms driving the curing of prion phenotypes.

Vector mosquito control is an integral part of malaria control. The global emergence of insecticide resistance in malaria-transmitting Anophelines has become an impediment and has created an urgent need for novel mosquito control approaches. Here, we show that a biopesticide derived from the soil-dwelling bacterium Chromobacterium sp. Panama (Csp_P) kills insecticide-resistant Anopheles mosquitoes, regardless of their resistance mechanisms. In addition, sublethal dose of Csp_P acts as a synergist to now used chemical insecticides across multiple classes. Moreover, Csp_P reduces host-seeking behavior and malaria parasite infection in vector mosquitoes in ways that further decrease transmission. Mosquito glutathione S-transferases are essential for Csp_Ps mosquito-killing mechanism. Enclosed field trials in Burkina Faso, conducted in diverse ecological settings and supported by a mathematical model, have now demonstrated its potential for malaria control in settings with widespread insecticide resistance.

Cellular metabolism is a complex process involving the consumption and production of metabolites, as well as the regulation of enzyme synthesis and activity. Modeling of metabolic processes is important to understand the underlying mechanisms, with a wide range of applications in metabolic engineering and health sciences. Cybernetic modeling is a powerful technique that accounts for unknown intricate regulatory mechanisms in complex cellular processes. It models regulation as goal-oriented, where the levels and activities of enzymes are modulated by the cybernetic control variables to achieve the cybernetic objective. This study used cybernetic model to study the enzyme competition between arachidonic acid (AA) and eicosapentaenoic acid (EPA) metabolism in murine macrophages. AA and EPA compete for the shared enzyme cyclooxygenase. Upon external stimuli, AA produces proinflammatory 2-series prostaglandins and EPA metabolizes to antiinflammatory 3-series prostaglandins, where proinflammatory and antiinflammatory responses are necessary for homeostasis. The cybernetic model adequately captured the experimental data for control and EPA-supplemented conditions. The model is validated by performing an F-test, conducting leave-one-out-metabolite cross-validation, and predicting an unseen experimental condition. The cybernetic variables provide insights into the competition between AA and EPA for the cyclooxygenase enzyme. Predictions from our model suggest that the system undergoes a switch from a predominantly proinflammatory state in the control to an antiinflammatory state with EPA-supplementation. The model can also be used to analytically determine the AA and EPA concentrations required for the switch to occur. The quantitative outcomes enhance understanding of proinflammatory and antiinflammatory metabolism in RAW 264.7 macrophages.

Human-caused global change produces biotic and abiotic conditions that increase the uncertainty and risk of failure of restoration efforts. A focus of managing for resiliency, that is, the ability of the system to respond to disturbance, has the potential to reduce this uncertainty and risk. However, identifying what drives resiliency might depend on how one measures it. An example of a system where identifying how the drivers of different aspects of resiliency can inform restoration under climate change is the northern coast of California, where kelp experienced a decline in coverage of over 95% due to the combination of an intense marine heat wave and the functional extinction of the primary predator of the kelp-grazing purple sea urchin, the sunflower sea star. Although restoration efforts focused on urchin removal and kelp reintroduction in this system are ongoing, the question of how to increase the resiliency of this system to future marine heat waves remains open. In this paper, we introduce a dynamical model that describes a tritrophic food chain of kelp, purple urchins, and a purple urchin predator such as the sunflower sea star. We run a global sensitivity analysis of three different resiliency metrics (recovery likelihood, recovery rate, and resistance to disturbance) of the kelp forest to identify their ecological drivers. We find that each metric depends the most on a unique set of drivers: Recovery likelihood depends the most on live and drift kelp production, recovery rate depends the most on urchin production and feedbacks that determine urchin grazing on live kelp, and resistance depends the most on feedbacks that determine predator consumption of urchins. Therefore, an understanding of the potential role of predator reintroduction or recovery in kelp systems relies on a comprehensive approach to measuring resiliency.

Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO2 levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES-HYDRO) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086-2100: SSP2-45, SSP5-85) and two CO2 levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1-11.3% under future climate scenarios, and, crucially, elevated CO2 provided only slight amelioration. By contrast, elevated CO2 mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO2 or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo-tropics accompanied by certain trait plant assemblages becoming nonviable.

Nonadherence to direct oral anticoagulant (DOAC) pharmacotherapy may increase the risk of thromboembolism or bleeding, and delayed or missed doses are the most common types of nonadherence. Current recommendations from regulatory agencies or guidelines regarding this issue lack evidence and fail to consider individual differences. This study aimed to develop individual remedial dosing strategies when the dose was delayed or missed for DOACs, including rivaroxaban, apixaban, edoxaban, and dabigatran etexilate. Remedial dosing regimens based on population pharmacokinetic (PK)-pharmacodynamic (PD) modeling and simulation strategies were developed to expeditiously restore drug concentration or PD biomarkers within the therapeutic range. Population PK-PD characteristics of DOACs were retrieved from previously published literature. The effects of factors that influence PK and PD parameters were assessed for their impact on remedial dosing regimens. A web-based dashboard was established with R-shiny to recommend remedial dosing regimens based on patient traits, dosing schedules, and delay duration. Addressing delayed or missed doses relies on the delay time and specific DOACs involved. Additionally, age, body weight, renal function, and polypharmacy may marginally affect remedial strategies. The proposed remedial dosing strategies surpass current recommendations, with less deviation time beyond the therapeutic range. The online dashboard offers quick and convenient solutions for addressing missed or delayed DOACs, enabling individualized remedial dosing strategies based on patient characteristics to mitigate the risks of bleeding and thrombosis.

Many prokaryotic and eukaryotic cells metabolize glucose to organism-specific by-products instead of fully oxidizing it to carbon dioxide and water-a phenomenon referred to as the Warburg Effect. The benefit to a cell is not fully understood, given that partial metabolism of glucose yields an order of magnitude less adenosine triphosphate (ATP) per molecule of glucose than complete oxidation. Here, we test a previously formulated hypothesis that the benefit of the Warburg Effect is to increase ATP production rate by switching from high-yielding respiration to faster glycolysis when excess glucose is available and respiration rate becomes limited by proteome occupancy. We show that glycolysis produces ATP faster per gram of pathway protein than respiration in Escherichia coli, Saccharomyces cerevisiae, and mammalian cells. We then develop a simple mathematical model of energy metabolism that uses five experimentally estimated parameters and show that this model can accurately predict absolute rates of glycolysis and respiration in all three organisms under diverse conditions, providing strong support for the validity of the ATP production rate maximization hypothesis. In addition, our measurements show that mammalian respiration produces ATP up to 10-fold slower than respiration in E. coli or S. cerevisiae, suggesting that the ATP production rate per gram of pathway protein is a highly evolvable trait that is heavily optimized in microbes. We also find that E. coli respiration is faster than fermentation, explaining the observation that E. coli, unlike S. cerevisiae or mammalian cells, never switch to pure fermentation in the presence of oxygen.

The worlds rich diversity of bats supports healthy ecosystems and important ecosystem services. Maintaining healthy biological systems requires prompt identification of threats to biodiversity and immediate action to protect species, which for wide-ranging bat species that span geopolitical boundaries warrants international coordination. Anthropogenic forces drive the threats to bats throughout North America and the world. We conducted an international expert elicitation to assess the status of 153 bat species in Canada, the United States, and Mexico. We used expert assessment to determine the conservation status, highest impact threats, and recent population trends for these species. We found that 53% of North American bat species have moderate to very high risk of extinction in the next 15 years. The highest impact threats varied with species and country, and four IUCN threat categories had the greatest overall impacts: Climate Change, Problematic Species (including disease), Agriculture, and Energy Production. Experts estimated that 90% of species assessed had decreasing population trends over the past 15 years, demonstrating the need for conservation action. Although the state of North American bats is concerning, we identify threats that can be addressed through internationally collaborative, proactive, and protective actions to support the recovery and resilience of North American bat species.

Many animal cells that crawl on extracellular substrates exhibit durotaxis, i.e., directed migration toward stiffer substrate regions. This has implications in several biological processes including tissue development and tumor progression. Here, we introduce a phenomenological model for single-cell durotaxis that incorporates both elastic deformation-mediated cell-substrate interactions and the stochasticity of cell migration. Our model is motivated by a key observation in an early demonstration of durotaxis: a single, contractile cell at a sharp interface between a softer and a stiffer region of an elastic substrate reorients and migrates toward the stiffer region. We model migrating cells as self-propelling, persistently motile agents that exert contractile traction forces on their elastic substrate. The resulting substrate deformations induce elastic interactions with mechanical boundaries, captured by an elastic potential. The dynamics is determined by two crucial parameters: the strength of the cellular traction-induced boundary elastic interaction (A), and the persistence of cell motility (Pe). Elastic forces and torques resulting from the potential orient cells perpendicular (parallel) to the boundary and accumulate (deplete) them at the clamped (free) boundary. Thus, a clamped boundary induces an attractive potential that drives durotaxis, while a free boundary induces a repulsive potential that prevents antidurotaxis. By quantifying the steady-state position and orientation probability densities, we show how the extent of accumulation (depletion) depends on the strength of the elastic potential and motility. We compare and contrast crawling cells with biological microswimmers and other synthetic active particles, where accumulation at confining boundaries is well known. We define metrics quantifying boundary accumulation and durotaxis, and present a phase diagram that identifies three possible regimes: durotaxis, and adurotaxis with and without motility-induced accumulation at the boundary. Overall, our model predicts how durotaxis depends on cell contractility and motility, successfully explains some previous observations, and provides testable predictions to guide future experiments.

Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or null models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our wrap-around method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue The spatial-social interface: a theoretical and empirical integration.

We propose a model for the feedback control processes that underlie the robustness and high sensitivity of mechanosensory hair cells. Our model encompasses self-tuning active processes intrinsic to these cells, which drive the amplification of mechanical stimuli by consuming metabolic energy, and a neural input process that protects these cells from damage caused by powerful stimuli. We explore the effects of these two feedback mechanisms on mechanical self-oscillations of the sense cells and their response to external forcing.

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the timescales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.

Ex vivo human skin models are valuable tools in skin research due to their physiological relevance. Traditionally, standard cultivation is performed in a cell culture incubator with a defined temperature of 37 °C and a specific atmosphere enriched with CO2 to ensure media stability. Maintaining the model under these specific conditions limits its flexibility in assessing exposures to which the skin is exposed to in daily life, for example changes in atmospheric compositions. In this study we demonstrated that the foreskin-derived skin model can be successfully cultured at room temperature outside a CO2 incubator using a CO2-independent, serum-free media. Over a cultivation period of three days, the integrity of the tissue and the preservation of immune cells is well maintained, indicating the models stability and resilience under the given conditions. Exposing our Medical University of Graz - human Organotypic Skin Explant Culture (MUG-hOSEC) model to cytotoxic and inflammatory stimuli results in responses analyzable within the supernatant. Besides the common analysis of released proteins upon treatment, such as cytokines and enzymes, we have included extracellular vesicle to obtain a more comprehensive picture of cell communication.

The genomes of living lungfishes can inform on the molecular-developmental basis of the Devonian sarcopterygian fish-tetrapod transition. We de novo sequenced the genomes of the African (Protopterus annectens) and South American lungfishes (Lepidosiren paradoxa). The Lepidosiren genome (about 91 Gb, roughly 30 times the human genome) is the largest animal genome sequenced so far and more than twice the size of the Australian (Neoceratodus forsteri)1 and African2 lungfishes owing to enlarged intergenic regions and introns with high repeat content (about 90%). All lungfish genomes continue to expand as some transposable elements (TEs) are still active today. In particular, Lepidosiren's genome grew extremely fast during the past 100 million years (Myr), adding the equivalent of one human genome every 10 Myr. This massive genome expansion seems to be related to a reduction of PIWI-interacting RNAs and C2H2 zinc-finger and Krüppel-associated box (KRAB)-domain protein genes that suppress TE expansions. Although TE abundance facilitates chromosomal rearrangements, lungfish chromosomes still conservatively reflect the ur-tetrapod karyotype. Neoceratodus' limb-like fins still resemble those of their extinct relatives and remained phenotypically static for about 100 Myr. We show that the secondary loss of limb-like appendages in the Lepidosiren-Protopterus ancestor was probably due to loss of sonic hedgehog limb-specific enhancers.

Patterns of population connectivity shape ecological and evolutionary phenomena from population persistence to local adaptation and can inform conservation strategy. Connectivity patterns emerge from the interaction of individual behavior with a complex and heterogeneous environment. Despite ample observation that dispersal patterns vary through time, the extent to which variation in the physical environment can explain emergent connectivity variation is not clear. Empirical studies of its contribution promise to illuminate a potential source of variability that shapes the dynamics of natural populations. We leveraged simultaneous direct dispersal observations and oceanographic transport simulations of the clownfish Amphiprion clarkii in the Camotes Sea, Philippines, to assess the contribution of oceanographic variability to emergent variation in connectivity. We found that time-varying oceanographic simulations on both annual and monsoonal timescales partly explained the observed dispersal patterns, suggesting that temporal variation in oceanographic transport shapes connectivity variation on these timescales. However, interannual variation in observed mean dispersal distance was nearly 10 times the expected variation from biophysical simulations, revealing that additional biotic and abiotic factors contribute to interannual connectivity variation. Simulated dispersal kernels also predicted a smaller scale of dispersal than the observations, supporting the hypothesis that undocumented abiotic factors and behaviors such as swimming and navigation enhance the probability of successful dispersal away from, as opposed to retention near, natal sites. Our findings highlight the potential for coincident observations and biophysical simulations to test dispersal hypotheses and the influence of temporal variability on metapopulation persistence, local adaptation, and other population processes.

A major challenge in ecology is to understand how different species interact to determine ecosystem function, particularly in communities with large numbers of co-occurring species. We use a trait-based model of microbial litter decomposition to quantify how different taxa impact ecosystem function. Furthermore, we build a novel framework that highlights the interplay between taxon traits and environmental conditions, focusing on their combined influence on community interactions and ecosystem function. Our results suggest that the ecosystem impact of a taxon is driven by its resource acquisition traits and the community functional capacity, but that physiological stress amplifies the impact of both positive and negative interactions. Furthermore, net positive impacts on ecosystem function can arise even as microbes have negative pairwise interactions with other taxa. As communities shift in response to global climate change, our findings reveal the potential to predict the biogeochemical functioning of communities from taxon traits and interactions.

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune systems capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

When a new molecular entity is predicted to exhibit high clearance in humans, pharmaceutical sponsors almost universally search for similar acting back-up compounds that will demonstrate low clearance. Here we show that, except for oral dosing, there can be marked advantages to developing and commercializing controlled release formulations of high clearance drugs, the expertise of readers of this journal. Our recent publications demonstrate that the universally held pharmacokinetic principle that drug delivery rate has no effect on measured drug clearance is not correct. Rather, we show that if clearance from the drug delivery site is markedly less than the iv bolus clearance of a drug, the in vivo drug clearance can be the drug delivery clearance controlled by the designed dosage form. This approach will be especially advantageous for high hepatic clearance drugs. These advantages include not being concerned with: a) saturable nonlinear kinetics, b) significant pharmacogenomic differences, c) drug-drug induction mechanisms, and d) in many cases drug-drug inhibition interactions. This is due to the ability of a drug sponsor to design clearance, independent of the pharmacokinetic characteristics for high clearance compounds, where clearance from the dosage form becomes the drug clearance from the patient. Recognition of this principle, as described here, results from our development of the use of Kirchhoff's Laws from physics to derive rate-defining clearance and rate constant elimination processes independent of differential equation derivations. The key message for readers of this journal is that high clearance drugs are potentially drugable through formulation design and should not be outright disregarded, since for such drugs the dose-corrected area under the curve can be increased if the release rate from the injection site is controlled and slow resulting in drug clearance from the body controlled by clearance from the dosage form. The concepts presented here describe previously unrecognized advantages of controlled release formulations.

Recently, we have proposed simple methodology to derive clearance and rate constant equations, independent of differential equations, based on Kirchhoff's Laws, a common methodology from physics used to describe rate-defining processes either in series or parallel. Our approach has been challenged in three recent publications, two published in this journal, but notably what is lacking is that none evaluate experimental pharmacokinetic data. As reviewed here, manuscripts from our laboratory have evaluated published experimental data, demonstrating that the Kirchhoff's Laws approach explains (1) why all of the experimental perfused liver clearance data appear to fit the equation that was previously believed to be the well-stirred model, (2) why linear pharmacokinetic systemic bioavailability determinations can be greater than 1, (3) why renal clearance can be a function of drug input processes, and (4) why statistically different bioavailability measures may be found for urinary excretion versus systemic concentration measurements. Our most recent paper demonstrates (5) how the universally accepted steady-state clearance approach used by the field for the past 50 years leads to unrealistic outcomes concerning the relationship between liver-to-blood Kpuu and hepatic availability FH , highlighting the potential for errors in pharmacokinetic evaluations based on differential equations. The Kirchhoff's Laws approach is applicable to all pharmacokinetic analyses of quality experimental data, those that were previously adequately explained with present pharmacokinetic theory, and those that were not. The publications that have attempted to rebut our position do not address unexplained experimental data, and we show here why their analyses are not valid. SIGNIFICANCE STATEMENT: The Kirchhoff's Laws approach to deriving clearance equations for linear systems in parallel or in series, independent of differential equations, successfully describes published pharmacokinetic data that has previously been unexplained. Three recent publications claim to refute our proposed methodology; these publications only make theoretical arguments, do not evaluate experimental data, and never demonstrate that the Kirchhoff methodology provides incorrect interpretations of experimental pharmacokinetic data, including statistically significant data not explained by present pharmacokinetic theory. We demonstrate why these analyses are invalid.

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

There is a strong relationship between metazoan body size and extinction risk. However, the size selectivity and underlying mechanisms in foraminifera, a common marine protozoa, remain controversial. Here, we found that foraminifera exhibit size-dependent extinction selectivity, favoring larger groups (>7.4 log10 cubic micrometer) over smaller ones. Foraminifera showed significant size selectivity in the Guadalupian-Lopingian, Permian-Triassic, and Cretaceous-Paleogene extinctions where the proportion of large genera exceeded 50%. Conversely, in extinctions where the proportion of large genera was

Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant-plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external-driver internal-topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments.

Dose optimization of sirolimus may further improve outcomes in allogeneic hematopoietic cell transplant (HCT) patients receiving post-transplantation cyclophosphamide (PTCy) to prevent graft-versus-host disease (GVHD). Sirolimus exposure-response association studies in HCT patients (i.e., the association of trough concentration with clinical outcomes) have been conflicting. Sirolimus has important effects on T-cells, including conventional (Tcons) and regulatory T-cells (Tregs), both of which have been implicated in the mechanisms by which PTCy prevents GVHD, but there is an absence of validated biomarkers of sirolimus effects on these cell subsets. Considering the paucity of existing biomarkers and the complexities of the immune system, we conducted a literature review to inform a quantitative systems pharmacology (QSP) model of GVHD. The published literature presented multiple challenges. The sirolimus pharmacokinetic models insufficiently describe sirolimus distribution to relevant physiological compartments. Despite multiple publications describing sirolimus effects on Tcons and Tregs in preclinical and human ex vivo models, consistent parameters relating sirolimus concentrations to circulating Tcons and Tregs could not be found. Each aspect presents a challenge in building a QSP model of sirolimus and its temporal effects on T-cell subsets and GVHD prevention. To optimize GVHD prevention regimens, phase I studies and systematic studies of immunosuppressant concentration-effect association are needed for QSP modeling.

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

Decisions to disperse from a habitat stand out among organismal behaviours as pivotal drivers of ecosystem dynamics across scales. Encounters with other species are an important component of adaptive decision-making in dispersal, resulting in widespread behaviours like tracking resources or avoiding consumers in space. Despite this, metacommunity models often treat dispersal as a function of intraspecific density alone. We show, focusing initially on three-species network motifs, that interspecific dispersal rules generally drive a transition in metacommunities from homogeneous steady states to self-organized heterogeneous spatial patterns. However, when ecologically realistic constraints reflecting adaptive behaviours are imposed-prey tracking and predator avoidance-a pronounced homogenizing effect emerges where spatial pattern formation is suppressed. We demonstrate this effect for each motif by computing master stability functions that separate the contributions of local and spatial interactions to pattern formation. We extend this result to species-rich food webs using a random matrix approach, where we find that eventually, webs become large enough to override the homogenizing effect of adaptive dispersal behaviours, leading once again to predominately pattern-forming dynamics. Our results emphasize the critical role of interspecific dispersal rules in shaping spatial patterns across landscapes, highlighting the need to incorporate adaptive behavioural constraints in efforts to link local species interactions and metacommunity structure. This article is part of the theme issue Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics.