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DOE OSTI2025

Comparison of greenhouse gas emission estimates from six hydropower reservoirs using modeling versus field surveys

As with most aquatic ecosystems, reservoirs play an important role in the global carbon (C) cycle and emit greenhouse gases (GHG) as carbon dioxide (CO 2 ) and methane (CH 4 ). However, GHG emissions from reservoirs are poorly quantified, especially in temperate systems, resulting in high uncertainty. We compared reservoir C emission estimates and uncertainty of diffusive, ebullitive, and degassing pathways in six hydropower reservoirs in the southeastern United States among four data sources: two field-based surveys and two models (including the GHG Reservoir “G-res” Tool). We found that CH 4 diffusion was most similar across data sources (modeled minus observed, bias = - 21 g CO 2-eq m -2 y -1 ) and had low relative uncertainty (coefficient of variation, CV = 0.98). On the other hand, CO 2 diffusion was least consistent across data sources (bias = - 518 g CO 2-eq m -2 y -1 ). Both field surveys indicated strong negative CO 2 diffusion (i.e., CO 2 uptake) at all reservoirs, while G-res estimated positive CO 2 diffusion. By extension, total C emissions showed similar discrepancies, leading to high uncertainty in upscaling and interpreting reservoir source-sink dynamics. Finally, CH 4 ebullition had the highest relative uncertainty (CV = 2.77) due to high variability across sites. We discuss limitations of field surveys and these models, including temperature-based annualization methods, varying definitions of ebullition zones, low sampling resolution, and lack of dynamism. Future field efforts focused on capturing variability in CO 2 diffusion and CH 4 ebullition will be especially valuable in reducing uncertainty and improving models to advance our understanding reservoir GHG emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2020

Critical land change information enhances the understanding of carbon balance in the United States

Large‐scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine‐scale processes including land use and land cover change into a large‐scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1‐km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

carbon sequestration↗

DOE OSTI2021

Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches

Focal Area(s): This white paper addresses how explainable machine learning (ML) algorithms can improve insights gained from complex data (Focal Area 3). We will also address how ML approaches related to sensor compression and low energy AI hardware can be used for efficient data acquisition (Focal Area 1). Science Challenge: We will focus on the coupled water cycle and carbon cycle processes in terrestrial ecosystems and terrestrial-aquatic interfaces. Thus, our approaches will be centered around several data-model integration challenges indicated in EESD strategic plans for two science focus areas: Terrestrial Ecosystem Science and Hydrobiogeochemistry. We can use AI to correct systematic model errors due to biases in either data or model structure/processes. Error patterns in model predictions usually vary by region, model, season etc. But there are systematic patterns in them. e.g., soil moisture tends to be overestimated in the arid western continental United States underestimated in wetter eastern USA; some land surface models tend to underestimate moisture in wet seasons and overestimate in dry seasons. Moreover, the consequences of extreme events (e.g., drought, extreme flooding, storm surges associated with tropical storms, hurricanes) on carbon cycle processes are not well represented in ecosystem and Earth system models. Redox-sensitive processes (e.g., rapid oxidation/reduction of iron and other redox-sensitive elements in soil microsites subjected to fluctuated hydrology) in terrestrial-aquatic interfaces further challenge model predictions of hot-spots (and hot-moments) due to poor understanding of underlying mechanisms. Thus, AI/ML approaches can be used to learn patterns in the data and model errors and use them to build model equations and correct process-based model errors.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗

NASA NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗

DOE OSTI2022

Contribution of Tibetan Plateau ecosystems to local and remote precipitation through moisture recycling

The ecosystems of the Tibetan Plateau (TP) provide multiple important ecosystem services that benefit both local populations and those beyond, such as through climate regulation services on precipitation for East Asia and China. However, the precipitation regulation service of the TP ecosystems for supplying moisture and maintaining precipitation is yet to be evaluated. In this study, we used the moisture recycling framework and a moisture tracking model to quantify the precipitation regulation services of TP ecosystems for their contribution to precipitation. We found TP ecosystems contributed substantially to local and downwind precipitation, with a declined contribution from 221 mm/year for the TP and neighboring areas, to <10 mm/year for eastern China and other surrounding countries. Among ecosystem types, grassland contributed most to precipitation, followed by barren and snow lands, forests, and shrublands. In terms of seasonality, precipitation contribution from TP ecosystems was greater in summer months than in non-summer months for western China, while the opposite was true for eastern China – though the magnitude was much smaller. Over the past two decades, the significant evapotranspiration increases in TP translated to a widespread increase in precipitation contribution for TP and downwind beneficiary regions from 2000 to 2020. Our study provides a quantitative way to understand the precipitation regulation services of TP ecosystems through moisture recycling, substantiating their key role to safeguard precipitation and the water cycle for downwind regions– effectively acting as an ecological security shelter that could be perceived by the public.

Li, Yan↗

DOE OSTI2024

How Earth System Models Can Inform Key Dimensions of Marine Food Security in the Alaskan Arctic

The Arctic is home to several groups of Indigenous Peoples, each with distinct ways of interacting with their environment and ways of life. Arctic, Indigenous Peoples’ food sovereignty is tightly linked with food security. Subsistence harvesting activities provide nutritious and culturally vital foods for Alaska Native households and communities. Climate change is causing rapid and more unpredictable shifts in environmental conditions that impact three of the key aspects of food security, availability, stability, and accessibility. While communities monitor the abundance and health of food webs and environments as part of subsistence harvest practices, anticipating major transformations and changes in these systems is challenging. We explored the potential of Earth System Model output in helping anticipate or project physical or ecosystem changes relevant to Alaska Indigenous peoples’ food security needs. Through examples of model products, that provide measures of accessibility and availability of marine resources, we show that modern models, such as the Energy Exascale Earth System Model presented here, can provide estimates of a broad suite of variables relevant to food security. We investigate how Earth System Model output could contribute to exploring questions related to aspects of Arctic food security such as accessibility and availability and highlight present model shortcomings that, if addressed, would move Earth System Models closer to being a useful tool for understanding environmentally driven changes to the availability and accessibility of harvestable food resources. Our example model-derived food security indicators illustrate how Earth System Model output could be combined with relevant, non-model, information sources; These model products are meant only as a starting point and a tool for engaging community members and to present, in an accessible way, the model’s potential utility, or current lack thereof, to rights holders and stakeholders concerned about food security. We are hopeful that with example products in hand, additional model development efforts will have a higher likelihood of success in achieving an iterative discussion with stakeholders regarding feasible and desired products.

Gibson, Georgina A.↗

DOE OSTI2022

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

58 GEOSCIENCES↗

DOE OSTI2021

Spatio-temporal differences in leaf physiology are associated with fire, not drought, in a clonally integrated shrub

Abstract In highly disturbed environments, clonality facilitates plant survival via resprouting after disturbance, resource sharing among interconnected stems and vegetative reproduction. These traits likely contribute to the encroachment of deep-rooted clonal shrubs in tallgrass prairie. Clonal shrubs have access to deep soil water and are typically thought of as relatively insensitive to environmental variability. However, how leaf physiological traits differ among stems within individual clonal shrubs (hereafter ‘intra-clonal’) in response to extreme environmental variation (i.e. drought or fire) is unclear. Accounting for intra-clonal differences among stems in response to disturbance is needed to more accurately parameterize models that predict the effects of shrub encroachment on ecosystem processes. We assessed intra-clonal leaf-level physiology of the most dominant encroaching shrub in Kansas tallgrass prairie, Cornus drummondii, in response to precipitation and fire. We compared leaf gas exchange rates from the periphery to centre within shrub clones during a wet (2015) and extremely dry (2018) year. We also compared leaf physiology between recently burned shrubs (resprouts) with unburned shrubs in 2018. Resprouts had higher gas exchange rates and leaf nitrogen content than unburned shrubs, suggesting increased rates of carbon gain can contribute to recovery after fire. In areas recently burned, resprouts had higher gas exchange rates in the centre of the shrub than the periphery. In unburned areas, leaf physiology remained constant across the growing season within clonal shrubs (2015 and 2018). Results suggest single measurements within a shrub are likely sufficient to parameterize models to understand the effects of shrub encroachment on ecosystem carbon and water cycles, but model parameterization may require additional complexity in the context of fire.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multiphysics Simulation of KRUSTY Warm Critical Experiments Using MOOSE Tools

A series of critical experiments were performed during the Kilopower Reactor Using Stirling TechnologY (KRUSTY) project, producing valuable and unique data for the validation of corresponding microreactor models. In this paper, the development of a high-fidelity multiphysics model of the KRUSTY microreactor within the MOOSE ecosystem is presented, with a focus on the microreactor configuration during the warm critical experiments. The model was established by coupling a deterministic neutronics model using the Griffin code, with a BISON thermomechanical model. The multiphysics model successfully simulated the KRUSTY 15 ¢ and 30 ¢ warm critical reactivity insertion experiments in satisfactory agreement with the experimental measurement data. In conclusion, the modeling results demonstrated the performance of MOOSE-based multiphysics tools in microreactor simulations, and spotlighted the useful applications of these tools in supporting microreactor design, qualification, and licensing activities.

KRUSTY↗

NASA NTRS1999

The Shuttle Radar Topography Mission: A Global DEM

Digital topographic data are critical for a variety of civilian, commercial, and military applications. Scientists use Digital Elevation Models (DEM) to map drainage patterns and ecosystems, and to monitor land surface changes over time.

Digital↗

DOE OSTI2023

A Processing and Analytics System for Microscopy Data Workflows: The Pycroscopy Ecosystem of Packages

Major advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopy techniques. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of the acquired datasets. Aside from lack of file standards, individual domain-specific analysis packages are often disjoint from the underlying datasets, and thus keeping track of analysis and processing steps remains tedious for the end-user, hampering reproducibility. Here, in this study, the pycroscopy ecosystem of packages is introduced, an open-source python-based ecosystem underpinned by a common data model. The data model, termed the N-dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes, but expanding them to accelerate microscopy relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis of scanning transmission electron microscopy (STEM) and scanning probe microscopy data are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta-data storage will be critical to overall experimental operations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2023

A Hyperspectral Inversion Framework for Estimating Absorbing Inherent Optical Properties and Biogeochemical Parameters in Inland and Coastal Waters

The simultaneous remote estimation of biogeochemical parameters (BPs) and inherent optical properties (IOPs) from hyperspectral satellite imagery of globally distributed optically distinct inland and coastal waters is a complex, unsolved, non-unique inverse problem. To tackle this problem, we leverage a machine-learning model termed Mixture Density Networks (MDNs). MDNs outperform operational algorithms by calculating the covariance between the simultaneously estimated products. We train the MDNs on a large ( N = 8237) dataset of co-aligned, in situ measured, hyperspectral remote sensing reflectance (R rs ), BPs, and absorbing IOPs from globally representative optically distinct inland and coastal waters. The estimated IOPs include absorption due to phytoplankton (a ph ), chromophoric dissolved organic matter (a cdom ), and non-algal particles (a nap ). The estimated BPs include chlorophyll-a, total suspended solids, and phycocyanin (PC). MDNs dramatically reduce uncertainty in the retrievals, relative to operational algorithms, when using a 50/50 dataset split, where the MDNs are trained on a randomly selected half of the in situ dataset and validated on the other half. Our model is shown to have higher, or equivalent, generalization performance than the calculated operational algorithms available for all BPs and IOPs (except PC) via a leave-one-out cross-validation assessment. The MDNs are sensitive to uncertainties in the hyperspectral satellite R rs , resulting from instrument noise and atmospheric correction; there is a difference of ~37.4–62.8% (using median symmetric accuracy) between the MDNs’ estimates derived from co-located satellite-derived R rs and in situ R rs . Of the IOPs, a cdom and a nap are less sensitive to uncertainties in hyperspectral satellite imagery relative to a ph , with remote estimates of a ph exhibiting incorrect spectral shape and magnitude relative to in situ measured IOPs. Despite the uncertainties in satellite derived R rs , the spatial distributions of BPs and IOPs in MDN-derived product maps of Lake Erie and the Curonian Lagoon, based on imagery taken with the Hyperspectral Imager for the Coastal Ocean (HICO) and PRecursore Iper-Spettrale della Missione Applicativa (PRISMA), are confirmed via co-aligned in situ measurements and agree with the literature’s understanding of these well-studied regions. The consistency and accuracy of the model on HICO and PRISMA imagery, despite radiometric uncertainties, demonstrate its applicability to future hyperspectral missions, such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, where the simultaneous estimation model will serve as a key part of phytoplankton community composition analysis.

Ryan E. O'Shea↗

DOE OSTI2023

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

Introduction: Forest landscapes offer resources and ecosystem services that are vital to the social, economic, and cultural well-being of human communities, but managing for these provisions can require socially and ecologically relevant trade-offs. We designed a spatial decision support model to reveal trade-offs and synergies between ecosystem services in a large eastern Cascade Mountain landscape in Washington State, USA. Methods: We used process-based forest landscape (LANDIS-II) and hydrology (DHSVM) models to compare outcomes associated with 100 years of simulated forest and wildfire dynamics for two management scenarios, Wildfire only and Wildfire + Treatments. We then examined the strength and spatial distribution of potential treatment effects and trends in a set of resources and ecosystem services over the simulation period. Results: We found that wildfire area burned increased over time, but some impacts could be mitigated by adaptation treatments. Treatment benefits were not limited to treated areas. Interestingly, we observed neighborhood benefits where fire spread and severity were reduced not only in treated patches but in adjacent patches and landscapes as well, creating potential synergies among some resource benefits and services. Ordinations provided further evidence for two main kinds of outcomes. Positive ecological effects of treatments were greatest in upper elevation moist and cold forests, while positive benefits to human communities were aligned with drier, low- and mid-elevation forests closer to the wildland urban interface. Discussion: Our results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests and can be used to inform management aimed at rebuilding resilient, climate-adapted landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Modeling methane dynamics in three wetlands in Northeastern China by using the CLM-Microbe model

Wetlands account for up to 70% of the natural source of methane (CH 4 ) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH 4 in wetlands. Therefore, simulating microbial mechanisms of CH 4 production and consumptionwould improve the predictability of CH 4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH 4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH 4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH 4 flux across sites. Marsh wetlands had higher CH 4 flux than mountain peatlands. Ebullition dominated the CH 4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH 4 flux, making all wetlands a CH 4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH 4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH 4 dynamics under the changing environment.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2005

Comparison of Heat and Moisture Fluxes from a Modified Soil-plant-atmosphere Model with Observations from BOREAS

This study evaluates the prediction of heat and moisture fluxes from a new land surface scheme with eddy correlation data collected at the old aspen site during the Boreal Ecosystem-Atmosphere Study (BOREAS) in 1994. The model used in this study couples a multilayer vegetation model with a soil model. Inclusion of organic material in the upper soil layer is required to adequately simulate exchange between the soil and subcanopy air. Comparisons between the model and observations are discussed to reveal model misrepresentation of some aspects of the diurnal variation of subcanopy processes. Evapotranspiration

Lee, Young-Hee↗

DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗