Pushing the Boundaries of Space Biology: Computation Modeling and Systems Biology
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Afshin Beheshti↗
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Results for “biological modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Electromechanical memcapacitor model offering biologically plausible spiking.
In this article, we introduce a new nanoscale electromechanical device - a leaky memcapacitor - and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movableplate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several types of spiking observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive, leaking resistive, and reset functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.
Zhang, Zixi↗
Following O and OH in He/O 2 and He/H 2 O gas mixtures—from the gas phase through the liquid phase to modifications on a biological sample
Abstract Applied cold atmospheric plasma allows for the controlled delivery of reactive oxygen and nitrogen species tailored for specific applications. Through the manipulation of the plasma parameters, feed gases, and careful consideration of the environment surrounding the treatment target, selective chemistries that preferentially influence the target can be produced and delivered. To demonstrate this, the COST reference microscale atmospheric pressure plasma jet is used to study the generation and transport of O and ⋅ OH from the gas phase through the liquid to the biological model target cysteine. Relative and absolute species densities of ⋅ OH and O are measured in the gas phase through laser induced fluorescence (LIF) and two-photon absorption LIF respectively. The transport of these species is followed into the liquid phase by hydrogen peroxide quantification and visualized by a fluorescence assay. Modifications to the model biological sample cysteine exposed to ⋅ OH and H 2 O 2 dominated chemistry (He/H 2 O (0.25%)) and O dominated chemistry (He/O 2 (0.6%)) is measured by FTIR spectroscopy. The origin of these species that modify cysteine is considered through the use of heavy water (H 2 18 O) and mass spectrometry. It is found that the reaction pathways differ significantly for He/O 2 and He/H 2 O. Hydrogen peroxide is formed mainly in the liquid phase in the presence of a substrate for He/O 2 whereas for He/H 2 O it forms in the gas phase. The liquid chemistry resulting from the He/O 2 admixture mainly targets the sulfur moiety of cysteine for oxidation up to irreversible oxidation states, while He/H 2 O treatment leads preferentially to reversible oxidation products. The more O or OH/H 2 O 2 dominated chemistry produced by the two gas admixtures studied offers the possibility to select species for target modification.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A model for the biological precipitation of Precambrian iron-formation
A biological model for the precipitation of Precambrian iron formations is presented. Assuming an oxygen deficient atmosphere and water column to allow sufficient Fe solubility, it is proposed that local oxidizing environments, produced biologically, led to precipitation of iron formations. It is further suggested that spheroidal structures about 30 mm in diameter, which are widespread in low grade cherty rion formations, are relict forms of the organic walled microfossil Eosphaera tylerii. The presence of these structures suggests that the organism may have had a siliceous test, which allowed sufficient rigidity for accumulation and preservation. The model involves precipitation of ferric hydrates by oxidation of iron in the photic zone by a variety of photosynthetic organisms. Silica may have formed in the frustules of silica secreting organisms, including Eosphaera tylerii. Iron formates formed, therefore, by a sediment rain of biologically produced ferric hydrates and silica and other organic material. Siderite and hematite formed diagenetically on basin floors, and subsequent metamorphism produced magnetite and iron silicates.
Laberge, G. L.↗
Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response
Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.
systems biology, hypergraph, viral infection, biol↗
Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development
Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.
Adrienne Hoarfrost↗
Biological Response Models: Predicting Injury and Mortality of Fish During Downstream Passage through Hydropower Facilities
This report describes the U.S. Department of Energy’s HydroPASSAGE project efforts to develop and collect biological response models for integration into the Biological Performance Assessment (BioPA) toolset and the Hydropower Biological Evaluation Toolset (HBET). These models help understand how fish are likely to respond during dam passage when exposed to hydraulic and physical stressors associated with turbines and other hydropower structures. When fish pass through hydropower facilities, they may encounter several stressors, of which the three most common are collision, rapid decompression, and fluid shear. Specialized equipment has been used to develop 99 biological response models for exposure to blade strike, rapid decompression, or fluid shear. The models were gathered from the literature or developed as part of this effort and include models for 31 different species of fish that have various predicted endpoints (i.e., injury or mortality). Among these models, considerable variation in susceptibility to the stressors has been observed from one species to another, and a species’ susceptibility to one stressor does not necessarily indicate similar susceptibility to another. Although several species have been examined, it is still unclear how many other species, which may have different morphological traits, may respond to these stressors, so further examination of the different species is needed. These models can and have been applied in several different cases, often using the BioPA toolset and HBET, to better understand the potential for injury and mortality that may occur during fish passage at hydropower facilities, including specific applications, such as turbine replacement, the installation of new turbines, or changing operations of currently installed turbines. As hydropower is continually developed to meet the electricity needs of society, tools such as HBET and BioPA, used with the integrated biological response models, will aid in the development of technologies and strategies that avoid, minimize, mitigate, or manage environmental effects.
13 HYDRO ENERGY↗
Incorporating biologically based models into assessments of risk from chemical contaminants
The general approach to assessment of risk from chemical contaminants in drinking water involves three steps: hazard identification, exposure assessment, and dose-response assessment. Traditionally, the risks to humans associated with different levels of a chemical have been derived from the toxic responses observed in animals. It is becoming increasingly clear, however, that further information is needed if risks to humans are to be assessed accurately. Biologically based models help clarify the dose-response relationship and reduce uncertainty.
Review, Tutorial↗
Completing and Adapting Models of Biological Processes
We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.
Margaria, Tiziana↗
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit
Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.
59 BASIC BIOLOGICAL SCIENCES↗
Divergent carbon use efficiency-growth rate tradeoff in popular biological growth models
Carbon use efficiency (CUE) is an important trait emerging from processes regulating biological growth. CUE can be computed either based on the growth of structural biomass or total biomass divided by substrate uptake rate. Nonequilibrium thermodynamics and observations suggest that, for an exponentially growing population of cells, structural biomass CUE should first increase, then peak, and finally decrease with specific growth rate; meanwhile, total biomass CUE increases asymptotically with specific growth rate. We compared predictions from six popular models that are often used for plant and microbial growth in existing ecosystem models. We found that, for an exponentially growing population of biological cells, (1) the source-driven Pirt and Compromise models predict that structural biomass CUE increase asymptotically with growth rate; (2) the apparent sink-driven modified Droop model predicts that structural biomass CUE decreases with growth rate; and (3) the sink-driven variable internal storage model and two dynamic energy budget models predict that structural biomass CUE first increases, then peaks, and finally decreases with growth rate. Moreover, the modified Droop model predicts that total biomass CUE is constant with growth rate, while all other five models predict that total biomass CUE increases with growth rate asymptotically. For non-exponential biological growth, we show that there is no static relationship between total biomass CUE or structural biomass CUE with respect to either growth rate or temperature. Therefore, we contend that biological growth models should explicitly represent interactions between substrate acquisition, substate transformation, and maintenance respiration to better capture observed CUE dynamics, and the sink-driven model should be preferred for general ecosystem biogeochemistry modeling.
Tang, Jinyun [Lawrence Berkeley National Laborator↗
Error analysis of leaf area estimates made from allometric regression models
Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).
Feiveson, A. H.↗
Predicting compatibility between ferredoxins and the Fe protein of nitrogenase using in silico protein modeling
Biological nitrogen fixation is the process by which certain bacteria and archaea use the enzyme nitrogenase to reduce atmospheric nitrogen into bioavailable ammonium. Engineering non‐nitrogen‐fixing organisms, like plants, to use nitrogenase could reduce dependency on synthetic fertilizer and mitigate the environmental impacts of industrial fertilizer production. However, nitrogenase activity requires delivery of reducing power by small electron carrying proteins known as ferredoxins and flavodoxins, and successfully engineering nitrogenase into new systems will require a mechanistic understanding of electron delivery by these proteins. Most organisms often have multiple ferredoxins, raising the question of which ferredoxin can support nitrogenase activity. The purpose of this study is to gain insight into how we can predict which ferredoxin is compatible with the Fe protein, the component of nitrogenase that interacts with ferredoxin or flavodoxin. Our in silico protein–protein docking simulations reveal that most ferredoxins and flavodoxins involved in nitrogen fixation have the shortest distance (≤10 Å) between their redox cofactor and the [4Fe‐4S] cluster of the Fe protein. We found shorter cofactor distance contributes to faster intermolecular electron tunneling rates. Bacterial ferredoxins that play a role in nitrogen fixation also exhibit more complementary interactions with the Fe protein than bacterial and plant ferredoxins not involved in this process. Heterologous expression of a set of ferredoxins from both nitrogen‐fixing and non‐nitrogen‐fixing bacteria in the diazotroph Rhodopseudomonas palustris supports our model‐derived prediction that shorter distances between the electron‐carrying cofactors favor nitrogenase compatibility. These findings offer a framework to predict and potentially enhance ferredoxin–nitrogenase compatibility, which will help to improve our ability to engineer nitrogen fixation into non‐nitrogen‐fixing organisms like plants.
59 BASIC BIOLOGICAL SCIENCES↗
An efficient approach to ARMA modeling of biological systems with multiple inputs and delays
This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.
Non-NASA Center↗
Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems
It is hard to identify nonlinear biological models strictly from data, with results that are often sensitive to experimental conditions. Automated experimental workflows and liquid handling enables unprecedented throughput, as well as the capacity to generate extremely large datasets. We seek to develop generalized identifiability conditions for informing the design of automated experiments to discover predictive nonlinear biological models. For linear systems, identifiability is characterized by persistence of excitation conditions. For nonlinear systems, no such persistence of excitation conditions exist. We use the input-Koopman operator method to model nonlinear systems and derive identifiability conditions for openloop systems initialized from a single initial condition. We show that nonlinear identifiability is intrinsically tied to the rank of a given dataset’s power spectral density, transformed through the lifted Koopman observable space. We illustrate these identifiability conditions with a simulated synthetic gene circuit model, the repressilator. We illustrate how rank degeneracy in datasets results in overfitted nonlinear models of the repressilator, resulting in poor predictive accuracy. Our findings provide novel experimental design criteria for discovery of globally predictive nonlinear models of biological phenomena.
Boddupalli, Nibodh↗
Modeling of Biological Fluid Systems
This study presents a detailed methodology for modeling and validating three- dimensional fluid flow in realistic reconstructed vasculature geometry. Optical scanned data from a sectioned stenotic lumen having -65% area constriction within an arterial vessel of 9 mm diameter is processed and converted into a finite-element mesh. Mass and momentum transfer equations within the reconstructed stenotic vessel with a-flow rate of 200 ml/rnin is solved using the Galarkin Finite Element formulation. The calculated velocities at several axial locations along the length of the vessel are compared with the Particle Image Velocimetry (PIV) measurements. The computation results are in agreement with the initial steady state PIV measurements. In addition, initial results from pulsatile flow computations at different time points of a pulse cycle will be presented.
Banerjee, Rupak↗
Cellular track model of biological damage to mammalian cell cultures from galactic cosmic rays
The assessment of biological damage from the galactic cosmic rays (GCR) is a current interest for exploratory class space missions where the highly ionizing, high-energy, high-charge ions (HZE) particles are the major concern. The relative biological effectiveness (RBE) values determined by ground-based experiments with HZE particles are well described by a parametric track theory of cell inactivation. Using the track model and a deterministic GCR transport code, the biological damage to mammalian cell cultures is considered for 1 year in free space at solar minimum for typical spacecraft shielding. Included are the effects of projectile and target fragmentation. The RBE values for the GCR spectrum which are fluence-dependent in the track model are found to be more severe than the quality factors identified by the International Commission on Radiological Protection publication 26 and seem to obey a simple scaling law with the duration period in free space.
Cucinotta, Francis A.↗
Biological indicators for monitoring water quality of MTF canals system
Biological models, diversity indexes, were developed to predict environmental effects of NASA's Mississippi test facility (MTF) chemical operations on canal systems in the area. To predict the effects on local streams, a physical model of unpolluted streams was established. The model is fed by artesian well water free of background levels of pollutants. The species diversity and biota composition of unpolluted MTF stream was determined; resulting information will be used to form baseline data for future comparisons. Biological modeling was accomplished by adding controlled quantities or kinds of chemical pollutants and evaluating the effects of these chemicals on the biological life of the stream.
Sethi, S. L.↗