Analyzing large biological datasets with association networks
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It has been an ongoing scientific debate whether biological parameters are conserved across experimental setups with different media, pH values, and other experimental conditions. Our work explores this question using Bayesian probability as a rigorous framework to assess the biological context of parameters in a model of the cell growth controller in You et al. When this growth controller is uninduced, the E. coli cell population grows to carrying capacity; however, when the circuit is induced, the cell population growth is regulated to remain well below carrying capacity. This growth control controller regulates the E. coli cell population by cell to cell communication using the signaling molecule AHL and by cell death using the bacterial toxin CcdB. To evaluate the context dependence of parameters such as the cell growth rate, the carrying capacity, the AHL degradation rate, the leakiness of AHL, the leakiness of toxin CcdB, and the IPTG induction factor, we collect experimental data from the growth control circuit in two different media, at two different pH values, and with several induction levels. We define a set of possible context dependencies that describe how these parameters may differ with the experimental conditions and we develop mathematical models of the growth controller across the different experimental contexts. We then determine whether these parameters are shared across experimental contexts or whether they are context dependent. For each of these possible context dependencies, we use Bayesian inference to assess its plausibility and to estimate the parameters of the growth controller. Ultimately, we find that there is significant experimental context dependence in this circuit. Furthermore, we also find that the estimated parameter values are sensitive to our assumption of a context relationship.
Upon sensing developmental or environmental cues, epigenetic regulators transform the chromatin landscape of a network of genes to modulate their expression and dictate adequate cellular and organismal responses. Knowledge of the specific biological processes and genomic loci controlled by each epigenetic regulator will greatly advance our understanding of epigenetic regulation in plants. To facilitate hypothesis generation and testing in this domain, we present EpiNet, an extensive gene regulatory network (GRN) featuring epigenetic regulators. EpiNet was enabled by (i) curated knowledge of epigenetic regulators involved in DNA methylation, histone modification, chromatin remodeling, and siRNA pathways; and (ii) a machine-learning network inference approach powered by a wealth of public transcriptome datasets. We applied GENIE3, a machine-learning network inference approach, to mine public Arabidopsis transcriptomes and construct tissue-specific GRNs with both epigenetic regulators and transcription factors as predictors. The resultant GRNs, named EpiNet, can now be intersected with individual transcriptomic studies on biological processes of interest to identify the most influential epigenetic regulators, as well as predicted gene targets of the epigenetic regulators. We demonstrate the validity of this approach using case studies of shoot and root apical meristem development.
Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multicellular organisms find themselves shapes the way they interact, respond to the environment and process external information. Since the birth of the field, synthetic biologists have had to grapple with contextual issues, particularly when the molecular and genetic devices inexplicably fail to function as designed when tested in vivo. In this review, we set out to identify and classify the sources of the unexpected divergences between design and actual function of synthetic systems and analyze possible methodologies aimed at controlling, if not preventing, unwanted contextual issues.
Abstract not provided
We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Here, compared to homogenous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3,974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly-solvated protein has been treated at a quantum-based MD level of detail.
Hydrogen sulfide is an extremely toxic gas that is also of great interest for biological applications when delivered in the correct amount and at the desired rate. Here we show that the highly porous metal-organic frameworks with the CPO-27 structure can bind the hydrogen sulfide relatively strongly, allowing the storage of the gas for at least several months. Delivered gas is biologically active in preliminary vasodilation studies of porcine arteries, and the structure of the hydrogen sulfide molecules inside the framework has been elucidated using a combination of powder X-ray diffraction and pair distribution function analysis.
Recent advances in the chemical design and synthesis of fluorophores in the second near-infrared biological window (NIR-II) for multimodal imaging and theranostics are summarized and highlighted in this review article.
Most biological nitrogen fixation (BNF) results from the activity of the molybdenum nitrogenase (Mo-nitrogenase, Nif), an oxygen-sensitive metalloenzyme complex found in all known diazotrophs.
Abstract The moss Physcomitrium (previously Physcomitrella) patens is a non-vascular plant belonging to the bryophytes that has been used as a model species to study the evolution of plant cell wall structure and biosynthesis. Here, we present an updated review of the cell wall biology of P. patens. Immunocytochemical and structural studies have shown that the cell walls of P. patens mainly contain cellulose, hemicelluloses (xyloglucan, xylan, glucomannan, and arabinoglucan), pectin, and glycoproteins, and their abundance varies among different cell types and at different plant developmental stages. Genetic and biochemical analyses have revealed that a number of genes involved in cell wall biosynthesis are functionally conserved between P. patens and vascular plants, indicating that the common ancestor of mosses and vascular plants had already acquired most of the biosynthetic machinery to make various cell wall polymers. Although P. patens does not synthesize lignin, homologs of the phenylpropanoid biosynthetic pathway genes exist in P. patens and they play an essential role in the production of caffeate derivatives for cuticle formation. Further genetic and biochemical dissection of cell wall biosynthetic genes in P. patens promises to provide additional insights into the evolutionary history of plant cell wall structure and biosynthesis.
Anomalous diffraction signals from typical native macromolecules are very weak, frustrating their use in structure determination. Here, native SAD procedures are described for enhancing the signal to noise in anomalous diffraction by using multiple crystals are described. Five applications demonstrate that truly routine structure determination is possible without the need for heavy atoms. Structure determinations for biological macromolecules that have no known structural antecedents typically involve the incorporation of heavier atoms than those found natively in biological molecules. Currently, selenomethionyl proteins analyzed using single- or multi-wavelength anomalous diffraction (SAD or MAD) data predominate for such de novo analyses. Naturally occurring metal ions such as zinc or iron often suffice in MAD or SAD experiments, and sulfur SAD has been an option since it was first demonstrated using crambin 30 years ago; however, SAD analyses of structures containing only light atoms (Z{sub max} ≤ 20) have not been common. Here, robust procedures for enhancing the signal to noise in measurements of anomalous diffraction by combining data collected from several crystals at a lower than usual X-ray energy are described. This multi-crystal native SAD method was applied in five structure determinations, using between five and 13 crystals to determine substructures of between four and 52 anomalous scatterers (Z ≤ 20) and then the full structures ranging from 127 to 1200 ordered residues per asymmetric unit at resolutions from 2.3 to 2.8 Å. Tests were devised to assure that all of the crystals used were statistically equivalent. Elemental identities for Ca, Cl, S, P and Mg were proven by f′′ scattering-factor refinements. The procedures are robust, indicating that truly routine structure determination of typical native macromolecules is realised. Synchrotron beamlines that are optimized for low-energy X-ray diffraction measurements will facilitate such direct structural analysis.
Highly-optimized parallel molecular dynamics programs have allowed researchers to achieve ground-breaking results in biological and materials sciences. This type of performance has come at the expense of portability: a significant effort is required for performance optimization on each new architecture. Using a metric that emphasizes speedup, we assess key accelerating programming components of four different best-performing molecular dynamics programs - GROMACS, NAMD, LAMMPS and CP2K-- each having a particular scope of application, for contribution to performance and for portability. We use builds with and without these components, tested on HPC systems. We also analyze the code-bases to determine compliance with portability recommendations. We find that for all four programs, the contributions of the non-portable components to speed are essential to the programs' performances; without them we see a reduction in time-to-solution of a magnitude that is insufferable to domain scientists. This characterizes the performance efficiency that must be approached for good performance portability on a programmatic level, suggesting solutions to this difficult problem, which should come from developers, industry and funding institutions, and possibly new research in programming languages.
Abstract This commentary summarizes the publication history of Global Change Biology for works on experimental manipulations over the past 25 years and highlights a number of key publications. The retrospective summary is then followed by some thoughts on the future of experimental work as it relates to mechanistic understanding and methodological needs. Experiments for elevated CO 2 atmospheres and anticipated warming scenarios which take us beyond historical analogs are suggested as future priorities. Disturbance is also highlighted as a key agent of global change. Because experiments are demanding of both personnel effort and limited fiscal resources, the allocation of experimental investments across Earth's biomes should be done in ecosystems of key importance. Uncertainty analysis and broad community consultation should be used to identify research questions and target biomes that will yield substantial gains in predictive confidence and societal relevance. A full range of methodological approaches covering small to large spatial scales will continue to be justified as a source of mechanistic understanding. Nevertheless, experiments operating at larger spatial scales encompassing organismal, edaphic, and environmental diversity of target ecosystems are favored, as they allow for the assessment of long‐term biogeochemical feedbacks enabling a full range of questions to be addressed. Such studies must also include adequate investment in measurements of key interacting variables (e.g., water and nutrient availability and budgets) to enable mechanistic understanding of responses and to interpret context dependency. Integration of ecosystem‐scale manipulations with focused process‐based manipulations, networks, and large‐scale observations will aid more complete understanding of ecosystem responses, context dependence, and the extrapolation of results. From the outset, these studies must be informed by and integrated with ecosystem models that provide quantitative predictions from their embedded mechanistic hypotheses. A true two‐way interaction between experiments and models will simultaneously increase the rate and robustness of Global Change research.
Understanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOE began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.
Space biology, surveying basic stress biology
The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.