PNNL-13501 - Pacific Northwest National Laboratory

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The resulting software that will be developed will run on modern, high-performance, massively parallel computer systems such as those available in the Molecular Science Computing Facility in EMSL. Publications and Presentations Jones-Oliveira JB, JS Oliveira, GK Schenter, and HE Trease. “Mathematical modeling of molecular surfaces using unstructured mesh techniques.” (to be submitted). Jones-Oliveira JB, JS Oliveira, HE Trease, and LL Trease. September 2000. “Hybrid grid generation 116 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report using NWGRID.” 7 th International Conference on Numerical Grid Generation in Computational Field Simulations. Jones-Oliveira JB, JS Oliveira, HE Trease, and P Eiseman. September 2000. “Automated geometry blocking using an adaptive mesh refinement algorithm.” 7 th International Conference on Numerical Grid Generation in Computational Field Simulations. Oliveira JS, HE Trease, JD Fowler. September 2000. “A symmetry invariant first-order remapping methodology.” 7 th International Conference on Numerical Grid Generation in Computational Field Simulations.
Development of Models of Cell-Signaling Pathways and Networks Study Control Number: PN99020/1348 Joseph S. Oliveira, John H. Miller, William R. Cannon, David A. Dixon This work focuses on development of the fundamental mathematics and model-building capabilities for a problemsolving environment called the Virtual Cell. This research environment will culminate in the ability to model multiscale, complex, whole-cellular processes with predictive capability for environmental signal recognition and response. The cellsignaling model of the virtual cell provides us with a computational test bed that will enable an experimentalist to test cellular processes computationally. This methodology will greatly enhance our capability to effectively understand and control fundamental biological processes from a bioengineering systems point of view. Project Description Coupling theory and experiment to understand complex cell-signaling networks requires a sophisticated appliedmathematical/computational-modeling capability to integrate back into the context of a functioning cell. In addition, acquired knowledge is needed on signaling kinetics, pathway cross-talk, protein structure/function and interactions, and metabolism coupled to other peripheral controlling and self-modifying phenomena. The goal of this work is to develop the fundamental mathematics and model-building capabilities for a problem-solving environment called the Virtual Cell. Three critical cell-signaling research issues have been identified and validated by a variety of means. These include 1) the need to analyze and understand the subcellular components involved in cell signaling, 2) the need to study cell signaling in vivo, and 3) the need to synthesize the experimental knowledge and acquired understanding into an integrated cellular context by the development of models and computation. These three research issues will also be used to further the development of a suite of computational biophysics problem-solving tools that links the multiscale biophysics of cytoplasmic diffusive reactive molecular hydrotransport to the emergence of both perturbative and nonperturbative cell-signaling pathways. The future goal is the construction of a computational biophysical systems engineering test bed that will enable us to explore the dynamic structure and function of cell complexes that specialize into tissue assemblies and finally organ transport systems. Approach Databases A goal of this work is to continue to collect the available experimental and model data on cell signaling, in order to develop models. These data will be used to develop models for some critical cell-signaling pathways, based on an operations systems approach. Petri Nets The complex operational processes associated with cell signaling—communications—lend themselves to being faithfully modeled by Petri nets. A Petri net is a directed, simply connected graph, composed of nodes (vertices) and edges. Every Petri net has two types of nodes: state nodes (S) and transition nodes (T). State nodes hold information called tokens. Transition nodes define a set of conditions that regulate the flow of information from one state node to another; e.g., information from state S1 is transferred to state S2 when a set of transition conditions T1 are satisfied. Petri nets represent a rich mathematical framework for modeling dynamically complex operational process control systems. Every network ensemble provides us with a number of discrete data abstractions for representing an object and its attributes, together with a set of constraints that determines the regulation of complex operational processes. An incidence (connectivity) matrix defines a framework for Computational Science and Engineering 117
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Laboratory Directed Research and De
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Laboratory Directed Research and De
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Computational Science and Engineeri
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Policy and Management Sciences Asse
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Introduction Department of Energy O
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5. After reviews by the Technical a
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• The Technical Council peer revi
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methods applicable to combustion, s
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Summary Each national laboratory mu
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Study Control Number: PN0004/1411 A
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Table 1. Positive and negative ions
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m/z Arbitrary Units Figure 1. Mass
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introduce low-volatility compounds
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arrangement, but the charge is reta
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two liquid chromatographic gradient
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Integrated Microfabricated Devices
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Matrix Assisted Laser Desorption/Io
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Molecular Beam Synthesis and Charac
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temperature distribution of the des
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expected to result in significant a
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Study Control Number: PN99069/1397
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Seuss DT and KA Prather. 1999. “M
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Analysis of Receptor-Modulated Ion
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laboratory (Lu and Xie 1997; Lu et
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Automated DNA Fingerprinting Microa
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Comparison of 4 Xanthomonas Isolate
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Approach Hematopoietic (bone marrow
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Anti-Human lgG Human lgG Protein G
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Characterization of Global Gene Reg
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PCR products for immobilization on
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identify novel proteins of importan
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Immunoprecipitaton of tyrosinephosp
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Coupled NMR and Confocal Microscopy
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In the optical microscopy image, th
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Development and Applications of a M
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Figure 3. Hepa-1 cells undergoing a
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cellular processing (such as post-t
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Page 87 and 88: Fungal Conversion of Agricultural W
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Page 91 and 92: Summary and Conclusions We demonstr
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Page 170 and 171: Summary and Conclusions In previous
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Figure 4. A filtered version of Fig
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Development of Modeling Tools for J
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Figure 2. Schematic of experimental
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Integrated Modeling and Simulation
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(a) (b) (c) (d) Figure 1. Results o
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Thurman DA. August 2000. Collaborat
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MARC Input initial m esh and result
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(Johnson 1999; Colligan 1999; Gould
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Modeling of High-Velocity Forming f
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Global divergence error 1 10 -14 0
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that users were unlikely to appreci
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Earth System Science
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the lateral extent of the plume so
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Biogeochemical Processes and Microb
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Analyses of populations of viable a
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The first task was to establish fiv
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purged for 2 minutes. The gas sampl
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developing code architecture to min
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Berkowitz, CM. June 2000. “Atmosp
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environmental monitoring, 2) portab
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corresponded to a current density o
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Enhancing Emissions Pre-Processing
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Environmental Fate and Effects of D
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nitrogen and unpredictable eutrophi
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Figure 1. Ordinary kriging of 2 m d
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precipitation of calcite occurred i
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Interrelationships Between Processe
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phenanthrene resistant fraction con
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injection of an immiscible gas phas
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still some key kinetic issues that
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Application of a Crop Model The res
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The Nature, Occurrence, and Origin
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Particle number concentration, 1/cm
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geometry optimized AlOOH and FeOOH
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allowable parameters using thermody
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TCE + 3H + + 3Fe 2+ Fe)=> acetylene
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Use of Shear-Thinning Fluids for In
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concentration of 0.5% AMX was the m
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Roberts AL, LA Totten, WA Arnold, D
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tetra-scale computing, envisioned a
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Energy Technology and Management
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Figure 2. Setup for the second expe
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phase. The algorithms will be teste
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[Pb-212]/[Pb-212] final 100 10 1 0.
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Plasma Particle Dielectric Fiber El
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(species, sub-species, and sex), sp
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Figure 3. Relative risk of bone and
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Development of a Preliminary Physio
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Development of a Virtual Respirator
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Figure 3. Use of the chest cavity a
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Examination of the Use of Diazolumi
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Indoor Air Pathways to Nuclear, Che
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Source Building Release Observed re
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To illustrate the potential impact
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Non-Invasive Biological Monitoring
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Materials Science and Technology
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Figure 1. (a) - (c) Successive magn
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Component Fabrication and Assembly
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Development of a Low-Cost Photoelec
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elaxations, indicating the adequacy
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Promising chemical durability, as m
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Study Control Number : PN98028/1274
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High Power Density Solid Oxide Fuel
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Hybrid Plasma Process for Vacuum De
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Matson DW, PM Martin, WD Bennett, J
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We showed the proof-of-principle ap
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Bandgap Energy (eV) 2.9 2.85 2.8 2.
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Ultrathin Electrolyte Test Results
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Detailed Investigations on Hydrogen
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identified low energy step structur
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Development of Novel Photo-Catalyst
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-2 -1 Energy (eV) 0 1 2 Cu2O SrTiO3
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Here, we perform adsorption and des
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exceed those in the all-metal devic
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Intensity (a.u.) 0 100 200 300 400
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integrated voltage (V) 0.10 0.08 0.
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The first step was to implement thi
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Spontaneous Formation of Cu2O Nano-
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Actinide Chemistry at the Aqueous-M
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Figure 5. Close-up atomic force mic
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to 300°C and 400°C. Unfortunately
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Summary and Conclusions Transmutati
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Advanced Calibration/Registration T
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Figure 1a. Violet filter Figure 1b.
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Autonomous Ultrasonic Probe for Pro
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containing nitrous oxide, an optica
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Chemical Detection Using Cavity Enh
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Figure 3 is a color rendered simula
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appropriate study has been performe
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Exploitation of Conventional Chemic
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Figures 3 and 4 illustrate the impa
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enefits of using a modified spread-
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Single Channel Signal 0.0025 0.0020
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Therefore, in order to extract the
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A next-generation technology is nee
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Wind Dir. Figure 2. Negative ion co
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Volumetric Imaging of Materials by
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amplitude and peak frequency change
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Figure 3. 13 C NMR spectrum of corn
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Approach Two flow loops, one that o
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Modification of Ion Exchange Resins
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Permanganate Treatment for the Deco
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minimized by medium exchange. After
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Figure 1. Thermogravimetric analysi
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Validation and Scale-Up of Monosacc
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Concentration (g/100g solution) Con
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Analysis of High-Volume, Hyper-Dime
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ecent figure of merit values. Shoul
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Probabilistic Methods Development f
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a) Traditional PCA-based process co
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Acronyms and Abbreviations 2-D two-
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PNNL-13501 - Pacific Northwest National Laboratory

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