PNNL-13501 - Pacific Northwest National Laboratory

More documents

Recommendations

Info

and showed that a Petri net faithfully represents each subnetwork. Any family of chemical kinetic ratereaction graphs that is defined by the same order structure as the EGFR signaling network is also faithfully represented by a Petri net. We observe that this family of networks is both simply connected and directed, there are no self-loops, and as such, defines a set of directed graphs (digraphs), where each digraph is a multi-graph. Every such multi-graph (hyper-graph) is characterized by a path algebra representation. This algebraic representation defines an oriented matroid (an n-dimensional hyper-cube combinatorial geometry). We next proved that effective combinatorial optimizations of the oriented matroid geometry of any chemical kinetic ratereaction graph is optimally searched by the “greedy algorithm” in O (n) time and space complexity for circuit sub-paths that satisfy an objective function (i.e., are defined by the state and transition invariants). 2. We have proven a graph isomorphism representation theorem that asserts that the directed graph network representation of the now linearized kinetic reaction model of an inter-cellular signal transduction pathway is faithfully represented by a discrete digraph that will be referred to as a Petri net. The proof of the graph isomorphism representation theorem follows from equating the linear matrix representation of graph network representation of the now linearized kinetic reaction model to the linear incidence matrix representation of a Petri net. We can also define a decision procedure that determines if two or more networks of reaction paths are isomorphic. 3. The combinatorial representation theory further allows us to define a linearized kinetic reaction model of an inter-cellular signal transduction pathway as a “birth and death” stochastic process, which in turn defines a continuous Markov process. This continuous Markov process can be discretized. We next obtain discrete Markov systems representation. This representation defines a stochastic Petri net. With this Petri net representation, we can now develop an application of hidden Markov models to find missing reaction parameter data. 4. The stochastic formulation of chemical kinetics is being employed for two reasons. First, stochastic simulations provide statistical data regarding product distributions that are not obtained from the usual deterministic kinetic formulation. Second, use of the 120 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report stochastic kinetic simulation allows for an easy extension of the simulation to include diffusion through a Brownian dynamics simulator, and hence to also include structural information of the cell and its components. In principle, the simulation could extend downward to include organelle structure, protein structure, protein charges, etc. The extension downward to include more detail is only limited by the availability of data and computational resources. Petri nets are being developed that provide the same information as stochastic simulations. Summary and Conclusions During the last 2 years, we described some of the mathematical and systems engineering foundations that are key to understanding the complex nature of emerging cell-signaling communications networks. We also outlined some of the requirements for building a set of computational tools for the construction of a complete adaptive computational model of a living cell. The following is a list of objectives set for the current year: • paper defining equation-based graph isomorphisms, based upon a process control network model of cellsignaling pathways • develop computational simulation code for both generating Petri nets and their optimized process control model • model transport of activated kinase from cytoplasm to nucleus by diffusion kinetics and prove that there is a correspondence between the Markov state model representation of a timed stochasatic cell-signaling Petri net model and the underlying Markov transport in the cytoplasmic hydrodynamic medium • extend UCHC virtual cell PSE by application of NWGrid and NWPhys • test model for signaling from the CaR by experimental data from OHSU. Publications and Presentations Oliveira JS and CG Bailey. “Computational complexity of emerging cell signaling networks.” (in progress). Oliveira JS and CG Bailey. “Combinatorial symmetries of emerging cell signaling networks.” (in progress).
Oliveira JS, JB Jones-Oliveira, and CG Bailey. “A combinatorial representation theory of inter-cellular signal transduction pathways.” (in internal peer review). Oliveira JS, JB Jones-Oliveira, and CG Bailey. “Matroidal optimizations of emerging cell signaling networks.” (in progress). Dixon DA. May 2000. Co-organized the Computational Challenges In The Post-Genomic Age Symposium, San Francisco. Dixon DA. May 2000. “An overview of the virtual cell.” Presented at the Computational Challenges In The Post- Genomic Age Symposium, San Francisco (invited). Oliveira JS. August 2000. “Computational Grand Challenges Associated with Combinatorial Models of Intra-Cellular Signaling Transduction Networks.” Presented at the Mathematical Challenges of the 21st Century, American Mathematical Society, UCLA. Oliveira JS. August 2000. Chaired the Session on Mathematical Applications to the Natural Sciences (Mathematical Biology) at the Mathematical Challenges of the 21st Century, American Mathematical Society, UCLA. Computational Science and Engineering 121
Page 1 and 2:
Laboratory Directed Research and De
Page 3 and 4:
Laboratory Directed Research and De
Page 5 and 6:
Computational Science and Engineeri
Page 7 and 8:
Policy and Management Sciences Asse
Page 9 and 10:
Introduction Department of Energy O
Page 11 and 12:
5. After reviews by the Technical a
Page 13 and 14:
• The Technical Council peer revi
Page 15 and 16:
methods applicable to combustion, s
Page 17 and 18:
Summary Each national laboratory mu
Page 19 and 20:
Study Control Number: PN0004/1411 A
Page 21 and 22:
Table 1. Positive and negative ions
Page 23 and 24:
m/z Arbitrary Units Figure 1. Mass
Page 25 and 26:
introduce low-volatility compounds
Page 27 and 28:
arrangement, but the charge is reta
Page 29 and 30:
two liquid chromatographic gradient
Page 31 and 32:
Integrated Microfabricated Devices
Page 33 and 34:
Matrix Assisted Laser Desorption/Io
Page 35 and 36:
Molecular Beam Synthesis and Charac
Page 37 and 38:
temperature distribution of the des
Page 39 and 40:
expected to result in significant a
Page 41 and 42:
Study Control Number: PN99069/1397
Page 43 and 44:
Page 45 and 46:
Seuss DT and KA Prather. 1999. “M
Page 47 and 48:
Analysis of Receptor-Modulated Ion
Page 49 and 50:
laboratory (Lu and Xie 1997; Lu et
Page 51 and 52:
Automated DNA Fingerprinting Microa
Page 53 and 54:
Comparison of 4 Xanthomonas Isolate
Page 55 and 56:
Approach Hematopoietic (bone marrow
Page 57 and 58:
Page 59 and 60:
Anti-Human lgG Human lgG Protein G
Page 61 and 62:
Characterization of Global Gene Reg
Page 63 and 64:
PCR products for immobilization on
Page 65 and 66:
identify novel proteins of importan
Page 67 and 68:
Immunoprecipitaton of tyrosinephosp
Page 69 and 70:
Coupled NMR and Confocal Microscopy
Page 71 and 72:
In the optical microscopy image, th
Page 73 and 74:
Development and Applications of a M
Page 75 and 76:
Figure 3. Hepa-1 cells undergoing a
Page 77 and 78:
cellular processing (such as post-t
Page 79 and 80:
8. Initial demonstration of an off-
Page 81 and 82:
expression systems for several year
Page 83 and 84: accumulation of protein products, i
Page 85 and 86: Study Control Number: PN00039/1446
Page 87 and 88: Fungal Conversion of Agricultural W
Page 89 and 90: Fungal Conversion of Waste Glucose/
Page 91 and 92: Summary and Conclusions We demonstr
Page 93 and 94: are shown in Figure 1(a) and 1(b),
Page 99 and 100: Anchorage-Independent Growth (% con
Page 101 and 102: selection of seedlings demonstratin
Page 103 and 104: NMR-Based Structural Genomics Micha
Page 105 and 106: Solution-State Structure and Fold-C
Page 107 and 108: Plant Root Exudates and Microbial G
Page 109 and 110: 98 99 100 6 7 10 3 1 12 11 15 16 17
Page 115 and 116: Summary and Conclusions • The gre
Page 119 and 120: Jones-Oliveira JB, JS Oliveira, HE
Page 121 and 122: • securely moves files to a targe
Page 125 and 126: Plenary invited lecture, “The Ele
Page 127 and 128: Figure 1. X3DGEN tetrahedral mesh o
Page 129 and 130: Figure 5a. OSO volume rendering of
Page 131 and 132: Development of Models of Cell-Signa
Page 133: SOS p 23 EGFR Professor Rodland at
Page 137 and 138: Figure 2. Associating a dataset mar
Page 139: to 1) incorporate three-dimensional
Page 142 and 143: shown in Figure 1. Simulated result
Page 144 and 145: HostBuilder: A Tool for the Combina
Page 146 and 147: Invariant Discretization Methods fo
Page 148 and 149: Graphics, Inc., workstation. The co
Page 152 and 153: Mixed Hamiltonian Methods for Geoch
Page 154 and 155: guyanaite, and grimaldiite (see Fig
Page 156 and 157: in the Environmental Molecular Scie
Page 158 and 159: Simulation and Modeling of Electroc
Page 160 and 161: Summary and Conclusions We implemen
Page 164 and 165: Since the implementation of the gen
Page 166 and 167: asis of previous performance, perce
Page 168 and 169: Bioinformatics for High-Throughput
Page 170 and 171: Summary and Conclusions In previous
Page 172 and 173: User Cat - Supervisor (client) Anal
Page 174 and 175: The second goal was to research way
Page 176 and 177: Figure 1. A visualization technique
Page 178 and 179: and interactions. The flexibility o
Page 184 and 185:
Figure 4. A filtered version of Fig
Page 186 and 187:
Development of Modeling Tools for J
Page 188 and 189:
Figure 2. Schematic of experimental
Page 190 and 191:
Integrated Modeling and Simulation
Page 192 and 193:
(a) (b) (c) (d) Figure 1. Results o
Page 194 and 195:
Thurman DA. August 2000. Collaborat
Page 196 and 197:
MARC Input initial m esh and result
Page 198 and 199:
(Johnson 1999; Colligan 1999; Gould
Page 200 and 201:
Modeling of High-Velocity Forming f
Page 202 and 203:
Global divergence error 1 10 -14 0
Page 204 and 205:
that users were unlikely to appreci
Page 206 and 207:
Earth System Science
Page 208 and 209:
the lateral extent of the plume so
Page 210 and 211:
Biogeochemical Processes and Microb
Page 212 and 213:
Analyses of populations of viable a
Page 214 and 215:
The first task was to establish fiv
Page 216 and 217:
purged for 2 minutes. The gas sampl
Page 218 and 219:
developing code architecture to min
Page 220 and 221:
Berkowitz, CM. June 2000. “Atmosp
Page 222 and 223:
environmental monitoring, 2) portab
Page 224 and 225:
corresponded to a current density o
Page 226 and 227:
Enhancing Emissions Pre-Processing
Page 228 and 229:
Environmental Fate and Effects of D
Page 230 and 231:
nitrogen and unpredictable eutrophi
Page 232 and 233:
Figure 1. Ordinary kriging of 2 m d
Page 234 and 235:
Page 236 and 237:
precipitation of calcite occurred i
Page 238 and 239:
Page 240 and 241:
Interrelationships Between Processe
Page 242 and 243:
phenanthrene resistant fraction con
Page 244 and 245:
injection of an immiscible gas phas
Page 246 and 247:
still some key kinetic issues that
Page 248 and 249:
Page 250 and 251:
Application of a Crop Model The res
Page 252 and 253:
The Nature, Occurrence, and Origin
Page 254 and 255:
Particle number concentration, 1/cm
Page 256 and 257:
geometry optimized AlOOH and FeOOH
Page 258 and 259:
Page 260 and 261:
allowable parameters using thermody
Page 262 and 263:
TCE + 3H + + 3Fe 2+ Fe)=> acetylene
Page 264 and 265:
Use of Shear-Thinning Fluids for In
Page 266 and 267:
concentration of 0.5% AMX was the m
Page 268 and 269:
Roberts AL, LA Totten, WA Arnold, D
Page 270 and 271:
tetra-scale computing, envisioned a
Page 272 and 273:
Page 274 and 275:
Energy Technology and Management
Page 276 and 277:
Figure 2. Setup for the second expe
Page 278 and 279:
phase. The algorithms will be teste
Page 280 and 281:
Page 282 and 283:
[Pb-212]/[Pb-212] final 100 10 1 0.
Page 284 and 285:
Plasma Particle Dielectric Fiber El
Page 286 and 287:
(species, sub-species, and sex), sp
Page 288 and 289:
Figure 3. Relative risk of bone and
Page 290 and 291:
Development of a Preliminary Physio
Page 292 and 293:
Development of a Virtual Respirator
Page 294 and 295:
Figure 3. Use of the chest cavity a
Page 296 and 297:
Examination of the Use of Diazolumi
Page 298 and 299:
Indoor Air Pathways to Nuclear, Che
Page 300 and 301:
Source Building Release Observed re
Page 302 and 303:
To illustrate the potential impact
Page 304 and 305:
Non-Invasive Biological Monitoring
Page 306 and 307:
Page 308 and 309:
Materials Science and Technology
Page 310 and 311:
Figure 1. (a) - (c) Successive magn
Page 312 and 313:
Component Fabrication and Assembly
Page 314 and 315:
Development of a Low-Cost Photoelec
Page 316 and 317:
elaxations, indicating the adequacy
Page 318 and 319:
Promising chemical durability, as m
Page 320 and 321:
Page 322 and 323:
Study Control Number : PN98028/1274
Page 324 and 325:
High Power Density Solid Oxide Fuel
Page 326 and 327:
Hybrid Plasma Process for Vacuum De
Page 328 and 329:
Page 330 and 331:
Matson DW, PM Martin, WD Bennett, J
Page 332 and 333:
We showed the proof-of-principle ap
Page 334 and 335:
Bandgap Energy (eV) 2.9 2.85 2.8 2.
Page 336 and 337:
Page 338 and 339:
Ultrathin Electrolyte Test Results
Page 340 and 341:
Detailed Investigations on Hydrogen
Page 342 and 343:
identified low energy step structur
Page 344 and 345:
Development of Novel Photo-Catalyst
Page 346 and 347:
-2 -1 Energy (eV) 0 1 2 Cu2O SrTiO3
Page 348 and 349:
Here, we perform adsorption and des
Page 350 and 351:
exceed those in the all-metal devic
Page 352 and 353:
Page 354 and 355:
Intensity (a.u.) 0 100 200 300 400
Page 356 and 357:
integrated voltage (V) 0.10 0.08 0.
Page 358 and 359:
The first step was to implement thi
Page 360 and 361:
Spontaneous Formation of Cu2O Nano-
Page 362 and 363:
Actinide Chemistry at the Aqueous-M
Page 364 and 365:
Figure 5. Close-up atomic force mic
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
to 300°C and 400°C. Unfortunately
Page 372 and 373:
Page 374 and 375:
Summary and Conclusions Transmutati
Page 376 and 377:
Page 378 and 379:
Advanced Calibration/Registration T
Page 380 and 381:
Figure 1a. Violet filter Figure 1b.
Page 382 and 383:
Autonomous Ultrasonic Probe for Pro
Page 384 and 385:
containing nitrous oxide, an optica
Page 386 and 387:
Chemical Detection Using Cavity Enh
Page 388 and 389:
Figure 3 is a color rendered simula
Page 390 and 391:
appropriate study has been performe
Page 392 and 393:
Exploitation of Conventional Chemic
Page 394 and 395:
Figures 3 and 4 illustrate the impa
Page 396 and 397:
Page 398 and 399:
enefits of using a modified spread-
Page 400 and 401:
Page 402 and 403:
Single Channel Signal 0.0025 0.0020
Page 404 and 405:
Therefore, in order to extract the
Page 406 and 407:
A next-generation technology is nee
Page 408 and 409:
Page 410 and 411:
Wind Dir. Figure 2. Negative ion co
Page 412 and 413:
Page 414 and 415:
Volumetric Imaging of Materials by
Page 416 and 417:
amplitude and peak frequency change
Page 418 and 419:
Page 420 and 421:
Figure 3. 13 C NMR spectrum of corn
Page 422 and 423:
Approach Two flow loops, one that o
Page 424 and 425:
Page 426 and 427:
Modification of Ion Exchange Resins
Page 428 and 429:
Permanganate Treatment for the Deco
Page 430 and 431:
Page 432 and 433:
minimized by medium exchange. After
Page 434 and 435:
Figure 1. Thermogravimetric analysi
Page 436 and 437:
Validation and Scale-Up of Monosacc
Page 438 and 439:
Concentration (g/100g solution) Con
Page 440 and 441:
Analysis of High-Volume, Hyper-Dime
Page 442 and 443:
Page 444 and 445:
ecent figure of merit values. Shoul
Page 446 and 447:
Probabilistic Methods Development f
Page 448 and 449:
a) Traditional PCA-based process co
Page 450:
Acronyms and Abbreviations 2-D two-
show all

PNNL-13501 - Pacific Northwest National Laboratory

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?