Stability of biological networks as represented in Random Boolean Nets.

We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare

Simulations of Oligomeric Intermediates in Prion Diseases

We extend our previous stochastic cellular automata based model for areal aggregation of prion proteins on neuronal surfaces. The new anisotropic model allow us to simulate both strong beta-sheet and weaker attachment bonds between proteins. Constraining binding directions allows us to generate aggregate structures with the hexagonal lattice symmetry found in recently observed in vitro experiments. We argue that these constraints on rules may correspond to underlying steric constraints on the aggregation process. We find that monomer dominated growth of the areal aggregate is too slow to account for some observed doubling time-to-incubation time ratios inferred from data, and so consider aggregation dominated by relatively stable but non-infectious oligomeric intermediates. We compare a kinetic theory analysis of oligomeric aggregation to spatially explicit simulations of the process. We find that with suitable rules for misfolding of oligomers, possibly due to water exclusion by the surrounding aggregate, the resulting oligomeric aggregation model maps onto our previous monomer aggregation model. Therefore it can produce some of the same attractive features for the description of prion incubation time data. We propose experiments to test the oligomeric aggregation model.Comment: 8 pages, 10 figures For larger versions of several figures, see http://asaph.ucdavis.edu/~dmobley and click on the prion paper lin

ChemCell : a particle-based model of protein chemistry and diffusion in microbial cells.

Prokaryotic single-cell microbes are the simplest of all self-sufficient living organisms. Yet microbes create and use much of the molecular machinery present in more complex organisms, and the macro-molecules in microbial cells interact in regulatory, metabolic, and signaling pathways that are prototypical of the reaction networks present in all cells. We have developed a simple simulation model of a prokaryotic cell that treats proteins, protein complexes, and other organic molecules as particles which diffuse via Brownian motion and react with nearby particles in accord with chemical rate equations. The code models protein motion and chemistry within an idealized cellular geometry. It has been used to simulate several simple reaction networks and compared to more idealized models which do not include spatial effects. In this report we describe an initial version of the simulation code that was developed with FY03 funding. We discuss the motivation for the model, highlight its underlying equations, and describe simulations of a 3-stage kinase cascade and a portion of the carbon fixation pathway in the Synechococcus microbe

R&D for computational cognitive and social models : foundations for model evaluation through verification and validation (final LDRD report).

Sandia National Laboratories is investing in projects that aim to develop computational modeling and simulation applications that explore human cognitive and social phenomena. While some of these modeling and simulation projects are explicitly research oriented, others are intended to support or provide insight for people involved in high consequence decision-making. This raises the issue of how to evaluate computational modeling and simulation applications in both research and applied settings where human behavior is the focus of the model: when is a simulation 'good enough' for the goals its designers want to achieve? In this report, we discuss two years' worth of review and assessment of the ASC program's approach to computational model verification and validation, uncertainty quantification, and decision making. We present a framework that extends the principles of the ASC approach into the area of computational social and cognitive modeling and simulation. In doing so, we argue that the potential for evaluation is a function of how the modeling and simulation software will be used in a particular setting. In making this argument, we move from strict, engineering and physics oriented approaches to V&V to a broader project of model evaluation, which asserts that the systematic, rigorous, and transparent accumulation of evidence about a model's performance under conditions of uncertainty is a reasonable and necessary goal for model evaluation, regardless of discipline. How to achieve the accumulation of evidence in areas outside physics and engineering is a significant research challenge, but one that requires addressing as modeling and simulation tools move out of research laboratories and into the hands of decision makers. This report provides an assessment of our thinking on ASC Verification and Validation, and argues for further extending V&V research in the physical and engineering sciences toward a broader program of model evaluation in situations of high consequence decision-making

Numerical tools for atomistic simulations.

The final report for a Laboratory Directed Research and Development project entitled 'Parallel Atomistic Computing for Failure Analysis of Micromachines' is presented. In this project, atomistic algorithms for parallel computers were developed to assist in quantification of microstructure-property relations related to weapon micro-components. With these and other serial computing tools, we are performing atomistic simulations of various sizes, geometries, materials, and boundary conditions. These tools provide the capability to handle the different size-scale effects required to predict failure. Nonlocal continuum models have been proposed to address this problem; however, they are phenomenological in nature and are difficult to validate for micro-scale components. Our goal is to separately quantify damage nucleation, growth, and coalescence mechanisms to provide a basis for macro-scale continuum models that will be used for micromachine design. Because micro-component experiments are difficult, a systematic computational study that employs Monte Carlo methods, molecular statics, and molecular dynamics (EAM and MEAM) simulations to compute continuum quantities will provide mechanism-property relations associated with the following parameters: specimen size, number of grains, crystal orientation, strain rates, temperature, defect nearest neighbor distance, void/crack size, chemical state, and stress state. This study will quantify sizescale effects from nanometers to microns in terms of damage progression and thus potentially allow for optimized micro-machine designs that are more reliable and have higher fidelity in terms of strength. In order to accomplish this task, several atomistic methods needed to be developed and evaluated to cover the range of defects, strain rates, temperatures, and sizes that a material may see in micro-machines. Therefore we are providing a complete set of tools for large scale atomistic simulations that include pre-processing of realistic material configurations, processing under different environments, and post-processing with appropriate continuum quantities. By running simulations with these tools, we are able to determine size scale effects that correlate microstructure and defect configurations with mechanical properties of materials

Model-building codes for membrane proteins.

We have developed a novel approach to modeling the transmembrane spanning helical bundles of integral membrane proteins using only a sparse set of distance constraints, such as those derived from MS3-D, dipolar-EPR and FRET experiments. Algorithms have been written for searching the conformational space of membrane protein folds matching the set of distance constraints, which provides initial structures for local conformational searches. Local conformation search is achieved by optimizing these candidates against a custom penalty function that incorporates both measures derived from statistical analysis of solved membrane protein structures and distance constraints obtained from experiments. This results in refined helical bundles to which the interhelical loops and amino acid side-chains are added. Using a set of only 27 distance constraints extracted from the literature, our methods successfully recover the structure of dark-adapted rhodopsin to within 3.2 {angstrom} of the crystal structure

Quantum gate decomposition algorithms.

Quantum computing algorithms can be conveniently expressed in a format of a quantum logical circuits. Such circuits consist of sequential coupled operations, termed ''quantum gates'', or quantum analogs of bits called qubits. We review a recently proposed method [1] for constructing general ''quantum gates'' operating on an qubits, as composed of a sequence of generic elementary ''gates''

Multi-scale saliency search in image analysis.

Saliency detection in images is an important outstanding problem both in machine vision design and the understanding of human vision mechanisms. Recently, seminal work by Itti and Koch resulted in an effective saliency-detection algorithm. We reproduce the original algorithm in a software application Vision and explore its limitations. We propose extensions to the algorithm that promise to improve performance in the case of difficult-to-detect objects