A Multiscale Factorization Method for Simulating Mesoscopic Systems with Atomic Precision

Mesoscopic $N-$atom systems derive their structural and dynamical properties from processes coupled across multiple scales in space and time. An efficient method for understanding these systems in the friction dominated regime from the underlying N-atom formulation is presented. The method integrates notions of multiscale analysis, Trotter factorization, and a hypothesis that the momenta conjugate to coarse-grained variables can be treated as a stationary random process. The method is demonstrated for Lactoferrin, Nudaurelia Capensis Omega Virus, and Cowpea Chlorotic Mottle Virus to assess its accuracy and scaling with system size.Comment: This is the latest version with improved convergence analysi

ProtoMD: A Prototyping Toolkit for Multiscale Molecular Dynamics

ProtoMD is a toolkit that facilitates the development of algorithms for multiscale molecular dynamics (MD) simulations. It is designed for multiscale methods which capture the dynamic transfer of information across multiple spatial scales, such as the atomic to the mesoscopic scale, via coevolving microscopic and coarse-grained (CG) variables. ProtoMD can be also be used to calibrate parameters needed in traditional CG-MD methods. The toolkit integrates `GROMACS wrapper' to initiate MD simulations, and `MDAnalysis' to analyze and manipulate trajectory files. It facilitates experimentation with a spectrum of coarse-grained variables, prototyping rare events (such as chemical reactions), or simulating nanocharacterization experiments such as terahertz spectroscopy, AFM, nanopore, and time-of-flight mass spectroscopy. ProtoMD is written in python and is freely available under the GNU General Public License from github.com/CTCNano/proto_md

SIMULATION-ENHANCED FRACTURE DETECTION: RESEARCH AND DEMONSTRATION IN U.S. BASINS

Remote detection and characterization of fractured reservoirs is facilitated in this project by developing a revolutionary software system. The Model-Automated Geo-Informatics (MAGI) software integrates basin modeling, seismic data, synthetic seismic wave propagation and well data via information theory. The result is a seismic inversion cast in terms of fracture and other reservoir characteristics. The MAGI software was fully tested on synthetic data to verify program accuracy and robustness to data error. In Phase II, we (1) collected geological information (stratigraphic, structural, thermal, geochemical, fracturing and other information across the study area) (Task 4.1); (2) created a GIS database that is compatible with the input requirements of MAGI (Task 4.1); (3) implemented a web-based interface for user friendly access (Task 4.2); (4) gathered and preprocessed seismic data for input into MAGI; (5) developed two- and three-dimensional wave propagation simulators (in time domain) for fluid saturated porous media and implemented matching layer methodology for absorbing boundary conditions (Task 4.3); (6) developed parallel version of the seismic simulators (Task 4.3); (7) proposed an information theory framework that allows for the integration of multiple data types of a range of quality (Task 4.4); (8) developed and implemented highly efficient, parallel, Gauss-Newton seismic waveform inversion code based on reciprocity theorem (Task 4.5); (9) verified and demonstrated the accuracy and efficiency of the wave propagation and seismic waveform inversion codes (Tasks 4.3 and 4.5); and (10) identified the requirements for seismic data to allow seismic inversion (Task 4.6). With these accomplishments, we are prepared to carry out a demonstration in the Illinois Basin. A database of the proposed study area and the web-based system to facilitate geologic and seismic data input are ready for this demonstration as are mapping tools for comparison and observations

Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

BACKGROUND: Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. RESULTS: Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. CONCLUSION: Multiplex time series data can be used for the construction of the network of cellular processes and the calibration of the associated physicochemical parameters. We have demonstrated these concepts in the context of gene regulation understood through the analysis of gene expression microarray time series data. Casting the approach in a probabilistic framework has allowed us to address the uncertainties in gene expression microarray data. Our approach was found to be robust to error in the gene expression microarray data and mistakes in a proposed TRN