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Mudita Singhal

Publications and source records attributed to Mudita Singhal.

2 recordsLinked to original sources

COPASI--a COmplex PAthway SImulator.

MOTIVATION: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. RESULTS: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. AVAILABILITY: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

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Enabling proteomics discovery through visual analysis. The peptide permutation and protein prediction tool.

Proteins play a key role in cellular processes, making proteomics central to understanding systems biology. MS techniques provide a means to observe entire proteomes at a global level. Yet, high-throughput MS proteomics techniques generate data faster than it can currently be analyzed. The success of proteomics depends on high-throughput experimental techniques coupled with sophisticated visual analysis and data-mining methods. Visual analysis has been applied successfully in a number of fields plagued with huge, complex data sets and will likely be an important tool in proteomics discovery. PQuad, a novel visualization of MS proteomics data, provides powerful analysis capabilities that support a number of proteomic data applications. In particular, PQuad supports differential proteomics by simplifying the comparison of peptide sets from different experimental conditions as well as different protein identification or confidence scoring techniques. Finally, PQuad supports data validation and quality control by providing a variety of resolutions for huge amounts of data to reveal errors undetected by other methods.

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