Molecular Systems Biology
About: Molecular Systems Biology is an academic journal. The journal publishes majorly in the area(s): Gene & Regulation of gene expression. It has an ISSN identifier of 1744-4292. It is also open access. Over the lifetime, 1261 publication(s) have been published receiving 140543 citation(s).
Papers published on a yearly basis
01 Jan 2011-Molecular Systems Biology
TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
Abstract: Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.
01 Jan 2006-Molecular Systems Biology
TL;DR: These mutants—the ‘Keio collection’—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome‐wide testing of mutational effects in a common strain background, E. coli K‐12 BW25113.
Abstract: We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants-the 'Keio collection'-provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).
01 Jan 2007-Molecular Systems Biology
TL;DR: A protein‐network‐based approach is applied that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases, which provide novel hypotheses for pathways involved in tumor progression.
Abstract: Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
01 Jan 2007-Molecular Systems Biology
TL;DR: An updated genome‐scale reconstruction of the metabolic network in Escherichia coli K‐12 MG1655 with increased scope and computational capability is presented, expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
Abstract: An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
01 Jan 2008-Molecular Systems Biology
TL;DR: This tutorial explains the application of SRM for quantitative proteomics, including the selection of proteotypic peptides and the optimization and validation of transitions, and normalization and various factors affecting sensitivity and accuracy are discussed.
Abstract: Systems biology relies on data sets in which the same group of proteins is consistently identified and precisely quantified across multiple samples, a requirement that is only partially achieved by current proteomics approaches. Selected reaction monitoring (SRM)—also called multiple reaction monitoring—is emerging as a technology that ideally complements the discovery capabilities of shotgun strategies by its unique potential for reliable quantification of analytes of low abundance in complex mixtures. In an SRM experiment, a predefined precursor ion and one of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted peptide can constitute a definitive assay. Typically, a large number of peptides are quantified during a single LC-MS experiment. This tutorial explains the application of SRM for quantitative proteomics, including the selection of proteotypic peptides and the optimization and validation of transitions. Furthermore, normalization and various factors affecting sensitivity and accuracy are discussed.
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