This book constitutes the refereed proceedings of the 17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013, held in Beijing, China, in April 2013. The 32 revised full papers...
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ISBN:
(数字)9783642371950
ISBN:
(纸本)9783642371943
This book constitutes the refereed proceedings of the 17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013, held in Beijing, China, in April 2013. The 32 revised full papers were carefully reviewed and selected from 167 submissions. The papers cover a wide range of topics including molecular sequence analysis; genes and regulatory elements; molecular evolution; gene expression; biological networks; sequencing and genotyping technologies; genomics; epigenomics; metagenomics; population, statistical genetics; systems biology; computational proteomics; computational structural biology; imaging; large-scale data management.
Duplication events are regarded as sources of evolutionary novelty, but our understanding of general trends for the long-term trajectory of additional genomic material is still lacking. Organisms with a history of who...
Duplication events are regarded as sources of evolutionary novelty, but our understanding of general trends for the long-term trajectory of additional genomic material is still lacking. Organisms with a history of whole genome duplication (WGD) offer a unique opportunity to study potential trends in the context of gene retention and/or loss, gene and network dosage, and changes in gene expression. In this review, we discuss the prevalence of polyploidy across the tree of life, followed by an overview of studies investigating genome evolution and gene expression. We then provide an overview of methods in network biology, phylogenomics, and population genomics that are critical for advancing our understanding of evolution post-WGD, highlighting the need for models that can accommodate polyploids. Finally, we close with a brief note on the importance of random processes in the evolution of polyploids with respect to neutral versus selective forces, ancestral polymorphisms, and the formation of autopolyploids versus allopolyploids.
Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar sc...
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Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar scaling pattern with time: The highest rates are those measured over the shortest time interval. This creates a disconnect between micro- and macroevolution, although the pattern is the opposite of what some might expect: Patterns of change over short timescales predict that evolution has tremendous potential to create variation and that potential is barely tapped by macroevolution. In this review, we discuss this shared scaling pattern across evolutionary rates. We break down possible explanations for scaling into two categories, estimation error and model misspecification, and discuss how both apply to each type of rate. We also discuss the consequences of this ubiquitous pattern, which can lead to unexpected results when comparing ratesover different timescales. Finally, after addressing purely statistical concerns, we explore a few possibilities for a shared unifying explanation across the three types of rates that results from a failure to fully understand and account for how biological processes scale over time.
DNA methylation is a chemical modification that occurs predominantly on CG dinucleotides in mammalian genomes. However, recent studies have revealed that non-CG methylation (mCH) is abundant and nonrandomly distribute...
DNA methylation is a chemical modification that occurs predominantly on CG dinucleotides in mammalian genomes. However, recent studies have revealed that non-CG methylation (mCH) is abundant and nonrandomly distributed in the genomes of pluripotent cells and brain cells, and is present at lower levels in many other human cells and tissues. Surprisingly, mCH in pluripotent cells is distinct from that in brain cells in terms of sequence specificity and association with transcription, indicating the existence of different mCH pathways. In addition, several recent studies have begun to reveal the biological significance of mCH in diverse cellular processes. In reprogrammed somatic cells, mCH marks megabase-scale regions that have failed to revert to the pluripotent epigenetic state. In myocytes, promoter mCH accumulation is associated with the transcriptional response to environmental factors. In brain cells, mCH accumulates during the establishment of neural circuits and is associated with Rett syndrome. In this review, we summarize the current understanding of mCH and its possible functional consequences in different biological contexts.
This interdisciplinary thesis introduces a systems biology approach to study the cell fate decision mediated by autophagy. A mathematical model of interaction between Autophagy and Apoptosis in mammalian cells is prop...
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ISBN:
(数字)9783319149622
ISBN:
(纸本)9783319149615;9783319365121
This interdisciplinary thesis introduces a systems biology approach to study the cell fate decision mediated by autophagy. A mathematical model of interaction between Autophagy and Apoptosis in mammalian cells is proposed. In this dynamic model autophagy acts as a gradual response to stress (Rheostat) that delays the initiation of bistable switch of apoptosis to give the cells an opportunity to survive. The author shows that his dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells, he has provided a systems biology experimental framework to study quantitative and dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative fluorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells.
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