Logic motif of combinatorial control in transcriptional networks

Combinatorial control is prevalent in transcriptional regulatory networks. However, whether there are specific logic patterns over- or under-represented in real networks remains uninvestigated. Using a theoretic model and _in-silico_ simulations, we systematically study how the relative abundance of distinct regulatory logic patterns influences the network’s global dynamics. We find that global dynamic characteristics are sensitive to several specific logic patterns regardless of the detailed network topology. We show it is possible to infer logic motifs based on the sensitivity profile and the biological interpretations of these global characteristics

3D ShapeNets: A Deep Representation for Volumetric Shapes

3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet -- a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.Comment: to be appeared in CVPR 201

Molecular evolution of Cide family proteins: Novel domain formation in early vertebrates and the subsequent divergence

<p>Abstract</p> <p>Background</p> <p>Cide family proteins including Cidea, Cideb and Cidec/Fsp27, contain an N-terminal CIDE-N domain that shares sequence similarity to the N-terminal CAD domain (NCD) of DNA fragmentation factors Dffa/Dff45/ICAD and Dffb/Dff40/CAD, and a unique C-terminal CIDE-C domain. We have previously shown that Cide proteins are newly emerged regulators closely associated with the development of metabolic diseases such as obesity, diabetes and liver steatosis. They modulate many metabolic processes such as lipolysis, thermogenesis and TAG storage in brown adipose tissue (BAT) and white adipose tissue (WAT), as well as fatty acid oxidation and lipogenesis in the liver.</p> <p>Results</p> <p>To understand the evolutionary process of Cide proteins and provide insight into the role of Cide proteins as potential metabolic regulators in various species, we searched various databases and performed comparative genomic analysis to study the sequence conservation, genomic structure, and phylogenetic tree of the CIDE-N and CIDE-C domains of Cide proteins. As a result, we identified signature sequences for the N-terminal region of Dffa, Dffb and Cide proteins and CIDE-C domain of Cide proteins, and observed that sequences homologous to CIDE-N domain displays a wide phylogenetic distribution in species ranging from lower organisms such as hydra (<it>Hydra vulgaris</it>) and sea anemone (<it>Nematostella vectensis</it>) to mammals, whereas the CIDE-C domain exists only in vertebrates. Further analysis of their genomic structures showed that although evolution of the ancestral CIDE-N domain had undergone different intron insertions to various positions in the domain among invertebrates, the genomic structure of <it>Cide </it>family in vertebrates is stable with conserved intron phase.</p> <p>Conclusion</p> <p>Based on our analysis, we speculate that in early vertebrates CIDE-N domain was evolved from the duplication of NCD of Dffa. The CIDE-N domain somehow acquired the CIDE-C domain that was formed around the same time, subsequently generating the Cide protein. Subsequent duplication and evolution have led to the formation of different Cide family proteins that play unique roles in the control of metabolic pathways in different tissues.</p