Computational analysis of transcriptional regulatory elements: a field in flux.
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Biomedical subjects
A Hatzigeorgiou
Publications and source records attributed to A Hatzigeorgiou.
Cell cycle regulation of the murine cyclin E gene depends on an E2F binding site in the promoter.
Cyclin E controls progression through the G1 phase of the cell cycle in mammalian fibroblasts and potentially in many other cell types. Cyclin E is a rate-limiting activator of cdk2 kinase in late G1. The abundance of cyclin E is controlled by phase-specific fluctuations in the mRNA level; in mammalian fibroblasts, mRNA is not detected under conditions of serum starvation and is accumulated upon serum stimulation, with expression starting in mid-G1. Here, we report the cloning of the murine cyclin E promoter. We isolated a 3.8-kb genomic fragment that contains several transcriptional start sites and confers cell cycle regulation on a luciferase reporter gene. This fragment also supports transcriptional activation by adenovirus E1A, a known upstream regulator of cyclin E gene expression. An E2F binding site which is required for G1-specific activation of the cyclin E promoter in synchronized NIH 3T3 cells was identified in this fragment.
3T3 Cells↗
A parallel neural network simulator on the connection machine CM-5.
We here present a parallel implementation of artificial neural networks on the connection machine CM-5 and compare it with other parallel implementations on SIMD and MIMD architectures. This parallel implementation was developed with the goal of efficiently training large neural networks with huge training pattern sets for applications in molecular biology, in particular the prediction of coding regions in DNA sequences. The implementation uses training pattern parallelism and makes use of the parallel I/O facilities of the CM-5 and its efficient reduction operations available within the control network to achieve a high scalability. The parallel simulator obtains a maximum speed of 149.25 MCUPS for training feedforward networks with backpropagation on a 512 processor CM-5 system without using the CM-5 vector facility. The implementation poses no restriction on the type of network topology and works with different batch training algorithms like BP. Quickprop and Rprop.
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