Analysis of transcription control signals using artificial neural networks. 1995

T M Nair, and S S Tambe, and B D Kulkarni
Chemical Engineering Division, National Chemical Laboratory, Pune, India.

The role of the upstream region in controlling the transcription efficiency of a gene is well established. However, the question of predicting the extent of gene expressed given the upstream region has so far remained unresolved. Using an artificial neural network (ANN) to capture the internal representation associated with the transcription control signal, the present work predicts the rate of mRNA synthesis based on the pattern contained in the upstream region. Further, the model has been used to predict the transcription efficiency for all possible single base mutations associated with the beta-globin promoter. The simulation results reveal that apart from the experimental observation that alpha-79G-A and -78G-A mutation increases the efficiency of transcription, mutation in these regions by C or T also causes an increase in transcription. Furthermore the simulation results verify that mutations in the conserved region, in general, decrease the transcriptional efficiency. However, the results also show that certain sequence elements, when mutated, either cause a marginal increase in the level of transcription or have no effect on transcription levels. The simulation results can be used as a guide in designing mutation experiments since an a priori estimate of the possible outcome of a mutation can be obtained.

UI MeSH Term Description Entries
D008957 Models, Genetic Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment. Genetic Models,Genetic Model,Model, Genetic
D011401 Promoter Regions, Genetic DNA sequences which are recognized (directly or indirectly) and bound by a DNA-dependent RNA polymerase during the initiation of transcription. Highly conserved sequences within the promoter include the Pribnow box in bacteria and the TATA BOX in eukaryotes. rRNA Promoter,Early Promoters, Genetic,Late Promoters, Genetic,Middle Promoters, Genetic,Promoter Regions,Promoter, Genetic,Promotor Regions,Promotor, Genetic,Pseudopromoter, Genetic,Early Promoter, Genetic,Genetic Late Promoter,Genetic Middle Promoters,Genetic Promoter,Genetic Promoter Region,Genetic Promoter Regions,Genetic Promoters,Genetic Promotor,Genetic Promotors,Genetic Pseudopromoter,Genetic Pseudopromoters,Late Promoter, Genetic,Middle Promoter, Genetic,Promoter Region,Promoter Region, Genetic,Promoter, Genetic Early,Promoter, rRNA,Promoters, Genetic,Promoters, Genetic Middle,Promoters, rRNA,Promotor Region,Promotors, Genetic,Pseudopromoters, Genetic,Region, Genetic Promoter,Region, Promoter,Region, Promotor,Regions, Genetic Promoter,Regions, Promoter,Regions, Promotor,rRNA Promoters
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D005914 Globins A superfamily of proteins containing the globin fold which is composed of 6-8 alpha helices arranged in a characterstic HEME enclosing structure. Globin
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D012333 RNA, Messenger RNA sequences that serve as templates for protein synthesis. Bacterial mRNAs are generally primary transcripts in that they do not require post-transcriptional processing. Eukaryotic mRNA is synthesized in the nucleus and must be exported to the cytoplasm for translation. Most eukaryotic mRNAs have a sequence of polyadenylic acid at the 3' end, referred to as the poly(A) tail. The function of this tail is not known for certain, but it may play a role in the export of mature mRNA from the nucleus as well as in helping stabilize some mRNA molecules by retarding their degradation in the cytoplasm. Messenger RNA,Messenger RNA, Polyadenylated,Poly(A) Tail,Poly(A)+ RNA,Poly(A)+ mRNA,RNA, Messenger, Polyadenylated,RNA, Polyadenylated,mRNA,mRNA, Non-Polyadenylated,mRNA, Polyadenylated,Non-Polyadenylated mRNA,Poly(A) RNA,Polyadenylated mRNA,Non Polyadenylated mRNA,Polyadenylated Messenger RNA,Polyadenylated RNA,RNA, Polyadenylated Messenger,mRNA, Non Polyadenylated
D012984 Software Sequential operating programs and data which instruct the functioning of a digital computer. Computer Programs,Computer Software,Open Source Software,Software Engineering,Software Tools,Computer Applications Software,Computer Programs and Programming,Computer Software Applications,Application, Computer Software,Applications Software, Computer,Applications Softwares, Computer,Applications, Computer Software,Computer Applications Softwares,Computer Program,Computer Software Application,Engineering, Software,Open Source Softwares,Program, Computer,Programs, Computer,Software Application, Computer,Software Applications, Computer,Software Tool,Software, Computer,Software, Computer Applications,Software, Open Source,Softwares, Computer Applications,Softwares, Open Source,Source Software, Open,Source Softwares, Open,Tool, Software,Tools, Software
D014158 Transcription, Genetic The biosynthesis of RNA carried out on a template of DNA. The biosynthesis of DNA from an RNA template is called REVERSE TRANSCRIPTION. Genetic Transcription
D015398 Signal Transduction The intracellular transfer of information (biological activation/inhibition) through a signal pathway. In each signal transduction system, an activation/inhibition signal from a biologically active molecule (hormone, neurotransmitter) is mediated via the coupling of a receptor/enzyme to a second messenger system or to an ion channel. Signal transduction plays an important role in activating cellular functions, cell differentiation, and cell proliferation. Examples of signal transduction systems are the GAMMA-AMINOBUTYRIC ACID-postsynaptic receptor-calcium ion channel system, the receptor-mediated T-cell activation pathway, and the receptor-mediated activation of phospholipases. Those coupled to membrane depolarization or intracellular release of calcium include the receptor-mediated activation of cytotoxic functions in granulocytes and the synaptic potentiation of protein kinase activation. Some signal transduction pathways may be part of larger signal transduction pathways; for example, protein kinase activation is part of the platelet activation signal pathway. Cell Signaling,Receptor-Mediated Signal Transduction,Signal Pathways,Receptor Mediated Signal Transduction,Signal Transduction Pathways,Signal Transduction Systems,Pathway, Signal,Pathway, Signal Transduction,Pathways, Signal,Pathways, Signal Transduction,Receptor-Mediated Signal Transductions,Signal Pathway,Signal Transduction Pathway,Signal Transduction System,Signal Transduction, Receptor-Mediated,Signal Transductions,Signal Transductions, Receptor-Mediated,System, Signal Transduction,Systems, Signal Transduction,Transduction, Signal,Transductions, Signal
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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