An investigation on the robustness, accuracy and simulation performance of a physics-based deep-submicronmeter BSIM model for analog/digital circuit simulation

Author(s):  
Yuhua Cheng ◽  
Min-Chie Jeng ◽  
Zhihong Liu ◽  
Kai Chen ◽  
Mansun Chan ◽  
...  
1996 ◽  
Vol 2 (4) ◽  
pp. 295-302 ◽  
Author(s):  
BRUCE W. WATSON

Finite automata and various extensions of them, such as transducers, are used in areas as diverse as compilers, spelling checking, natural language grammar checking, communication protocol design, digital circuit simulation, digital flight control, speech recognition and synthesis, genetic sequencing, and Java program verification. Unfortunately, as the number of applications has grown, so has the variety of implementations and implementation techniques. Typically, programmers will be confused enough to resort to their text books for the most elementary algorithms. Recently, advances have been made in taxonomizing algorithms for constructing and minimizing automata and in evaluating various implementation strategies Watson 1995. Armed with this, a number of general-purpose toolkits have been developed at universities and companies. One of these, FIRE Lite, was developed at the Eindhoven University of Technology, while its commercial successor, FIRE Engine II, has been developed at Ribbit Software Systems Inc. Both of these toolkits provide implementations of all of the known algorithms for constructing automata from regular expressions, and all of the known algorithms for minimizing deterministic finite automata. While the two toolkits have a great deal in common, we will concentrate on the structure and use of the noncommercial FIRE Lite. The prototype version of FIRE Lite was designed with compilers in mind. More recently, computation linguists and communications protocol designers have become interested in using the toolkit. This has led to the development of a much more general interface to FIRE Lite, including the support of both Mealy and Moore regular transducers. While such a toolkit may appear extremely complex, there are only a few choices to be made. We also consider a ‘recipe’ for making good use of the toolkits. Lastly, we consider the future of FIRE Lite. While FIRE Engine II has obvious commercial value, we are committed to maintaining a version which is freely available for academic use.


2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Valeria Boscaino ◽  
Antonino Fiannaca ◽  
Laura La Paglia ◽  
Massimo La Rosa ◽  
Riccardo Rizzo ◽  
...  

Abstract Background In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics. Results In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data. Conclusions The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.


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