tailieunhanh - Modeling protein interaction networks with monotonic answer set programming
In this paper, we propose the use of monotonic answer set programming (L-ASP) to model protein interaction networks. We study these networks using monotonic answer set programming, all the information represent in a Boolean network and establish a general LASP frame work which describes the network semantics, then we use it to describe a particular case. | ISSN:2249-5789 Mona Gharib et al , International Journal of Computer Science & Communication Networks,Vol 3(4),218-225 Modeling Protein Interaction Networks with Monotonic Answer Set Programming Mona Gharib and Fatima Rajab. Mathematics Department, Faculty of Science, Zagazig University, Zagazig, Egypt. E-mail: mona@ Abstract In this paper, we propose the use of monotonic answer set programming (L-ASP) to model protein interaction networks. We study these networks using monotonic answer set programming, all the information represent in a Boolean network and establish a general LASP frame work which describes the network semantics, then we use it to describe a particular case. Keywords: Logic programming, Answer set programming, Monotonic answer sets, Biological System. 1. INTRODUCTION The rapid development of molecular biology leds to provide a huge amount of experimental biological information, integrating this information into a coherent model is an important task of systems biology [2]. At present working mathematicians and computer scientists together diligently and extensively in this area to facilitate the modeling process. Existing approaches (see . [5,6] for good overviews) can roughly be divided into two groups: quantitative and qualitative ones. Typical quantitative models are built using differential equations. This type of models require specific mathematical skills and a lot of experimental data to build, such as the concentration of different proteins over time, making their construction costly and time-consuming, and qualitative models are used to analyze the dynamics of the system when there is a lack of experimental data, even though this has an impact on the accuracy of the model. However, it was found that simplification has been made in the quality models, such as discrete timing and the absence of concentration dynamics, still allow to model the behavior of a system correctly. Discrete models are one of the cornerstones of .
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