Author : Rajesh Krishnan
Publisher :
ISBN 13 :
Total Pages : 147 pages
Book Rating : 4.:/5 (181 download)
Book Synopsis Development of a Modular Software System for Modeling and Analyzing Biological Pathways by : Rajesh Krishnan
Download or read book Development of a Modular Software System for Modeling and Analyzing Biological Pathways written by Rajesh Krishnan and published by . This book was released on 2007 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological pathways provide a comprehensive view of a biological phenomenon, in the form of a network of inter-related reactions or processes. Modeling the biochemical reactions helps in studying and analyzing a biological pathway. This is done through parameter extraction and development of mathematical models of the biological systems. The importance of such modeling lies in the ability to easily perform mathematical mutations and optimizations to achieve a specific result, which can then be duplicated in the laboratory. The ability to control the outputs of biological reactions increases the possibilities for new applications, such as developing crops resistant to infection and bio-engineering drugs for diseases like Hepatitis and HIV-AIDS. Studying random mutations through practical experimentation is time consuming and expensive. Mathematical modeling definitely provides an affordable and convenient virtual experimental platform. However current methods are limited, as they produce results that may be difficult to be reproduced by biologists. The typical results do not address the practical constraints and feasibilities of the proposed mathematical mutation. Hence, there is a definite need for efficient algorithms and software, which not only help study the effect of mutations in a mathematical setting, but also provide practical methods to control biological pathways in a laboratory setting. In this dissertation, we develop an algorithm named Box which addresses this issue. The Box algorithm encompasses all the steps needed, from modeling a pathway to producing the biological controls needed to achieve desired mutations. The Box algorithm can be explained in terms of six logical steps: bio-modeling development language, bio-control database integration, sensitivity analysis, bio-rules formation, output optimization and comparison. The first step, the bio-modeling development language BMDL, is a new type of representation for a biological model. It is a weighted gate representation that gives a template for each reaction type and automatically extracts the ordinary differential equation (ODE) model of the system. The bio-control database integration includes the experimental control of the biological system. The remaining four steps support virtual experimentation to optimize specific outputs. In this dissertation we have applied the Box algorithm to three biological pathways, the phage lambda system, the bioluminescence system and the TNFalpha-mediated NF-kB pathway. With the phage lambda system, our goal was to control the gain of phage lambda considered as a switch. We successfully applied the Box algorithm to produce the desired gain and also output the experimental controls needed to achieve the gain. For the bioluminescence process produced by the V. Fischeri bacterial cells, the Box algorithm was able to substantially increase the luminescence, as expressed by the Lux A/B concentration. It could do so by modifying just four rate parameters, and without the need for any other mechanism such as increasing the population of cells or auto-inducer concentration. The TNFalpha-mediated NF-kB pathway is a complex mechanism controlling cell proliferation and apoptosis. We applied our Box algorithm to improve the NF-kB output of the process. The Box algorithm provided an accurate characterization and optimization of this process. We have successfully demonstrated the application of our Box algorithm for modeling and optimizing biological pathways. The Box algorithm can translate bio-chemical reactions into mathematical models and merge mathematical mutation parameters with biological rules. This capability helps establish a convenient communication channel between the biologist and the modeling software. Our Box algorithm provides results in biological terminology rather than in numeric form. Hence biologists can expect to gain a good understanding of the biological phenomenon and its controlling factors by applying this software prior to practical experimentation.
