000			02771nam a2200205Ia 4500
008			210226s2020||||xx |||||||||||||| ||eng||
022			_lphd
041			_aeng
082			_a572.80285378242 _bURO
100			_aUrooj Ainuddin, _eAU
245		0	_aCreating Silicon Mimetics of Genetic Circuits (PhD Thesis)
260			_aKarachi : _bNED University of Engineering and Technology Department of Computer and Information Systems Engineering, _c2020
300			_aXIV, 158 p. _b: ill
504			_aYN
520			_aAbstract : This research delves into the fertile domain of systems biology, where processes taking place in living cells are probed and parallels for them are drawn using the language of mathematics. Lately, researchers have started porting these processes to electronic circuits. This work deals with the study and modeling of biological systems, creating analogues for them in the digital world. It uses the finite state machine FSM) to model genetic mechanisms. It also explores the stochastic nature of these circuits by fashioning them into Markov chains. The research is focused on bistable genetic switches and their conversion to digital machines and Markov chains. Three genetic switches have been used as modeling subjects: the Collins toggle switch, the lambda (λ) switch, and the lac operon. Digital models in the form of finite state machines have been designed to mimic the original switch's response to concentrations of interacting proteins. Functionality of the digital switches has been elaborated with extensive truth tables of the models. Moreover, cellular noise, inherent to all genetic processes, has been incorporated by developing Markov chains :er the switches. Steady state analysis of the Markov transition matrix has been conducted for the models. Agreement in response of the genetic switch and the steady state results validates the proposed design strategy. The motivation for this study is the hope that scientists will be able to transfer processes from the cytoplasmic mix to field-programmable gate arrays (FPGAs), which would not only expand the horizons of systems biology, but will also make inroads in synthetic biology, helping to convert a finite automaton back to a synthetic genetic circuit. The beneficiaries of the work are medical and pharmaceutical researchers engaged in disease identification and drug design. Digital and Markovian modeling paradigms will help in exploration of states of genetic circuits, identification of drug targets, and elaboration of diseased conditions in terms of system variables and probabilities.
650			_aDesign Silicon Mimetic Thesis _9121633
650			_aGenetic Circuit Thesis
650		0	_aComputational Modeling Thesis _9882825
942			_cPHD _2ddc
999			_c364080 _d364080