Stochasticity In Stable Lysogeny And The Bistability And State Transition In Phage ? Genetic Switch

Bistable circuits are ubiquitous in biological systems,and are often of importance as the basic functional module.The CI-Cro circuit in phage ? is one of these most classical representatives.There are two modes in the life cycle of phage ?: In lysogeny,phage ? is latent in its host cell for generations,and CI is the only active protein in this very stable state;In lysis,phage ? lyses the host and generates its own progeny,and Cro is the product of cro,which is the key early lytic gene.The two proteins share the same regulatory region,and regulate the expression autologously and mutually.In order to balance the circuit to maintain the bistable states,feedbacks in the circuit should cooperate well with each other.In proper conditions,the genetic switch from lysogeny to lysis will happen and subsequently following the induction process.This thesis will focus on the balance of bistable circuits,the expression of cI in stable lysogeny and the induction process.In stable lysogeny,although the gene regulatory circuits are identical genetically,and are exposed to homogeneous environment,they can still show remarkable phenotypic difference.To predict how phenotype is shaped,understanding of how each factor contributes is required.During gene expression processes,noise could arise either intrinsically in biochemical processes of gene expression or extrinsically from other cellular processes such as cell growth.In this work,important noise sources in gene expression of phage ? lysogen are quantified using models described by stochastic differential equations.Results show that DNA looping has sophisticated impacts on gene expression noise: When DNA looping provides autorepression,like in wild type,it reduces noise in the system;When the autorepression is defected as it is in certain mutants,DNA looping increases expression noise.We also study how each gene operator affects the expression noise by changing the binding affinity between the gene and the transcription factor systematically.We find that the system shows extraordinarily large noise when the binding affinity is in certain range,which changes the system from mono-expression to bi-expression state.In addition,we find that cell growth causes nonnegligible noise,which increases with gene expression level.When exploring the bistable circuits,we modeled a synthetic phage ? regulatory circuit-TL phage,which consists of a double negative feedback and an additional self-inhibition feedback from the lytic gene.Results show that such network can exhibit two different types of mapping from diagrams to phenotypes: the doublenegative feedback is stronger or the self-inhibition feedback is stronger.For the former case,the system shows bistable behavior and phase transition behavior.Lysogenic gene product is the only repressor to maintain lysogeny.Whereas in the latter case,it is almost the opposite.The system shows monostable behavior and no phase transition is found.Most surprisingly,lytic gene product is also a repressor to maintain lysogeny.Further results show that the expression level of lytic gene during prophage induction can be more complex in the second case than in the first one.Normally,we combine such basic modules in the whole system to process a series of functions.This is based on our expectation to their qualitative behaviors.Our results show that quantitative differences on strength of feedbacks can alter the whole diagram and lead to totally different phenotypes.Results on gene expression noise show large stochasticity in the very stable state of a cell.Combining two parts of results,we found that great variation of consequences can be caused by the stochasticity and the phenotypic differences.When such basic modules are put into large networks,the stochasticity will be amplified during the propagation through the network.More importantly,combining different phenotypes caused by each small module may cause unpredictable behavior of the system.Our work provides insights into the following researches which are relevant to such basic modules and help predict the ensemble system behaviors.