Kinetics On The Lysogeny/Lysis Switch Of Phageλ And Informatics On The Cell Wall Lytic Enzyme

Two important aspects in the study of biology function of phageλ(bacteriophage lambda)—the kinetic behavior of the lysogeny/lysis swith of phageλand the bioinformatics on cell wall lyric enzymes—are studied and discussed in this thesis.Genetic regulation network plays key role in many biological processes.In order to understand the physical and chemical characteristics of the system,several biochemical reaction models have been proposed to describe the interactions of genetic regulation network.The control of the life cycle of E.coli(Escherichia coli) infected by phageλis a well-known and well-studied paradigm of a genetic regulatory system.During thepast three decades,abundant of quantitative experimental data have been accumulated on the protein-operator interactions of E. coli infected by phageλ.In the meantime,some mathematical models have been presented for simulating the system.In the second part of this thesis,a kinetic model of the interactions between operators and regulators is developed to study the stabilities of genetic states and lysogeny/lysis switch in E.coli infected by phageλ.At first,according to the characteristics of two regulons(CI₂,Cro₂) and RNA polymerase binding to right operon,the interaction of regulons with binding sites are described through a series of biochemical reaction,and forty ordinary differential equations are deduce from acquired biological knowledge of E.coli infected by phageλ.Secondly,the adiabatic approximation is used to simplify the regulatory model.The kinetic evolutions of mRNA and regulator concentrations can be decoupled from the operators' equation. The stability of each state of the system is studied.The computational results show that lysogenic genetic states switch to lytic genetic states through two bifurcationsone is from single stable state to three-point state and the other is from three-point state to single stable state.Then we indicate that the property of the lysogeny/lysis switch satisfies a topological characteristics theorem(Poincare-Hopf theorem) by calculating eigenvalue of each state.Furthermore,the entropy production rate of the system is calculated.We find higher entropy production in multiple singular point and saddle point states as compared with lysogenic and lytic stable focal point states. The lower entropy production rate of lysogenic state is helpful for the explanation of its high biological stability.At last,by considering the cooperative interaction,the influence of the left operators on the lysogeny/lysis switch is discussed briefly.It shows that the cooperativity of the CI₂'s bound to left operators and right operators makes the lysogenic state more stable.When lysis of E.coli infected by phageλhappen,the cell wall lytic enzymes can destroy the cell wall of the bacteria.Then the progenies of phage are released to infect other bacterias.Cell wall lytic enzymes are highly evolved proteins to quickly destroy the bacterial cell wall of bacteria.There are two kinds of cell wall lytic enzymes:endolysins which are phage-coded enzymes and autolysins which are bacteria-coded enzymes.They can specificly attack bonds in the peptidoglycan to kill bacterias.It provides a valuable tool for the biotechnologist,with many applications in medicine,food industry and agriculture.They can be an effective antiinfective in an age of mounting antibiotic resistance.Discriminating cell wall lytic enzymes from non lytic enzymes is a very important task for curing bacterial infections.In the third part of this thesis,based on the characteristics of hydrophobicity and hydrophilicity of cell wall lytic enzymes and amino acid composition,we develop a fisher-discriminant-based classifier to predict cell wall lytic enzymes.Experiments show that 66.7%sensitivity with 88.6% specificity and 80.4%overall accuracy is obtained for discriminating cell wall lytic enzymes from non-lytic enzymes.Furthermore,the method is able to predict endolysin and autolysin with an overall accuracy of 92.9%.At last,endolysins and non-lyric enzymes of phage are discriminated by proposed method.The overall accuracy is 86.8%.Predicted results demonstrated that our method can provide highly useful information for further bacterial control research and drug research.