Intracellular kinetics of a growing virus: A genetically structured simulation for bacteriophage T7

Abstract

Viruses have evolved to efficiently direct the resources of their hosts toward their own reproduction. A quantitative understanding of viral growth will help researchers develop antiviral strategies, design metabolic pathways, construct vectors for gene therapy, and engineer molecular systems that self-assemble. As a model system we examine here the growth of bacteriophage T7 in Escherichia coli using a chemical-kinetic framework. Data published over the last three decades on the genetics, physiology, and biophysics of phage T7 are incorporated into a genetically structured simulation that accounts for entry of the T7 genome into its host, expression of T7 genes, replication of T7 DNA, assembly of T7 procapsids, and packaging of T7 DNA to finally produce intact T7 progeny. Good agreement is found between the simulated behavior and experimental observations for the shift in transcription capacity from the host to the phage, the initiation times of phage protein synthesis, and the intracellular assembly of both wildtype phage and a fast-growing deletion mutant. The simulation is utilized to predict the effect of antisense molecules targeted to different T7 mRNA. Further, a postulated mechanism for the down regulation of T7 transcription in vivo is quantitatively examined and shown to agree with available data. The simulation is found to be a useful tool for exploring and understanding the dynamics of virus growth at the molecular level.

Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes

“…Previously, we used existing experimental data on T7 development to create a simulation for the infection of a single E. coli BL21 cell by a single wild-type T7 (T7 ϩ ) particle (13). The current simulation (T7v2.5) treats the genome as an ordered array of 74 functional genetic elements.…”

“…Details of the assumptions used to construct T7v2.5 were presented elsewhere (13,24). Notable is that the host cell is well mixed and has a constant volume throughout infection, that nucleoside triphosphates, amino acids, and ribosomes are not limiting, and that DNA packaging is rate limiting for phage particle formation.…”

“…Here, we use a computer simulation for bacteriophage T7 development (13) in conjunction with laboratory experiments to compute, predict, and observe the effect of genome reorganization on T7 development. The simulation treats T7 at the logical scale of a self-replicating unit, from genome entry to production of progeny phage, and is resolved at the level of chemical species.…”

A virtual look at Epstein–Barr virus infection: Simulation mechanism

“…We are so far dissatisfied with our attempts to model EBV infection using differential equations (Duca, unpublished) and difference equations (Shapiro, Delgado-Eckert, unpublished). Moreover, there are reasons to mistrust the spatial homogeneity and well-mixed assumptions that underlie continuous models based on ordinary differential equations (ODEs) [10][11][12], despite the success of such models in immunology and virology [13][14][15][16][17][18][19][20][21]. Disease processes are spatially distributed and it is likely that this spatial distribution is critical in determining the course of infection, as has been argued by many, including [22,23].…”

Boolean monomial control systems

“…To date, most simulations of viral pathogenesis have tended to focus on HIV [94], [88], [87], [78], [82], [46], and employ mathematical models based on differential equations. However, there are reasons to mistrust the spatial homogeneity and well-mixed assumptions that underlie continuous models based on ordinary differential equations [85], [86], [100], despite the success of such models in immunology and virology [95], [19], [88], [80], [81], [39], [13], [27], [82]. Disease processes are spatially distributed.…”