Phage development depends not only upon phage functions but also on the physiological state of the host, characterized by levels and activities of host cellular functions. We establishedEscherichia coliat different physiological states by continuous culture under different dilution rates and then measured its production of phage T7 during a single cycle of infection. We found that the intracellular eclipse time decreased and the rise rate increased as the growth rate of the host increased. To develop mechanistic insight, we extended a computer simulation for the growth of phage T7 to account for the physiology of its host. Literature data were used to establish mathematical correlations between host resources and the host growth rate; host resources included the amount of genomic DNA, pool sizes and elongation rates of RNA polymerases and ribosomes, pool sizes of amino acids and nucleoside triphosphates, and the cell volume. The in silico (simulated) dependence of the phage intracellular rise rate on the host growth rate gave quantitatively good agreement with our in vivo results, increasing fivefold for a 2.4-fold increase in host doublings per hour, and the simulated dependence of eclipse time on growth rate agreed qualitatively, deviating by a fixed delay. When the simulation was used to numerically uncouple host resources from the host growth rate, phage growth was found to be most sensitive to the host translation machinery, specifically, the level and elongation rate of the ribosomes. Finally, the simulation was used to follow how bottlenecks to phage growth shift in response to variations in host or phage functions.

We created a simulation based on experimental data from bacteriophage T7 that computes the developmental cycle of the wild-type phage and also of mutants that have an altered genome order. We used the simulation to compute the fitness of more than 10⁵ mutants. We tested these computations by constructing and experimentally characterizing T7 mutants in which we repositioned gene 1, coding for T7 RNA polymerase. Computed protein synthesis rates for ectopic gene 1 strains were in moderate agreement with observed rates. Computed phage-doubling rates were close to observations for two of four strains, but significantly overestimated those of the other two. Computations indicate that the genome organization of wild-type T7 is nearly optimal for growth: only 2.8% of random genome permutations were computed to grow faster, the highest 31% faster, than wild type. Specific discrepancies between computations and observations suggest that a better understanding of the translation efficiency of individual mRNAs and the functions of qualitatively “nonessential” genes will be needed to improve the T7 simulation. In silico representations of biological systems can serve to assess and advance our understanding of the underlying biology. Iteration between computation, prediction, and observation should increase the rate at which biological hypotheses are formulated and tested.

Although our knowledge of HIV-1 growth, from a molecular mechanistic perspective, has rapidly increased, we do not yet know how the overall growth rate of HIV-1 depends on its constituent biochemical reactions. Such an understanding would be of fundamental importance and potentially useful for designing and evaluating anti-HIV strategies. As a first step toward addressing this need we formulate and implement here a global computer simulation for the intracellular growth of HIV-1 on a CD4+ T lymphocyte. Our simulation accounts for the kinetics of reverse transcription, integration of proviral DNA into the host genome, transcription, mRNA splicing and transport from the nucleus, translation, feedback of regulatory proteins to the nucleus, transport of viral proteins to the cell membrane, particle assembly, budding, and maturation. The simulation quantitatively captures the experimentally observed intracellular dynamics of viral DNA, mRNA, and proteins while employing no "fudge factors." Moreover, it provides an estimate of the intracellular growth rate of HIV-1 and enables evaluation of mono- and combined anti-HIV strategies.