The Alpha Project: a model system for systems biology research.

One goal of systems biology is to understand how genome-encoded parts interact to produce quantitative phenotypes. The Alpha Project is a medium-scale, interdisciplinary systems biology effort that aims to achieve this goal by understanding fundamental quantitative behaviours of a prototypic signal transduction pathway, the yeast pheromone response system from Saccharomyces cerevisiae. The Alpha Project distinguishes itself from many other systems biology projects by studying a tightly bounded and well-characterised system that is easily modified by genetic means, and by focusing on deep understanding of a discrete number of important and accessible quantitative behaviours. During the project, the authors have developed tools to measure the appropriate data and develop models at appropriate levels of detail to study a number of these quantitative behaviours. The authors have also developed transportable experimental tools and conceptual frameworks for understanding other signalling systems. In particular, the authors have begun to interpret system behaviours and their underlying molecular mechanisms through the lens of information transmission, a principal function of signalling systems. The Alpha Project demonstrates that interdisciplinary studies that identify key quantitative behaviours and measure important quantities, in the context of well-articulated abstractions of system function and appropriate analytical frameworks, can lead to deeper biological understanding. The authors' experience may provide a productive template for systems biology investigations of other cellular systems.

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

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 10(5) 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.

Toward antiviral strategies that resist viral escape.

We studied the effect on viral growth of drugs targeting different virus functions using a computer simulation for the intracellular growth of bacteriophage T7. We found that drugs targeting components of negative-feedback loops gain effectiveness against mutant viruses that attenuate the drug-target interaction. The greater inhibition of such mutants than of the wild type suggests a drug design strategy that would hinder the development of drug resistance.

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

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 wild-type 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. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 375-389, 1997.