PubMed · 13507687
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L KREYBERG. 1957-12-17. Local freezing.. https://doi.org/10.1098/rspb.1957.0079
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We explore how an infected cell's virion production rate can affect the relative fitness of a virus within a host. We perform an invasion analysis, based on an age-structured model of viral dynamics, to derive the within-host relative viral fitness. We find that for chronic infections, in the absence of trade-offs between viral life history stages, natural selection favors viral strains whose virion production rate maximizes viral burst size. We then show how various life history trade-offs such as that between virion production and immune system recognition and clearance of virally infected cells can lead to natural selection favoring production rates lower than the one that maximizes burst size. Our findings suggest that HIV replication rates should vary between cells with different life spans, as has been suggested by recent observation.
The classification of rules may be one of the most fundamental targets in the study of cellular automata. In this paper, we propose a method for achieving such a classification, in which a new quiescent string dominance parameter F, which is orthogonal to lambda, is introduced. For N -neighbor and K -state cellular automata, in the region 1/K<lambda<1-1/K, the maximum F corresponds to the class III rules, and its minimum, to the class II or class I rules. Therefore, transition of the pattern class takes place between them without fail. By using lambda and F, the phase diagram of cellular automata is determined in the (lambda, F ) plane for five-neighbor and four-state cellular automata. The phase diagram indicates that along the F axis, class III rules are distributed in a large F region, while class I and class II rules are, in a small F region, and class IV rules are found in the overlapping region of class II and III rules. These distributions are almost independent of lambda. Along the lambda axis, all four pattern classes are found in the region 0.25<lambda<0.75, and no correlation between pattern class and lambda parameter is observed.
Building an ontology of a defined knowledge domain can help to model an appropriate database structure for the relevant contents. On the other hand, having a comprehensive overview of the knowledge of a certain domain as it may be provided by corresponding databases facilitates building an appropriate ontology, or ontologies with different granularities, which may then provide many additional benefits in handling the stored and in retrieving additional information from heterogenous sources. In this communication, the first steps are reported how we may derive an ontology for the domain of "molecular regulation" from our databases TRANSFAC (transcriptional regulation), TRANSPATH (signal transduction) and CYTOMER (cellular locations).