Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations

We present results from an experiment similar to one performed by Packard (1988), in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton's lambda parameter (Langton, 1990), and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near ``critical'' lambda values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with lambda values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to lambda, dynamical-behavior classes, and computation in CA. The main constructive results of our study are identifying the emergence and competition of computational strategies and analyzing the central role of symmetries in an evolutionary system. In particular, we demonstrate how symmetry breaking can impede the evolution toward higher computational capability.Comment: 38 pages, compressed .ps files (780Kb) available ONLY thru anonymous ftp. (Instructions available via `get 9303003' .

Elucidation of Hepatitis C Virus Transmission and Early Diversification by Single Genome Sequencing

A precise molecular identification of transmitted hepatitis C virus (HCV) genomes could illuminate key aspects of transmission biology, immunopathogenesis and natural history. We used single genome sequencing of 2,922 half or quarter genomes from plasma viral RNA to identify transmitted/founder (T/F) viruses in 17 subjects with acute community-acquired HCV infection. Sequences from 13 of 17 acute subjects, but none of 14 chronic controls, exhibited one or more discrete low diversity viral lineages. Sequences within each lineage generally revealed a star-like phylogeny of mutations that coalesced to unambiguous T/F viral genomes. Numbers of transmitted viruses leading to productive clinical infection were estimated to range from 1 to 37 or more (median = 4). Four acutely infected subjects showed a distinctly different pattern of virus diversity that deviated from a star-like phylogeny. In these cases, empirical analysis and mathematical modeling suggested high multiplicity virus transmission from individuals who themselves were acutely infected or had experienced a virus population bottleneck due to antiviral drug therapy. These results provide new quantitative and qualitative insights into HCV transmission, revealing for the first time virus-host interactions that successful vaccines or treatment interventions will need to overcome. Our findings further suggest a novel experimental strategy for identifying full-length T/F genomes for proteome-wide analyses of HCV biology and adaptation to antiviral drug or immune pressures

P04-41. Kinetics of Antibody Neutralization and Viral Evolution Following Envelope Vaccination in SIV-infected Rhesus Monkeys

Poster presentation. Conclusion: Our results indicate that env vaccination is associated with an accelerated development of autologous neutralizing antibodies. These antibodies were focused at least in part on the V1 region of Env, since there was selective pressure in this region of the envelope for the evolution of mutational changes

Classification of HIV-1 Sequences Using Profile Hidden Markov Models

Accurate classification of HIV-1 subtypes is essential for studying the dynamic spatial distribution pattern of HIV-1 subtypes and also for developing effective methods of treatment that can be targeted to attack specific subtypes. We propose a classification method based on profile Hidden Markov Model that can accurately identify an unknown strain. We show that a standard method that relies on the construction of a positive training set only, to capture unique features associated with a particular subtype, can accurately classify sequences belonging to all subtypes except B and D. We point out the drawbacks of the standard method; namely, an arbitrary choice of threshold to distinguish between true positives and true negatives, and the inability to discriminate between closely related subtypes. We then propose an improved classification method based on construction of a positive as well as a negative training set to improve discriminating ability between closely related subtypes like B and D. Finally, we show how the improved method can be used to accurately determine the subtype composition of Common Recombinant Forms of the virus that are made up of two or more subtypes. Our method provides a simple and highly accurate alternative to other classification methods and will be useful in accurately annotating newly sequenced HIV-1 strains