The GAL4/UAS gene expression system is a precise means of targeted gene expression employed to study biological phenomena in Drosophila. A modified GAL4/UAS system can be conditionally regulated using a temporal and regional gene expression targeting (TARGET) system that responds to heat shock induction. However heat shock-related temperature shifts sometimes cause unexpected physiological responses that confound behavioral analyses. We describe here the construction of a drug-inducible version of this system that takes advantage of tissue-specific GAL4 driver lines to yield either RU486-activated LexA-progesterone receptor chimeras (LexPR) or β-estradiol-activated LexA-estrogen receptor chimeras (XVE). Upon induction, these chimeras bind to a LexA operator (LexAop) and activate transgene expression. Using GFP expression as a marker for induction in fly brain cells, both approaches are capable of tightly and precisely modulating transgene expression in a temporal and dosage-dependent manner. Additionally, tissue-specific GAL4 drivers resulted in target gene expression that was restricted to those specific tissues. Constitutive expression of the active PKA catalytic subunit using these systems altered the sleep pattern of flies, demonstrating that both systems can regulate transgene expression that precisely mimics regulation that was previously engineered using the GeneSwitch/UAS system. Unlike the limited number of GeneSwitch drivers, this approach allows for the usage of the multitudinous, tissue-specific GAL4 lines for studying temporal gene regulation and tissue-specific gene expression. Together, these new inducible systems provide additional, highly valuable tools available to study gene function in Drosophila.
Logic programming with the stable model semantics has been proposed as a constraint programming paradigm for solving constraint satisfaction and other combinatorial problems. In such a language one writes function-free logic programs with negation. Such a program is instantiated to a ground program from which the stable models are computed. In this paper, we identify a class of logic programs for which the current techniques in solving SAT problems can be adopted for the computation of stable models efficiently. These logic programs are called 2-literal programs where each rule or constraint consists of at most two literals. Many logic programming encodings of graph-theoretic, combinatorial problems given in the literature fall into the class of 2-literal programs. We show that a 2-literal program can be translated to a SAT instance without using extra variables. We report and compare experimental results on solving a number of benchmarks by a stable model generator and by a SAT solver.
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