Delegation, Arbitration and High-Level Service Discovery as Key Elements of a Software Infrastructure for Pervasive Computing

The dream of pervasive computing is slowly becoming a reality. A number of projects around the world are constantly contributing ideas and solutions that are bound to change the way we interact with our environments and with one another. An essential component of the future is a software infrastructure that is capable of supporting interactions on scales ranging from a single physical space to intercontinental collaborations. Such infrastructure must help applications adapt to very diverse environments and must protect people's privacy and respect their personal preferences. In this paper we indicate a number of limitations present in the software infrastructures proposed so far (including our previous work). We then describe the framework for building an infrastructure that satisfies the abovementioned criteria. This framework hinges on the concepts of delegation, arbitration and high-level service discovery. Components of our own implementation of such an infrastructure are presented.

Analysis of the elements of catabolite repression in Clostridium acetobutylicum.

The ptsH gene, encoding the phosphotransferase protein HPr, from Clostridium acetobutylicum ATCC 824 was identified from the genome sequence, cloned and shown to complement a ptsH mutant of Escherichia coli. The deduced protein sequence shares significant homology with HPr proteins from other low-GC gram-positive bacteria, although the highly conserved sequence surrounding the Ser-46 phosphorylation site is not well preserved in the clostridial protein. Nevertheless, the HPr was phosphorylated in an ATP-dependent manner in cell-free extracts of C. acetobutylicum. Furthermore, purified His-tagged HPr from Bacillus subtilis was also a substrate for the clostridial HPr kinase/phosphorylase. This phosphorylation reaction is a key step in the mechanism of carbon catabolite repression proposed to operate in B. subtilis and other low-GC gram-positive bacteria. Putative genes encoding the HPr kinase/phosphorylase and the other element of this model, namely the catabolite control protein CcpA, were identified from the C. acetobutylicum genome sequence, suggesting that a similar mechanism of carbon catabolite repression may operate in this industrially important organism.

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs.

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.