BacillOndex: an integrated data resource for systems and synthetic biology.

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism.

Keratinocyte apoptosis in epidermal remodeling and clearance of psoriasis induced by UV radiation.

Psoriasis is a common chronic skin disorder, but the mechanisms involved in the resolution and clearance of plaques remain poorly defined. We investigated the mechanism of action of UVB, which is highly effective in clearing psoriasis and inducing remission, and tested the hypothesis that apoptosis is a key mechanism. To distinguish bystander effects, equal erythemal doses of two UVB wavelengths were compared following in vivo irradiation of psoriatic plaques; one is clinically effective (311 nm) and one has no therapeutic effect on psoriasis (290 nm). Only 311 nm UVB induced significant apoptosis in lesional epidermis, and most apoptotic cells were keratinocytes. To determine clinical relevance, we created a computational model of psoriatic epidermis. Modeling predicted apoptosis would occur in both stem and transit-amplifying cells to account for plaque clearance; this was confirmed and quantified experimentally. The median rate of keratinocyte apoptosis from onset to cell death was 20 minutes. These data were fed back into the model and demonstrated that the observed level of keratinocyte apoptosis was sufficient to explain UVB-induced plaque resolution. Our human studies combined with a systems biology approach demonstrate that keratinocyte apoptosis is a key mechanism in psoriatic plaques clearance, providing the basis for future molecular investigation and therapeutic development.

Model annotation for synthetic biology: automating model to nucleotide sequence conversion.

MOTIVATION: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biology projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behaviour. The larger model then acts as a blueprint for physical implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their physical manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. RESULTS: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biology Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. AVAILABILITY: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biology/downloads.html.

Evolutionary computation for the design of a stochastic switch for synthetic genetic circuits.

Biological systems are inherently stochastic, a fact which is often ignored when simulating genetic circuits. Synthetic biology aims to design genetic circuits de novo, and cannot therefore afford to ignore the effects of stochastic behavior. Since computational design tools will be essential for large-scale synthetic biology, it is important to develop an understanding of the role of stochasticity in molecular biology, and incorporate this understanding into computational tools for genetic circuit design. We report upon an investigation into the combination of evolutionary algorithms and stochastic simulation for genetic circuit design, to design regulatory systems based on the Bacillus subtilis sin operon.