Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data, have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback. To address these problems, this paper presents a method to learn genetic network connectivity which exploits the time series nature of experimental data to achieve better causal predictions. This method first breaks up the data into bins. Next, it determines an initial set of potential influence vectors for each gene based upon the probability of the gene's expression increasing in the next time step. These vectors are then combined to form new vectors with better scores. Finally, these influence vectors are competed against each other to determine the final influence vector for each gene. The result is a directed graph representation of the genetic network's repression and activation connections. Results are reported for several synthetic networks with tight feedback showing significant improvements in recall and runtime over Yu's dynamic Bayesian approach. Promising preliminary results are also reported for an analysis of experimental data for genes involved in the yeast cell cycle.

Electronic design automation (EDA) tools have facilitated the design of ever more complex integrated circuits each year. Synthetic biology would also benefit from the development of genetic design automation (GDA) tools. Existing GDA tools require biologists to design genetic circuits at the molecular level, roughly equivalent to designing electronic circuits at the layout level. Analysis of these circuits is also performed at this very low level. This paper presents the background and issues involved in the development of such a GDA tool for modeling, analysis, and design.

SUMMARY: iBioSim is a tool that supports learning of genetic circuit models, efficient abstraction-based analysis of these models and the design of synthetic genetic circuits. iBioSim includes project management features and a graphical user interface that facilitate the development and maintenance of genetic circuit models as well as both experimental and simulation data records. AVAILABILITY: iBioSim is available for download for Windows, Linux, and MacOS at http://www.async.ece.utah.edu/iBioSim/ CONTACT: [email protected].