Gene ontology (GO) is a large public database which not only provides a set of controlled biological and biochemical vocabularies (terms) to describe gene products based upon their functions in the cell, but also contains gene annotation data from heterogeneous data sources. In this paper, using a new method to measure the semantic similarity of GO terms, an efficient algorithm is proposed to find genes that have similar biological functions with a given gene. An online tool is then implemented to search the top N genes having similar biological functions with a particular gene within the same or cross different species. Furthermore, various performance enhancement techniques are utilized to reduce the user query response time of the online tool. This tool is available at: http://bioinformatics.cletnson.edu/G-SESAME/Programs/geneTopl.php.

MOTIVATION: Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems. RESULT: In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is extended from the single vertex to the sub-graph, and a formal definition of module in a network is used. A new agglomerative algorithm was developed to identify modules from the network by combining the new module definition with the relative edge order generated by the Girvan-Newman (G-N) algorithm. A JAVA program, MoNet, was developed to implement the algorithm. Applying MoNet to the yeast core protein interaction network from the database of interacting proteins (DIP) identified 86 simple modules with sizes larger than three proteins. The modules obtained are significantly enriched in proteins with related biological process Gene Ontology terms. A comparison between the MoNet modules and modules defined by Radicchi et al. (2004) indicates that MoNet modules show stronger co-clustering of related genes and are more robust to ties in betweenness values. Further, the MoNet output retains the adjacent relationships between modules and allows the construction of an interaction web of modules providing insight regarding the relationships between different functional modules. Thus, MoNet provides an objective approach to understand the organization and interactions of biological processes in cellular systems. AVAILABILITY: MoNet is available upon request from the authors.

MOTIVATION: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. RESULTS: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. AVAILABILITY: The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. SUPPLEMENTARY INFORMATION: http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.

A unified multilevel mixed-mode simulation capability for mixed analog/digital integrated circuits is described. First, a methodology for describing arbitrary analog or mixed analog/digital blocks at the behavioral level is proposed. In order to verify such models, a verification tool has been developed. The tools modgens and modgenz convert transfer functions H(s) and H(z), respectively, into state space representations in the time domain and generate behavioral models. Thus, the methodology allows digital, analog, and mixed analog/digital subcircuits to be described at various levels. While the analog portions of the circuit are simulated with high accuracy, the digital portions can be simulated in various models. Simulation is event-driven. For the behavioral analog models, block elimination with unique reordering and pivoting techniques are used to accommodate state variables. This capability has been integrated into the MOTIS3 design verification system. The simulation of representative mixed analog/digital simulation examples is described.>

The method presented allows digital, analog and mixed D/A portions of a circuit to be described at behavioral, functional, cell, and transistor levels. The analog portions of the circuit are simulated with maximum accuracy while the digital portions are simulated in various modes. M/sup 3/ is event-driven and uses block elimination with unique reordering and pivoting techniques to accommodate state variables for behavioral analog or mixed D/A models. The simulator has been integrated into the MOTIS3 design verification system. A representative mixed D/A simulation example is included.>

The simulator is built on the second-generation MOTIS mixed-mode simulator to accommodate simulation for analog circuits. It uses circuit-simulation techniques for the analog portions of the chip, and timing and logic simulation techniques for the digital portions. The simulator can be used for overall design verification of a chip at the transistor level.>

An efficient and accurate algorithm has been developed for predicting the timing waveforms of general MOS transistor circuits. The algorithm uses a switch-level simulation technique to determine the steady-state conditions, a forward prediction method to predict the transient time between two adjacent voltage levels, a simplified timing simulation technique to correct this delay, and novel approaches for controlling the voltage step automatically. The simulation is further speeded up by the use of a table lookup model to calculate the transistor current and macromodels to evaluate certain circuit structures. This algorithm has been implemented as a new simulation mode, called fast timing, in the MOTIS3 multilevel mixed-mode simulator. Many production chips have been verified, and the results show that the fast-timing simulation can be three orders of magnitude faster than a conventional circuit analysis program such as SPICE, one order of magnitude faster than the MOTIS3 timing simulation, and only five times slower than the MOTIS3 unit-delay, switch-level evaluator. The delay accuracy of the new simulator is within 5 percent of the timing simulation.