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1.  The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents 
Bioinformatics  2014;30(16):2367-2374.
Motivation: Currently, there are no ontologies capable of describing both the spatial organization of groups of cells and the behaviors of those cells. The lack of a formalized method for describing the spatiality and intrinsic biological behaviors of cells makes it difficult to adequately describe cells, tissues and organs as spatial objects in living tissues, in vitro assays and in computational models of tissues.
Results: We have developed an OWL-2 ontology to describe the intrinsic physical and biological characteristics of cells and tissues. The Cell Behavior Ontology (CBO) provides a basis for describing the spatial and observable behaviors of cells and extracellular components suitable for describing in vivo, in vitro and in silico multicell systems. Using the CBO, a modeler can create a meta-model of a simulation of a biological model and link that meta-model to experiment or simulation results. Annotation of a multicell model and its computational representation, using the CBO, makes the statement of the underlying biology explicit. The formal representation of such biological abstraction facilitates the validation, falsification, discovery, sharing and reuse of both models and experimental data.
Availability and implementation: The CBO, developed using Protégé 4, is available at and at BioPortal (
Contact: or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4133580  PMID: 24755304
2.  Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model 
PLoS ONE  2011;6(11):e27431.
Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
PMCID: PMC3216957  PMID: 22102894
3.  Multi-Cell Simulations of Development and Disease Using the CompuCell3D Simulation Environment 
Mathematical modeling and computer simulation have become crucial to biological fields from genomics to ecology. However, multi-cell, tissue-level simulations of development and disease have lagged behind other areas because they are mathematically more complex and lack easy-to-use software tools that allow building and running in-silico experiments without requiring in-depth knowledge of programming. This tutorial introduces Glazier-Graner-Hogeweg (GGH) multi-cell simulations and CompuCell3D, a simulation framework that allows users to build, test and run GGH simulations.
PMCID: PMC2739628  PMID: 19399437
Glazier-Graner-Hogeweg model; GGH; CompuCell3D; Python; C++; mitosis; cell growth; cell sorting; chemotaxis; CPM; multi-cell modeling; tissue-level modeling; developmental biology; computational biology
4.  MutDB: update on development of tools for the biochemical analysis of genetic variation 
Nucleic Acids Research  2007;36(Database issue):D815-D819.
Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB (, a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters.
PMCID: PMC2238958  PMID: 17827212
5.  Identification of similar regions of protein structures using integrated sequence and structure analysis tools 
Understanding protein function from its structure is a challenging problem. Sequence based approaches for finding homology have broad use for annotation of both structure and function. 3D structural information of protein domains and their interactions provide a complementary view to structure function relationships to sequence information. We have developed a web site and an API of web services that enables users to submit protein structures and identify statistically significant neighbors and the underlying structural environments that make that match using a suite of sequence and structure analysis tools. To do this, we have integrated S-BLEST, PSI-BLAST and HMMer based superfamily predictions to give a unique integrated view to prediction of SCOP superfamilies, EC number, and GO term, as well as identification of the protein structural environments that are associated with that prediction. Additionally, we have extended UCSF Chimera and PyMOL to support our web services, so that users can characterize their own proteins of interest.
Users are able to submit their own queries or use a structure already in the PDB. Currently the databases that a user can query include the popular structural datasets ASTRAL 40 v1.69, ASTRAL 95 v1.69, CLUSTER50, CLUSTER70 and CLUSTER90 and PDBSELECT25. The results can be downloaded directly from the site and include function prediction, analysis of the most conserved environments and automated annotation of query proteins. These results reflect both the hits found with PSI-BLAST, HMMer and with S-BLEST. We have evaluated how well annotation transfer can be performed on SCOP ID's, Gene Ontology (GO) ID's and EC Numbers. The method is very efficient and totally automated, generally taking around fifteen minutes for a 400 residue protein.
With structural genomics initiatives determining structures with little, if any, functional characterization, development of protein structure and function analysis tools are a necessary endeavor. We have developed a useful application towards a solution to this problem using common structural and sequence based analysis tools. These approaches are able to find statistically significant environments in a database of protein structure, and the method is able to quantify how closely associated each environment is to a predicted functional annotation.
PMCID: PMC1435900  PMID: 16526955
6.  MutDB services: interactive structural analysis of mutation data 
Nucleic Acids Research  2005;33(Web Server issue):W311-W314.
Non-synonymous single nucleotide polymorphisms (SNPs) and mutations have been associated with human phenotypes and disease. As more and more SNPs are mapped to phenotypes, understanding how these variations affect the function and expression of genes and gene products becomes an important endeavor. We have developed a set of tools to aid in the understanding of how amino acid substitutions affect protein structures. To do this, we have annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, if available. We then developed a novel web interface to this data that allows for visualization of the location of these substitutions. We have also developed a web service interface to the dataset and developed interactive plugins for UCSF's Chimera structural modeling tool and PyMOL that integrate our annotations with these sophisticated structural visualization and modeling tools. The web services portal and plugins can be downloaded from and the web interface is at .
PMCID: PMC1160165  PMID: 15980479

Results 1-6 (6)