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author:("Zhou, fleming")
1.  SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling 
International journal of agent technologies and systems  2010;2(3):10.4018/jats.2010070102.
Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.
PMCID: PMC3806198  PMID: 24163721
Agent-Based; Computer Simulation; Framework; Models; SPARK
2.  Flexible Approaches for Teaching Computational Genomics in a Health Information Management Program 
The astonishing improvement of high-throughput biotechnologies in recent years makes it possible to access a huge amount of genomic data. The association between genomic data and genetic disease has already been and will continue to be applied to personalized healthcare. Health information management (HIM) professionals are the ones who will handle personal genetic information and provide solid evidence to support physicians’ diagnoses and personalized treatment strategies, and therefore they will need to have the knowledge and skills to process genomic data. In this paper, we describe flexible approaches for teaching a computational genomics course in the HIM program at the University of Pittsburgh. HIM programs at other universities may choose an appropriate approach to fit into their own curriculum.
PMCID: PMC3709875  PMID: 23861672
education; genomics; flexibility
3.  Identifying the mechanisms of intron gain: progress and trends 
Biology Direct  2012;7:29.
Continued improvements in Next-Generation DNA/RNA sequencing coupled with advances in gene annotation have provided researchers access to a plethora of annotated genomes. Subsequent analyses of orthologous gene structures have identified numerous intron gain and loss events that have occurred both recently and in the very distant past. This research has afforded exceptional insight into the temporal and lineage-specific rates of intron gain and loss among various species throughout evolution. Numerous studies have also attempted to identify the molecular mechanisms of intron gain and loss. However, even after considerable effort, very little is known about these processes. In particular, the mechanism(s) of intron gain have proven exceptionally enigmatic and remain topics of considerable debate. Currently, there exists no definitive consensus as to what mechanism(s) may generate introns. Because many introns are known to affect gene expression, it is necessary to understand the molecular process(es) by which introns may be gained. Here we review the seven most commonly purported mechanisms of intron gain and, when possible, summarize molecular evidence for or against the occurrence of each of these mechanisms. Furthermore, we catalogue indirect evidence that supports the occurrence of each mechanism. Finally, because these proposed mechanisms fail to explain the mechanistic origin of many recently gained introns, we also look at trends that may aid researchers in identifying other potential mechanism(s) of intron gain.
This article was reviewed by Eugene Koonin, Scott Roy (nominated by W. Ford Doolittle), and John Logsdon.
PMCID: PMC3443670  PMID: 22963364
Intron; Intron gain; Intron evolution; Gene structure; Evolution; Mechanism
4.  Mechanisms of intron gain and loss in Drosophila 
It is widely accepted that orthologous genes have lost or gained introns throughout evolution. However, the specific mechanisms that generate these changes have proved elusive. Introns are known to affect nearly every level of gene expression. Therefore, understanding their mechanism of evolution after their initial fixation in eukaryotes is pertinent to understanding the means by which organisms develop greater regulation and complexity.
To investigate possible mechanisms of intron gain and loss, we identified 189 intron gain and 297 intron loss events among 11 Drosophila species. We then investigated these events for signatures of previously proposed mechanisms of intron gain and loss. This work constitutes the first comprehensive study into the specific mechanisms that may generate intron gains and losses in Drosophila. We report evidence of intron gain via transposon insertion; the first intron loss that may have occurred via non-homologous end joining; intron gains via the repair of a double strand break; evidence of intron sliding; and evidence that internal or 5' introns may not frequently be deleted via the self-priming of reverse transcription during mRNA-mediated intron loss. Our data also suggest that the transcription process may promote or result in intron gain.
Our findings support the occurrence of intron gain via transposon insertion, repair of double strand breaks, as well as intron loss via non-homologous end joining. Furthermore, our data suggest that intron gain may be enabled by or due to transcription, and we shed further light on the exact mechanism of mRNA-mediated intron loss.
PMCID: PMC3296678  PMID: 22182367
5.  Adding a Genomic Healthcare Component to a Health Information Management Curriculum 
The inclusion of genomic information will become routine in electronic health records (EHRs). Educating health information management (HIM) students about how to best manage, protect, properly release, and use this information for patient care is of utmost importance. This study examined the usefulness of incorporating genomic modules into an existing course in quality management. Pretest and posttest results showed that students improved in all areas related to genomics in healthcare. Also, students enjoyed the class scenarios and discussion on the ethical use of genomic information. Interspersing genomic information management throughout an existing quality management class is an effective way to add this information to an existing HIM curriculum.
PMCID: PMC2966353  PMID: 21063543
genomic information; pretest-posttest study; quality management; education
6.  Sim4cc: a cross-species spliced alignment program 
Nucleic Acids Research  2009;37(11):e80.
Advances in sequencing technologies have accelerated the sequencing of new genomes, far outpacing the generation of gene and protein resources needed to annotate them. Direct comparison and alignment of existing cDNA sequences from a related species is an effective and readily available means to determine genes in the new genomes. Current spliced alignment programs are inadequate for comparing sequences between different species, owing to their low sensitivity and splice junction accuracy. A new spliced alignment tool, sim4cc, overcomes problems in the earlier tools by incorporating three new features: universal spaced seeds, to increase sensitivity and allow comparisons between species at various evolutionary distances, and powerful splice signal models and evolutionarily-aware alignment techniques, to improve the accuracy of gene models. When tested on vertebrate comparisons at diverse evolutionary distances, sim4cc had significantly higher sensitivity compared to existing alignment programs, more than 10% higher than the closest competitor for some comparisons, while being comparable in speed to its predecessor, sim4. Sim4cc can be used in one-to-one or one-to-many comparisons of genomic and cDNA sequences, and can also be effectively incorporated into a high-throughput annotation engine, as demonstrated by the mapping of 64 000 Fagus grandifolia 454 ESTs and unigenes to the poplar genome.
PMCID: PMC2699533  PMID: 19429899
7.  Universal seeds for cDNA-to-genome comparison 
BMC Bioinformatics  2008;9:36.
To meet the needs of gene annotation for newly sequenced organisms, optimized spaced seeds can be implemented into cross-species sequence alignment programs to accurately align gene sequences to the genome of a related species. So far, seed performance has been tested for comparisons between closely related species, such as human and mouse, or on simulated data. As the number and variety of genomes increases, it becomes desirable to identify a small set of universal seeds that perform optimally or near-optimally on a large range of comparisons.
Using statistical regression methods, we investigate the sensitivity of seeds, in particular good seeds, between four cDNA-to-genome comparisons at different evolutionary distances (human-dog, human-mouse, human-chicken and human-zebrafish), and identify classes of comparisons that show similar seed behavior and therefore can employ the same seed. In addition, we find that with high confidence good seeds for more distant comparisons perform well on closer comparisons, within 98–99% of the optimal seeds, and thus represent universal good seeds.
We show for the first time that optimal and near-optimal seeds for distant species-to-species comparisons are more generally applicable to a wide range of comparisons. This finding will be instrumental in developing practical and user-friendly cDNA-to-genome alignment applications, to aid in the annotation of new model organisms.
PMCID: PMC2375135  PMID: 18215286

Results 1-7 (7)