Simulating the Allocation of Organs for Transplantation

The demand for donated organs greatly exceeds supply and many candidates die awaiting transplantation. Policies for allocating deceased donor organs may address equity of access and medical efficacy, but typically must be implemented with incomplete information. Simulation-based analysis can inform the policy process by predicting the likely effects of alternative policies on a wide variety of outcomes of interest. This paper describes a family of simulations developed by the US Scientific Registry of Transplant Recipients and initial experience in the application of one member of this family, the Liver Simulated Allocation Model (LSAM).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45816/1/10729_2004_Article_5277541.pd

A simulation approach to PERT network analysis

This paper presents simulation as a useful analytical tool for project network analysis. Simulation is a powerful tool for evaluating many of the decision parameters involved in project management. A computer program, named STARC, is used to illustrate the effectiveness of computer simulation for project planning. STATGRAPHICS software is used to illustrate some of the post-simulation statistical analyses that can be conducted.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Identification of a Novel ZIC3 Isoform and Mutation Screening in Patients with Heterotaxy and Congenital Heart Disease

Patients with heterotaxy have characteristic cardiovascular malformations, abnormal arrangement of their visceral organs, and midline patterning defects that result from abnormal left-right patterning during embryogenesis. Loss of function of the transcription factor ZIC3 causes X-linked heterotaxy and isolated congenital heart malformations and represents one of the few known monogenic causes of congenital heart disease. The birth incidence of heterotaxy-spectrum malformations is significantly higher in males, but our previous work indicated that mutations within ZIC3 did not account for the male over-representation. Therefore, cross species comparative sequence alignment was used to identify a putative novel fourth exon, and the existence of a novel alternatively spliced transcript was confirmed by amplification from murine embryonic RNA and subsequent sequencing. This transcript, termed Zic3-B, encompasses exons 1, 2, and 4 whereas Zic3-A encompasses exons 1, 2, and 3. The resulting protein isoforms are 466 and 456 amino acid residues respectively, sharing the first 407 residues. Importantly, the last two amino acids in the fifth zinc finger DNA binding domain are altered in the Zic3-B isoform, indicating a potential functional difference that was further evaluated by expression, subcellular localization, and transactivation analyses. The temporo-spatial expression pattern of Zic3-B overlaps with Zic3-A in vivo, and both isoforms are localized to the nucleus in vitro. Both isoforms can transcriptionally activate a Gli binding site reporter, but only ZIC3-A synergistically activates upon co-transfection with Gli3, suggesting that the isoforms are functionally distinct. Screening 109 familial and sporadic male heterotaxy cases did not identify pathogenic mutations in the newly identified fourth exon and larger studies are necessary to establish the importance of the novel isoform in human disease

A DNMT3B Alternatively Spliced Exon and Encoded Peptide Are Novel Biomarkers of Human Pluripotent Stem Cells

A major obstacle in human stem cell research is the limited number of reagents capable of distinguishing pluripotent stem cells from partially differentiated or incompletely reprogrammed derivatives. Although human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) express numerous alternatively spliced transcripts, little attention has been directed at developing splice variant-encoded protein isoforms as reagents for stem cell research. In this study, several genes encoding proteins involved in important signaling pathways were screened to detect alternatively spliced transcripts that exhibited differential expression in pluripotent stem cells (PSCs) relative to spontaneously differentiated cells (SDCs). Transcripts containing the alternatively spliced exon 10 of the de novo DNA methyltransferase gene, DNMT3B, were identified that are expressed in PSCs. To demonstrate the utility and superiority of splice variant specific reagents for stem cell research, a peptide encoded by DNMT3B exon 10 was used to generate an antibody, SG1. The SG1 antibody detects a single DNMT3B protein isoform that is expressed only in PSCs but not in SDCs. The SG1 antibody is also demonstrably superior to other antibodies at distinguishing PSCs from SDCs in mixed cultures containing both pluripotent stem cells and partially differentiated derivatives. The tightly controlled down regulation of DNMT3B exon 10 containing transcripts (and exon 10 encoded peptide) upon spontaneous differentiation of PSCs suggests that this DNMT3B splice isoform is characteristic of the pluripotent state. Alternatively spliced exons, and the proteins they encode, represent a vast untapped reservoir of novel biomarkers that can be used to develop superior reagents for stem cell research and to gain further insight into mechanisms controlling stem cell pluripotency

WeederH: an algorithm for finding conserved regulatory motifs and regions in homologous sequences

BACKGROUND: This work addresses the problem of detecting conserved transcription factor binding sites and in general regulatory regions through the analysis of sequences from homologous genes, an approach that is becoming more and more widely used given the ever increasing amount of genomic data available. RESULTS: We present an algorithm that identifies conserved transcription factor binding sites in a given sequence by comparing it to one or more homologs, adapting a framework we previously introduced for the discovery of sites in sequences from co-regulated genes. Differently from the most commonly used methods, the approach we present does not need or compute an alignment of the sequences investigated, nor resorts to descriptors of the binding specificity of known transcription factors. The main novel idea we introduce is a relative measure of conservation, assuming that true functional elements should present a higher level of conservation with respect to the rest of the sequence surrounding them. We present tests where we applied the algorithm to the identification of conserved annotated sites in homologous promoters, as well as in distal regions like enhancers. CONCLUSION: Results of the tests show how the algorithm can provide fast and reliable predictions of conserved transcription factor binding sites regulating the transcription of a gene, with better performances than other available methods for the same task. We also show examples on how the algorithm can be successfully employed when promoter annotations of the genes investigated are missing, or when regulatory sites and regions are located far away from the genes

Fine-Tuning Enhancer Models to Predict Transcriptional Targets across Multiple Genomes

Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34 Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks