Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

On a novel coalescent model for genome-wide evolution of copy number variations.

Since we are limited in our knowledge of human demographic history and variations of recombination and mutation rates, large-scale computer simulation is a necessary tool in genetics. Here we propose and computationally simulate a model of evolution for unique and segmentally duplicated regions of human genome. Since such segmentally duplicated regions show a complex behaviour of copy number changes, our model will hopefully lead to a better understanding of the evolutionary developments of CNVs, algorithms for associations studies with CNV markers, and finally, for characterising parameters for stochastic diffusion models, describing asymptotic behaviour of evolutionary processes.

A novel approach to multihazard modeling and simulation.

OBJECTIVE: To develop and apply a novel modeling approach to support medical and public health disaster planning and response using a sarin release scenario in a metropolitan environment. METHODS: An agent-based disaster simulation model was developed incorporating the principles of dose response, surge response, and psychosocial characteristics superimposed on topographically accurate geographic information system architecture. The modeling scenarios involved passive and active releases of sarin in multiple transportation hubs in a metropolitan city. Parameters evaluated included emergency medical services, hospital surge capacity (including implementation of disaster plan), and behavioral and psychosocial characteristics of the victims. RESULTS: In passive sarin release scenarios of 5 to 15 L, mortality increased nonlinearly from 0.13% to 8.69%, reaching 55.4% with active dispersion, reflecting higher initial doses. Cumulative mortality rates from releases in 1 to 3 major transportation hubs similarly increased nonlinearly as a function of dose and systemic stress. The increase in mortality rate was most pronounced in the 80% to 100% emergency department occupancy range, analogous to the previously observed queuing phenomenon. Effective implementation of hospital disaster plans decreased mortality and injury severity. Decreasing ambulance response time and increasing available responding units reduced mortality among potentially salvageable patients. Adverse psychosocial characteristics (excess worry and low compliance) increased demands on health care resources. Transfer to alternative urban sites was possible. CONCLUSIONS: An agent-based modeling approach provides a mechanism to assess complex individual and systemwide effects in rare events.

A whole-genome shotgun optical map of Yersinia pestis strain KIM.

Yersinia pestis is the causative agent of the bubonic, septicemic, and pneumonic plagues (also known as black death) and has been responsible for recurrent devastating pandemics throughout history. To further understand this virulent bacterium and to accelerate an ongoing sequencing project, two whole-genome restriction maps (XhoI and PvuII) of Y. pestis strain KIM were constructed using shotgun optical mapping. This approach constructs ordered restriction maps from randomly sheared individual DNA molecules directly extracted from cells. The two maps served different purposes; the XhoI map facilitated sequence assembly by providing a scaffold for high-resolution alignment, while the PvuII map verified genome sequence assembly. Our results show that such maps facilitated the closure of sequence gaps and, most importantly, provided a purely independent means for sequence validation. Given the recent advancements to the optical mapping system, increased resolution and throughput are enabling such maps to guide sequence assembly at a very early stage of a microbial sequencing project.