Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

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Optimizing agent-based transmission models for infectious diseases

Background

Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations.

Results

We have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26 % up to more than 70 %. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference.

Conclusions

Investment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0612-2) contains supplementary material, which is available to authorized users.     

Pattern, growth, and control

Systems biology seeks not only to discover the machinery of life but to understand how such machinery is used for control, i.e., for regulation that achieves or maintains a desired, useful end. This sort of goal-directed, engineering-centered approach also has deep historical roots in developmental biology. Not surprisingly, developmental biology is currently enjoying an influx of ideas and methods from systems biology. This Review highlights current efforts to elucidate design principles underlying the engineering objectives of robustness, precision, and scaling as they relate to the developmental control of growth and pattern formation. Examples from vertebrate and invertebrate development are used to illustrate general lessons, including the value of integral feedback in achieving set-point control; the usefulness of self-organizing behavior; the importance of recognizing and appropriately handling noise; and the absence of "free lunch." By illuminating such principles, systems biology is helping to create a functional framework within which to make sense of the mechanistic complexity of organismal development.     

Distinguishing between turnover and nestedness in the quantification of biotic homogenization

Compositional changes through local extinction and colonization are inherent to natural communities, but human activities are increasingly influencing the rate and nature of the species being lost and gained. Biotic homogenization refers to the process by which the compositional similarity of communities increases over time through a non-random reshuffling of species. Despite the extensive conceptual development of the homogenization framework, approaches to quantify patterns of homogenization are scarcely developed. Most studies have used classical dissimilarity indices that actually quantify two components of compositional variation: turnover and nestedness. Here we demonstrate that a method that partitions those two components reveals patterns of homogenization that are otherwise obscured using traditional techniques. The forest understorey vegetation of an unmanaged reserve was recorded in permanent plots in 1979 and 2009. In only thirty years, the local species richness significantly decreased and the variation in the species composition from site to site shifted towards a structure with reduced true species turnover and increased dissimilarity due to nestedness. A classic analysis masked those patterns. In summary, we illustrated the need to move beyond the simple quantification of homogenization using classical indices and advocate integration of the multitude of ways to quantify community similarity into the homogenization framework.     

A landscape of driver mutations in melanoma

Despite recent insights into melanoma genetics, systematic surveys for driver mutations are challenged by an abundance of passenger mutations caused by carcinogenic UV light exposure. We developed a permutation-based framework to address this challenge, employing mutation data from intronic sequences to control for passenger mutational load on a per gene basis. Analysis of large-scale melanoma exome data by this approach discovered six novel melanoma genes (PPP6C, RAC1, SNX31, TACC1, STK19, and ARID2), three of which-RAC1, PPP6C, and STK19-harbored recurrent and potentially targetable mutations. Integration with chromosomal copy number data contextualized the landscape of driver mutations, providing oncogenic insights in BRAF- and NRAS-driven melanoma as well as those without known NRAS/BRAF mutations. The landscape also clarified a mutational basis for RB and p53 pathway deregulation in this malignancy. Finally, the spectrum of driver mutations provided unequivocal genomic evidence for a direct mutagenic role of UV light in melanoma pathogenesis.