Scott Christley

Genome sequences of the human body louse and its primary endosymbiont provide insights into the permanent parasitic lifestyle

As an obligatory parasite of humans, the body louse (Pediculus humanus humanus) is an important vector for human diseases, including epidemic typhus, relapsing fever, and trench fever. Here, we present genome sequences of the body louse and its primary bacterial endosymbiont Candidatus Riesia pediculicola. The body louse has the smallest known insect genome, spanning 108 Mb. Despite its status as an obligate parasite, it retains a remarkably complete basal insect repertoire of 10,773 protein-coding genes and 57 microRNAs. Representing hemimetabolous insects, the genome of the body louse thus provides a reference for studies of holometabolous insects. Compared with other insect genomes, the body louse genome contains significantly fewer genes associated with environmental sensing and response, including odorant and gustatory receptors and detoxifying enzymes. The unique architecture of the 18 minicircular mitochondrial chromosomes of the body louse may be linked to the loss of the gene encoding the mitochondrial single-stranded DNA binding protein. The genome of the obligatory louse endosymbiont Candidatus Riesia pediculicola encodes less than 600 genes on a short, linear chromosome and a circular plasmid. The plasmid harbors a unique arrangement of genes required for the synthesis of pantothenate, an essential vitamin deficient in the louse diet. The human body louse, its primary endosymbiont, and the bacterial pathogens that it vectors all possess genomes reduced in size compared with their free-living close relatives. Thus, the body louse genome project offers unique information and tools to use in advancing understanding of coevolution among vectors, symbionts, and pathogens.

Sequencing of Culex quinquefasciatus establishes a platform for mosquito comparative genomics.

Closing the Vector Circle The genome sequence of Culex quinquefasciatus offers a representative of the third major genus of mosquito disease vectors for comparative analysis. In a major international effort, Arensburger et al. (p. 86) uncovered divergences in the C. quinquefasciatus genome compared with the representatives of the other two genera Aedes aegypti and Anopheles gambiae. The main difference noted is the expansion of numbers of genes, particularly for immunity, oxidoreductive functions, and digestive enzymes, which may reflect specific aspects of the Culex life cycle. Bartholomay et al. (p. 88) explored infection-response genes in Culex in more depth and uncovered 500 immune response-related genes, similar to the numbers seen in Aedes, but fewer than seen in Anopheles or the fruit fly Drosophila melanogaster. The higher numbers of genes were attributed partly to expansions in those encoding serpins, C-type lectins, and fibrinogen-related proteins, consistent with greater immune surveillance and associated signaling needed to monitor the dangers of breeding in polluted, urbanized environments. Transcriptome analysis confirmed that inoculation with unfamiliar bacteria prompted strong immune responses in Culex. The worm and virus pathogens that the mosquitoes transmit naturally provoked little immune activation, however, suggesting that tolerance has evolved to any damage caused by replication of the pathogens in the insects. The genome of a third mosquito species reveals distinctions related to vector capacities and habitat preferences. Culex quinquefasciatus (the southern house mosquito) is an important mosquito vector of viruses such as West Nile virus and St. Louis encephalitis virus, as well as of nematodes that cause lymphatic filariasis. C. quinquefasciatus is one species within the Culex pipiens species complex and can be found throughout tropical and temperate climates of the world. The ability of C. quinquefasciatus to take blood meals from birds, livestock, and humans contributes to its ability to vector pathogens between species. Here, we describe the genomic sequence of C. quinquefasciatus: Its repertoire of 18,883 protein-coding genes is 22% larger than that of Aedes aegypti and 52% larger than that of Anopheles gambiae with multiple gene-family expansions, including olfactory and gustatory receptors, salivary gland genes, and genes associated with xenobiotic detoxification.

VectorBase: A Data Resource for Invertebrate Vector Genomics

VectorBase (http://www.vectorbase.org) is an NIAID-funded Bioinformatic Resource Center focused on invertebrate vectors of human pathogens. VectorBase annotates and curates vector genomes providing a web accessible integrated resource for the research community. Currently, VectorBase contains genome information for three mosquito species: Aedes aegypti, Anopheles gambiae and Culex quinquefasciatus, a body louse Pediculus humanus and a tick species Ixodes scapularis. Since our last report VectorBase has initiated a community annotation system, a microarray and gene expression repository and controlled vocabularies for anatomy and insecticide resistance. We have continued to develop both the software infrastructure and tools for interrogating the stored data.

A Topological Analysis of the Open Souce Software Development Community

The fast growth of OSS has increased the interest in studying the composition of the OSS community and its collaboration mechanisms. Moreover, the success of a project may be related to the underlying social structure of the OSS development community. In this paper, we perform a quantitative analysis of Open Source Software developers by studying the entire development community at SourceForge. Statistics and social network properties are explored to find collaborations and the effects of different members in the OSS development community. Small world phenomenon and scale free behaviors are found in the SourceForge development network. These topological properties may potentially explain the success and efficiency of OSS development practices. We also infer from our analysis that weakly associated but contributing co-developers and active users may be an important factor in OSS development.

Human genomes as email attachments

SUMMARY The amount of genomic sequence data being generated and made available through public databases continues to increase at an ever-expanding rate. Downloading, copying, sharing and manipulating these large datasets are becoming difficult and time consuming for researchers. We need to consider using advanced compression techniques as part of a standard data format for genomic data. The inherent structure of genome data allows for more efficient lossless compression than can be obtained through the use of generic compression programs. We apply a series of techniques to James Watsons genome that in combination reduce it to a mere 4MB, small enough to be sent as an email attachment.

VectorBase: a home for invertebrate vectors of human pathogens

VectorBase () is a web-accessible data repository for information about invertebrate vectors of human pathogens. VectorBase annotates and maintains vector genomes providing an integrated resource for the research community. Currently, VectorBase contains genome information for two organisms: Anopheles gambiae, a vector for the Plasmodium protozoan agent causing malaria, and Aedes aegypti, a vector for the flaviviral agents causing Yellow fever and Dengue fever.

Patterns of Mesenchymal Condensation in a Multiscale, Discrete Stochastic Model

Cells of the embryonic vertebrate limb in high-density culture undergo chondrogenic pattern formation, which results in the production of regularly spaced “islands” of cartilage similar to the cartilage primordia of the developing limb skeleton. The first step in this process, in vitro and in vivo, is the generation of “cell condensations,” in which the precartilage cells become more tightly packed at the sites at which cartilage will form. In this paper we describe a discrete, stochastic model for the behavior of limb bud precartilage mesenchymal cells in vitro. The model uses a biologically motivated reaction–diffusion process and cell-matrix adhesion (haptotaxis) as the bases of chondrogenic pattern formation, whereby the biochemically distinct condensing cells, as well as the size, number, and arrangement of the multicellular condensations, are generated in a self-organizing fashion. Improving on an earlier lattice-gas representation of the same process, it is multiscale (i.e., cell and molecular dynamics occur on distinct scales), and the cells are represented as spatially extended objects that can change their shape. The authors calibrate the model using experimental data and study sensitivity to changes in key parameters. The simulations have disclosed two distinct dynamic regimes for pattern self-organization involving transient or stationary inductive patterns of morphogens. The authors discuss these modes of pattern formation in relation to available experimental evidence for the in vitro system, as well as their implications for understanding limb skeletal patterning during embryonic development.

Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

BackgroundSimulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models.ResultsWe construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture.ConclusionsWe demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community.

Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent

BackgroundStochastic effects can be important for the behavior of processes involving small population numbers, so the study of stochastic models has become an important topic in the burgeoning field of computational systems biology. However analysis techniques for stochastic models have tended to lag behind their deterministic cousins due to the heavier computational demands of the statistical approaches for fitting the models to experimental data. There is a continuing need for more effective and efficient algorithms. In this article we focus on the parameter inference problem for stochastic kinetic models of biochemical reactions given discrete time-course observations of either some or all of the molecular species.ResultsWe propose an algorithm for inference of kinetic rate parameters based upon maximum likelihood using stochastic gradient descent (SGD). We derive a general formula for the gradient of the likelihood function given discrete time-course observations. The formula applies to any explicit functional form of the kinetic rate laws such as mass-action, Michaelis-Menten, etc. Our algorithm estimates the gradient of the likelihood function by reversible jump Markov chain Monte Carlo sampling (RJMCMC), and then gradient descent method is employed to obtain the maximum likelihood estimation of parameter values. Furthermore, we utilize flux balance analysis and show how to automatically construct reversible jump samplers for arbitrary biochemical reaction models. We provide RJMCMC sampling algorithms for both fully observed and partially observed time-course observation data. Our methods are illustrated with two examples: a birth-death model and an auto-regulatory gene network. We find good agreement of the inferred parameters with the actual parameters in both models.ConclusionsThe SGD method proposed in the paper presents a general framework of inferring parameters for stochastic kinetic models. The method is computationally efficient and is effective for both partially and fully observed systems. Automatic construction of reversible jump samplers and general formulation of the likelihood gradient function makes our method applicable to a wide range of stochastic models. Furthermore our derivations can be useful for other purposes such as using the gradient information for parametric sensitivity analysis or using the reversible jump samplers for full Bayesian inference. The software implementing the algorithms is publicly available at http://cbcl.ics.uci.edu/sgd

Application of Social Network Analysis to the Study of Open Source Software

Publisher Summary This chapter constructs four social networks for the Open Source Software (OSS) development community at Source Forge. Social network analysis has been used in many research areas to discover the intrinsic mechanisms of social communities by examining the topological properties of the social network formed by relationships between the actors and the groups in those communities. For each social network, number of people are expanded in the network by including the next set of peripheral users as defined by their role in the community, moving from the core project leaders, to the core developers, to the co-developers, and finally out to active users. All the social networks have scale-free properties, and the inclusion of the co-developers and active users triggers the emergence of the small world phenomenon for the social network. The chapter examines how these topological network properties potentially explain the success and efficiency of OSS development practices.