Projects

  • Mapping the early effects of the Huntington’s disease mutation in mice

    3 Bullets: A multi-institute collaboration mapped in high resolution the earliest effects of the Huntington’s disease mutation in mice The study included four different genetic strains of mice which allowed the researchers to observe differences in the rate of mutation-induced changes as a result of genetic background Mapping early HD pathogenesis expands our understanding of early disease transitions and will inform other studies on proximal mechanisms and how to slow…

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  • An Integrated Systems Approach to the Development of Molecular Signature of Biological Relevance for Glioblastoma

    Code (ipython notebooks), html files, data and figures for review of the analysis done for An Integrated Systems Approach to the Development of Molecular Signature of Biological Relevance for Glioblastoma are available for download from GBM-blood-markers-paper.zip [7.

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  • Methanococcus maripaludis reconstruction

    Methanococcus maripaludis is an archaeon capable of generating methane gas from carbon dioxide and hydrogen. We have reconstructed its metabolism to enable better understanding of methanogenesis, particularly for learning ways to harness the process for producing liquid fuels from methane.

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  • Comparative analysis of yeast metabolic network models

    Scientists have been mapping the chemical reactions cells use to grow and manage waste since before enzymes were first identified more than 150 years ago. The model yeast Saccharomyces cerevisiae has one of the most extensively studied metabolic networks, including at least 25 metabolic network models published since 2003.

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  • Systems Biology of the Human Microbiome

    Microbial species interact with one another to create complex communities, the form and function of which have great environmental, industrial, and health impact. These communities and their environments are referred to as the microbiome – microscopic ecosystems present across the globe, extreme environments barely capable of supporting life, and even within host organisms including humans.

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  • A Systems Biology Approach to Preterm Birth

    Pregnancy complications are an important health concern in both developing and developed countries. Globally, up to 10% of pregnancies result in preterm birth, and high-risk pregnancy complications such as preeclampsia occur in 3-5% of all pregnancies.

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  • Clostridium beijerinckii reconstruction

    Clostritidium beijerinckii is an attractive organism for biofuel production because it can naturally produce high yields of butanol from sugars. In order to better understand this process, we have constructed the first genome scale metabolic model of C.

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  • MediaDB

    MediaDB collects chemically defined growth media from primary literature sources for fully sequenced organisms. The Price Lab maintains this database, which places emphasis on organisms with existing genome scale metabolic models.

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  • Methanosarcina barkeri reconstruction

    Updated Methanosarcina barkeri reconstruction In collaboration with Bill Metcalf’s lab at the University of Illinois at Urbana-Champaign, we are sequencing 20 novel Methanosarcina strains. The genomes are being analyzed to learn more about the evolution of metabolic and regulatory networks to identify environmental patterns in their evolution.

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  • Tissue Specific Encyclopedia of Metabolism (TSEM)

    The Price lab is building cell- and tissue-specific metabolic models based on transcriptomic data using our recently developed method –  metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). The key innovation of the mCADRE method is to use gene expression data and metabolic network structure to deterministically rank each reaction from a generic human metabolic map in terms of its importance in a tissue-specific metabolic context.

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  • Methanosarcina acetivorans reconstruction

    Methanosarcina acetivorans reconstruction In collaboration with Bill Metcalf’s lab at the University of Illinois at Urbana-Champaign, we are sequencing 20 novel Methanosarcina strains. The genomes are being analyzed to learn more about the evolution of metabolic and regulatory networks to identify environmental patterns in their evolution.

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  • Measuring the effect of inter-study variability on estimating prediction error

    We have assessed the influence of technical and biological sources of variability in transcriptomic data on predictive performance of molecular signatures learned from these data. Our approach compares two types of validation methods: (1) ordinary randomized validation (RCV), which extracts random subsets of sample data for testing, and (2) inter-study validation (ISV), which excludes an entire study for testing.

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  • ITEP: Integrated Toolkit for Exploration of Pan-genomes

    ITEP, the Integrated Toolkit for Exploration of microbial Pan-genomes, is a suite of scripts and Python libraries for the comparison of microbial genomes. It includes tools for de novo protein family prediction by clustering, ortholog detection, analysis of functional domains, identification of core and variable genes and gene regions, sequence alignments and tree generation, cluster curation, and the integration of cross-genome analysis and metabolic networks for study of metabolic network…

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  • ISSAC: Identification of Structured Signatures and Classifiers

    ISSAC is a multiclass classification tool that can learn molecular signatures from high throughput gene expression data. ISSAC constructs a framework for disease diagnosis: a tree-structured hierarchy of the disease phenotypes under consideration based on agglomerative clustering, and learns panels of binary classifiers corresponding to the nodes and edges of the diagnostic hierarchy.

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  • Honeybee Transcriptional Regulatory Network

    This was a collaborative project between our lab and Dr. Gene Robinson’s behavioral neuroscience lab and involves the application of systems approaches to predict the effect of a dynamic process like behavior on gene expression.

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  • Genetic Co-Occurrence Network across Sequenced Microbes

    We have developed a new phylogenetic approach and used it to identify co-occurrence patterns of orthologous genes across all sequenced microbes (at the time). This approach identifies “correlogs” that co-occur in genomes much more often than would be expected by their relative frequencies, suggesting a functional relationship.

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  • Patterns of spatial expression in the mouse brain

    Spatial expression patterns of neuron-specific genes in the adult mouse brain show remarkably clear, spatially-contiguous, transcriptionally-distinct clusters. Over the past year, we have been quantifying the relationships between these spatial expression patterns and known brain regions.

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  • SNAPR: a bioinformatics pipeline for efficient and accurate RNA-seq alignment and analysis

    The growth of next-generation sequencing data now exceeds the growth rate of storage capacity. Researchers’ ability to analyze these data depends upon bioinformatics tools that are fast, accurate, and easy to use.

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  • Probabilistic Regulation of Metabolism (PROM)

    Transcriptional regulation plays a key role in controlling metabolism and a forefront challenge in modeling organisms today is to build integrated models of regulation and metabolism. Predicting the effect of transcriptional perturbations on the metabolic network can lead to accurate predictions on how genetic mutations and perturbations are translated into flux responses at the metabolic level.

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  • GEMINI: Gene Expression and Metabolism Integrated for Network Inference

    There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding.

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  • Microbial Systems Biology

    Genome-scale models of microorganisms We are interested in driving forward programs related to model-guided cellular engineering efforts.  Genome-scale models provide a powerful resource to guide rational engineering of biological systems for applications in industrial and medical biotechnology.

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  • Genome-scale Metabolic Modeling

    Microbial metabolic models The primary research goal of this project is to pioneer a systems biology approach to build and utilize a predictable-genome scale model for the lesser-characterized organism C. beijerinckii in order to enhance butanol production for use as a chemical feedstock and a second-generation biofuel.

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  • Brain Structure and Function

    Molecular differences between brain regions We have shown that spatial expression patterns of neuron-specific genes in the adult mouse brain show remarkably clear, spatially contiguous, transcriptionally distinct clusters. Specifically, taking advantage of spatial expression data from the Allen Institute, we have applied clustering approaches to reveal that the expression patterns of 170 neuron-specific transcripts revealed strikingly clear and symmetrical signatures for most of the brain’s major subdivisions in the mouse…

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  • Human and Mouse Systems Biology

    Systems Biology and Disease The systems biology approach to study, diagnose, and treat human disease has become a critical and necessary method to combat the disease’s complex nature. High-throughput experimental technologies have enabled the identification of biological components at unprecedented scale, from cells and tissues to genes and proteins.

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  • Top-scoring ‘N’ algorithm

    We have developed a new general formulation of the relative expression algorithm that is geared towards classification using small numbers of measured features (e.g.

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  • Differential Rank Conservation (DIRAC)

    Differential Rank Conservation (DIRAC) is a novel approach for studying gene expression within pathways; this method belongs to a larger family of algorithms designed to identify relative expression (i.e.

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  • Gene Editing

    Gene editing tools such as CRISPR/Cas9 are employed by the Price Lab to further validate and characterize the transcriptional regulatory architecture of the genome.

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  • AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures

    Relative Expression Analysis, which is based on the relative ordering of expression values for a small number of genes, has been shown in several studies to accurately classify between disease phenotypes, cancer subclasses, disease outcomes and diverse human pathologies assayed through blood-borne leukocytes The TSP family of Relative Expression Analysis algorithms possess similar accuracy in classification tasks to other supervised learning based classifiers such as Support Vector Machines; but they…

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  • Top-Scoring Pair and Top-Scoring Triple on the Graphics Processing Unit (GPU)

    We have implemented the relative expression classification algorithms the top-scoring pair  (TSP) and top-scoring triplet (TST) for the graphics processing unit (GPU). The GPU is a specialized hardware most commonly associated with gaming applications.

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  • Dynamic Network Modeling

    Kinetic modeling of Parkinson’s Disease In Parkinson’s disease (PD), the activities of several components of the reactive oxygen species (ROS)-activated regulatory network are altered. This makes the cell susceptible to ROS damage.

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  • Understanding the genetic architecture of Bipolar Disorder

    3 Bullets Bipolar disorder (BD) is a common, severe and recurrent psychiatric disorder with no known cure and substantial morbidity and mortality. Heritable causes contribute up to 80 percent of lifetime risk for BD.

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