PD3 Clouds & Computational Methods

Personal, Dense, Dynamic Data (PD3) Clouds are a central resource of the lab in the support of scientific wellness, P4 healthcare and disease studies. Initial focus has been on the collection genomic, metabolomic, proteomic and gut microbiomic data, along with digital health measures from wearables and lifestyle questionnaires. However, in principal, any longitudinal data collected from individuals can be incorporated PD3 clouds to contribute to the global quantification and understanding of the individual for the purpose of tracking (and intervening in) health trajectories.

PD3 clouds and their use are described in recent seminal publications from the lab:

Lessons Learned as President of the Institute for Systems Biology (2000-2018). Hood LE. Genomics Proteomics Bioinformatics. 2018 Feb;16(1):1-9. doi:10.1016/j.gpb.2018.02.002. PMID: 29496591

A wellness study of 108 individuals using personal, dense, dynamic data clouds.  Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, McDonald DT, Kusebauch U, Moss CL, Zhou Y, Qin S, Moritz RL, Brogaard K, Omenn GS, Lovejoy JC, Hood L. Nat Biotechnol. 2017 Aug;35(8):747-756. doi: 10.1038/nbt.3870. Epub 2017 Jul 17. PMID: 28714965

Featured Projects

  • Patterns of Healthy Aging in the Gut Microbiome 

    Objectives: Characterize ‘healthy/unhealthy aging’ trajectories from the perspective of the gut microbiome across the adult human lifespan. Investigate changes in gut microbiome composition predictive of mortality and longevity in elderly populations. Study the reflection of the identified gut microbiome aging patterns in host physiology, primarily through blood analytes. Summarizing paragraph: Despite considerable progress in understanding the human gut microbiome, very little is known about how gut microbial changes across age…

  • Predicting Cancer Phenotypes

    This project aims to significantly advance our ability to study the complex mechanistic underpinnings of cancer using integrated multi-omic network models. Towards this goal, we are building metabolic networks relevant to cancer biology to serve as a mechanistic basis on which to ground statistical analyses of experimental multi-omic data. This collaborative systems-based approach for integrating and analyzing multi-omic information from regulatory network topology, signaling and metabolic pathways will lead to…

  • TReNa

    The regulation of target genes by their transcription factors is complex and incompletely understood. Multiple signals involving core promoters, distal enhancers, epigenetic controls on chromatin accessibility, stochastic and cooperative binding on different times scales are all involved. Predictive modeling of these processes in fine detail and at scale is beyond our current capabilities. We know, however, that gene regulation usually includes two gross features which, independently, are poor predictors, but…

Show More Projects