Predictive Phenomics Initiative

Project Image

Principal Investigator

Description

The Predictive Phenomics Initiative (PPI), at Pacific Northwest National Laboratory, is tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems

Projects (12)

The research goal of this project is to use stimuli-specific, synthetic nanobodies to target functional mediators without prior knowledge of the response networks or manipulating the biological system.

  1. Datasets

    0

The research objective of this project is to develop an integrative and automated multi-PTM profiling capability with deep proteome coverage.

  1. Datasets

    0

The science objective of this project is to apply structural proteomics technologies to map the molecular interactome.

  1. Datasets

    0

The science objectives of this project are to: Functionally enrich microbial communities and generate multi-omics to correlate biochemical mechanisms to activity. ​ Integrate PhenoProfiling with Thrust Areas 2 and 3 to develop models for phenotype prediction and interspecies interactions.​ Evaluate...

  1. Datasets

    1

The research goal of this project is to develop computational methods to predict cell regulation phenotypes using small molecule and proteome data to understand outcomes in complex biological systems.

  1. Datasets

    0

By developing explainable, predictive metabolic models of individual microbes, we aim to design consortia that convert light and abundant atmospheric gases into high-value molecules through microbial division of labor.

  1. Datasets

    0

We are constructing a streamlined approach to identify phenotype-relevant signatures by integrating various proteomics data. Leveraging protein structures and interaction networks, we will map structural changes and post-translational modifications to identify molecular drivers and subsequently...

  1. Datasets

    0

The research goal of this project is to establish model synthetic microbial communities to understand the rules regulating their biological function in order to utilize them as next generation bioproduction platforms capable of reducing carbon and nitrogen footprints in biomanufacturing processes.

  1. Datasets

    0

The research goal of this project is to develop a biologically informed machine learning (ML) model that integrates datasets from different studies, and leverages current biological knowledge in an automated manner, to improve predictions in biological data analysis.

  1. Datasets

    0

The research goal of this project is to develop new theory and tools that leverage evolutionary perspectives and knowledge of the energetics of reactions to predict the most likely regulation in a given environment. These methods will accelerate exploration, modeling and understanding of cell...

  1. Datasets

    0
Project

The research goal of this project is to build and understand model communities that show carbon storage phenotypes

  1. Datasets

    0

The research goal of this project is to identify and control host functions hijacked during viral infection through use of PNNL ‘omics technologies and modeling capabilities.

  1. Datasets

    0

Project status

Inactive
English
Datasets (1)
Publications (2)