The Building Blocks, Biological Pathways and Networks program has transitioned from Common Fund support.  For more information, please visit https://commonfund.nih.gov/bbpn/index. Please note that since the Building Blocks, Biological Pathways and Networks program is no longer supported by the Common Fund, the program website is being maintained as an archive and will not be updated on a regular basis. 




Maintaining a healthy human body requires an amazing feat of biological teamwork involving many “players” – from individual genes and molecules to entire cells and organs —which orchestrate the many intricate and interconnected biological pathways within the body.  To date, only a few of these pathways have been characterized.  This is largely due to the fact that the technology available for studying the myriad interactions between biological “players” in cells was equivalent to taking a snapshot.  Looking at a moment in time cannot reveal the dynamics of these interactions, which can fluctuate rapidly and vary from cell to cell, but are critical for their function.  The Common Fund's Building Blocks, Biological Pathways, and Networks (BBPN) Program was designed to advance our understanding of how these pathways:

  • are interconnected to form biological networks
  • function normally to maintain health
  • become disturbed and lead to disease, and
  • can be restored to health if they are disrupted

Solving these gaps in knowledge is key to understanding health and treating disease.  The program addressed these roadblocks by supporting the development of new technologies and resources to enable researchers to study, in real time, the molecular events that comprise biological pathways and networks in cells.  Technologies and resources developed through this program catalyzed hundreds of studies of normal and disease-related cellular processes.

The program consisted of three components:

  • National Technology Centers for Networks and Pathways (TCNPs)
  • Metabolomics Technology Development
  • Standards for Proteomics and Assessment of Critical Reagents for Proteomics

National Technology Centers for Networks and Pathways (TCNPs)

The TCNP initiative was designed to address the technological challenges of studying how proteins function normally in biological pathways and networks within cells. 

Prior to the TCNP Initiative, the approaches to "proteomics" – the study of all proteins in a cell -- involved making snapshots of proteins in a cell at a specific moment or in a specific state.  This method was unable to capture the transient interactions of proteins, such as the rapid changes that occur to protein structure, activity, and location while it maintains healthy biological pathways and networks, which is an area that could be targeted in treatment of disease.

The TCNP program was designed to address technology roadblocks in proteomics by fostering the development of new technologies and  approaches for studying, in real time, the actions and interactions of proteins within cells.  The program supported three independent research centers that cooperated in a networked national effort to develop new analytical technologies, methods, reagents, and infrastructure to accelerate the characterization of complex biochemical pathways and networks of protein interactions. They collaborated with biomedical researchers through several mechanisms, providing a synergistic push-pull between technological advancement and biomedical problem solving. The centers ensured broad access to the technologies, methods, and reagents they developed, and provided interdisciplinary academic and peer training for biomedical researchers.  Each center integrated biological, technological, and informatics capabilities, but each focused on different technologies and systems, with corresponding strengths. Cooperation among the centers allowed them to achieve a broader scope of research than would be possible if they functioned independently. 

For more information on the TCNPs, contact Douglas Sheeley, Sc.D., National Institute of General Medical Sciences, (301) 594-9762,  sheeleyd@mail.nih.gov.

Metabolomics Technology Development

The Metabolomics Technology Development initiative focused on the development and refinement of novel technologies to accelerate the study of metabolites – that is, the carbohydrates, lipids, amino acids, and other metabolites – that function in biological pathways and networks in cells. The technologies were intended to address current roadblocks to studying metabolite levels, actions and interactions within a single cell or even a specific part of a single cell change over time and from cell to cell in both health and disease. This initiative was supported initially through the Common Fund, and transitioned to the Institutes for support.

As part of this effort, the NIH collaborated with the National Institute of Standards and Technology (NIST) to develop Metabolites in Human Plasma (SRM 1950), a human plasma pool designed to represent the normal complement of metabolites in a cell – the so called “metabolome.”  This “standard reference material” is intended to provide researchers with a way to evaluate and compare new technologies for measuring cellular metabolites and to improve the reproducibility of measurements within and across technologies.

Standards for Proteomics and Assessment of Critical Reagents for Proteomics

Under this sub-initiative, NIH hosted a workshop on Standards in Proteomics involving experts in analysis, processing, and validation of proteomics data. Workshop participants identified a database for mass spectrometric data as an essential need of the community.For additional information on the meeting...

To follow up on this recommendation, the Common Fund supported the development of the "Peptidome" database, hosted by the National Center for Biotechnology Information (NCBI). Peptidome is a public repository that archives and freely distributes tandem mass spectrometry peptide and protein identification data generated by the scientific community. Several layers of data are captured to promote understanding of the experiment and analysis of the underlying data.

This page last reviewed on January 28, 2016