# Network Modeling

> **NIH NIH P41** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $175,536

## Abstract

V. TR&D3 - Abstract
The single-cell imaging and biochemical data being provided by large-scale projects such as
NIH LINCS highlight the need for models that can predict the dynamics of signaling proteins on
the scale of a whole cell, encompassing potentially millions of individual macromolecules on
timescales of minutes to hours. While TR&D2 made major progress in spatially realistic
simulations of synaptic events and associated dendritic structural changes, as we seek to tackle
problems at higher scales in a diversity of cells, the need to develop scalable approaches, albeit
at lower resolution, has become apparent. In response to these needs, we are proposing a new
TR&D, TR&D3, that will focus on the development of methods and software for development,
management, efficient simulation, and analysis of network models of molecular interactions in
the cell. Because of intrinsic limitations of the standard ordinary differential equation (ODE)
approach in handling biological complexity, we will adopt and further develop rule-based
modeling (RBM) tools, as exemplified by our widely used BioNetGen software, which provides
an ideal foundation for such an effort. RBM encompasses ODE-based dynamics but is also
much broader as it offers important advantages for highly complex systems: an object-oriented
approach to the representation of biomolecules and their interactions that provides intuitive
visualization capabilities, facilitates model annotation and comparison, and potentially supports
simulation at a wide range of spatial resolutions. Network-free stochastic simulation of rule-
based models provides an excellent starting point for further development of highly-efficient
simulation methods capable of addressing the full range of spatial and molecular complexity.
Our network modeling efforts are driven by six of the seven Driving Biomedical Projects and are
tightly integrated with the efforts of the other TR&Ds. We aim to provide mechanistic insights
across multiple scales and in many different cellular contexts, including neurons, immune cells,
and cancer cells. Our aims are to (1) advance RBM technology to develop efficient cell-scale
simulations in BioNetGen and NFsim, (2) further develop RuleBender as an interface to enable
efficient visualization and model building, managing, and analyzing, and (3) to provide a robust
software infrastructure that integrates RBM technology with others developed at MMBioS and
enables broad usage by the community, providing access to Pittsburgh Supercomputing
Center’s Bridges system for high-performance computing (HPC).

## Key facts

- **NIH application ID:** 9990799
- **Project number:** 5P41GM103712-09
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** James Faeder
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $175,536
- **Award type:** 5
- **Project period:** 2012-09-24 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9990799

## Citation

> US National Institutes of Health, RePORTER application 9990799, Network Modeling (5P41GM103712-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9990799. Licensed CC0.

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