# New Computational Systems Biology Methods for Modeling Gene Regulatory Circuits

> **NIH NIH R35** · NORTHEASTERN UNIVERSITY · 2024 · $399,479

## Abstract

Project Summary/Abstract
Title: New Computational Systems Biology Methods for Modeling Gene Regulatory Circuits
One of the biggest challenges in biology is to elucidate complex gene interactions and networks for the purpose
of developing interventions in human disease. Particularly important are those gene networks that control cellular
state transitions (e.g., replicative to quiescent, epithelial to mesenchymal, etc.). Thanks to the emergence of
next-generation sequencing technology, rich data resources are available for mapping gene regulatory
interactions. However, the field still lacks a systems-level understanding of how genes in a network collectively
perform their functions and control cellular state transitions, information that is critical for informed clinical
intervention. The PI’s long-term goal is to design effective computer-aided strategies for predicting therapeutic
interventions by integrating knowledge of gene regulatory networks, genomics data from patients, and systems-
biology model simulations. So far, numerous computational methods have been developed to infer and model
gene regulatory networks. However, they typically suffer from the following issues. First, current approaches are
still ineffective to choose an appropriate set of genes and regulatory interactions in a network to model. Current
approaches infer regulatory relationships based on association of gene expression signals, but generally don't
also consider whether an inferred gene regulatory network can operate as a functional dynamical system driving
expected transitions between the network states. Second, traditional mathematical modeling is hard to be applied
systematically to large systems, because many kinetic parameters are unmeasurable directly from experiments,
especially in vivo. The parameter uncertainty and the potential risk of overfitting in large systems have limited
the predictive power of systems biology. To address these issues, the PI’s research program will develop a suite
of computational systems biology algorithms to construct and model high-quality core regulatory circuits driving
cellular state transitions. We have recently developed enhanced ensemble-based mathematical modeling
algorithms for simulating network behaviors without the need of detailed kinetic parameters. This advance has
allowed an integrated top-down and bottom-up systems-biology modeling, as evident from the PI’s recently
developed network reconstruction and modeling method NetAct and network coarse-graining algorithm
SacoGraci. The PI’s research program will further advance novel technologies of ensemble-based modeling and
their applications to optimize high-quality systems-biology models that capture cellular state transitions. The
algorithms will be benchmarked and refined using in-silico simulated data, publicly available omics data sets,
and data from collaborations, with a focus on cell differentiation in developmental processes and state transitions
in oncogenesis. ...

## Key facts

- **NIH application ID:** 10765492
- **Project number:** 2R35GM128717-06
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** Mingyang Lu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $399,479
- **Award type:** 2
- **Project period:** 2018-08-01 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765492

## Citation

> US National Institutes of Health, RePORTER application 10765492, New Computational Systems Biology Methods for Modeling Gene Regulatory Circuits (2R35GM128717-06). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10765492. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*