# Reconstruction and Modeling of Dynamical Molecular Networks

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $337,622

## Abstract

Reconstruction and Modeling of Dynamical Molecular Networks: Abstract
Biological networks and their quantitative models can provide mechanistic insights into pathophysiology of
diseases as well as identify potential targets for therapeutic intervention. The quantitative models can be used
for hypotheses generation through simulation of perturbations of key molecules and tested experimentally
through pharmacological or genetic perturbations. This project deals with the development and implementation
of algorithms and methodologies for causal inference, analysis and modeling of molecular and modular networks
from large-scale temporal molecular data incorporating a priori knowledge related to biological pathways and
functions. The dynamical and nonlinear nature of biological systems will be captured through successive linear
models by identifying different temporal regimes in the time-course data. The temporal regimes will be identified
through a change-point detection algorithm. The change-points potentially reflect mechanistic changes in the
biological system. Then, a stable least absolute shrinkage and selection operator approach incorporating partial
least squares will be used to infer the potentially causal networks and develop models for specific pathways. We
will incorporate time-delay in our state-space modeling approach to identify if the data from the past contributes
significantly to prediction of the current value. Since both inference and interpretation of large (causal) molecular
networks from temporal data at the whole-systems level with thousands of components/molecules is prohibitively
challenging, modules corresponding to various biological pathways, mechanisms and functions will be identified
by integrating the quantitative temporal data and a priori biological knowledge. The hub-molecules or centroids
of the modules will serve as state-variables in a reduced-dimensional state-space and they will be used to infer
the networks and develop state-space models. The temporal evolution of the networks across various regimes
will be rigorously analyzed by performing both qualitative and quantitative comparisons of the networks. The
modular networks will also be compared with the corresponding coarse-grained versions of the detailed
molecular networks as internal validation. External validations will include comparison with existing mechanistic
models, if any. The predictive models of the networks will be used to generate experimentally testable
hypotheses regarding temporally specific pharmacological perturbations of key proteins. While these
methodologies will be applicable for many biological systems, in this project they will be applied to two systems,
viz., 1) cell-cycle progression in mouse embryonic fibroblasts, important for the study of molecular mechanisms
of cancer, and 2) differentiation of human induced pluripotent stem cells into neurons, important for the study of
neurodegenerative diseases. The methods will be applied to s...

## Key facts

- **NIH application ID:** 10189695
- **Project number:** 5R01LM012595-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Shankar Subramaniam
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $337,622
- **Award type:** 5
- **Project period:** 2018-08-06 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189695

## Citation

> US National Institutes of Health, RePORTER application 10189695, Reconstruction and Modeling of Dynamical Molecular Networks (5R01LM012595-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10189695. Licensed CC0.

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