# Reconstructing Kinase Network Dynamics to Predict Stochastic Cell Cycle Fate

> **NIH NIH F31** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $10,827

## Abstract

Abstract- Reconstructing Kinase Network Dynamics to Predict Stochastic Cell Cycle Fate.
 In a genetically identical and clonally-derived population of cells, stochastic gene expression causes
natural cell-to-cell variations in protein expression levels1–8, which causes single cells to exhibit different cell fate
when treated with the same stimuli, which can potentially give rise to a population of drug resistant cells2,9,
impeding cancer treatment. This phenomenon is referred to as natural phenotypic divergence (NPD), and it
arises from how protein expression noise influences the stochastic dynamics of interacting non-linear signaling
networks2,9–11. Both protein expression noise and the non-linear nature of signaling dynamics makes it difficult to
predict how single cells will respond to a perturbation such as, chemotherapeutics or mitogens. Focusing on cell
proliferation responses, we hypothesize that we can predict the timing and probability of cell proliferation
by inferring the dynamic connection architecture of the ERK, JNK and Akt signaling networks, which
ubiquitously control cell cycle entry. By understanding the connection architecture of these pathways, a
causal computational network model can be developed, which can predict the timing and probability of cell
proliferation at the single cell level. By combining this network model with live cell imaging experiments spanning
different breast cancer subtypes, we can evaluate the generality of how kinase networks control cell cycle entry
as well as how cell transformation affects these control systems; which can provide translational insight into
novel signaling targets in cancer, predict how transformed cells respond to chemotherapeutics, and the
development of transient drug resistance.
 To achieve this goal, the following aims are proposed: Aim 1. Generate perturbation imaging time course
data of ERK, JNK, Akt and S-phase entry dynamics for dynamic network model reconstruction. Aim 2. Construct
an ERK-JNK-Akt network model predictive of S-phase entry probability dynamics. Aim 3. Experimentally test
model-based predictions of S-phase entry response in a panel of breast cancer cell lines of varying clinical
subtypes. Live cell imaging will be used to acquire ERK JNK and Akt dynamics, along with S-phase entry
response using kinase translocation reporters (KTR)24,25 and the mCherry-geminin S-phase probe26 respectively.
This dynamic data will serve as input data for dynamic modular responses analysis22,23 (DMRA) which will be
used to construct a causal network model consisting of the empirical interaction strengths between ERK, JNK
and Akt, along with S-phase entry. This network model along with live cell imaging experiments in different breast
cancer subtypes will be used to generate and test model predictions, which provide insight the ubiquity of cell
cycle entry control systems in mammalian cells and how cell transformation affects that control. The model can
provide translational insigh...

## Key facts

- **NIH application ID:** 10228339
- **Project number:** 3F31GM129985-02S1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Alan Dennis Stern
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $10,827
- **Award type:** 3
- **Project period:** 2018-08-01 → 2020-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228339

## Citation

> US National Institutes of Health, RePORTER application 10228339, Reconstructing Kinase Network Dynamics to Predict Stochastic Cell Cycle Fate (3F31GM129985-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10228339. Licensed CC0.

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