# Understanding cascading cellular protein responses following multi-protein stimuli using network modeling and real-world evidence

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $343,907

## Abstract

Project Summary/ Abstract: Drug adverse events have been estimated to contribute to 16% of healthcare
spending in the US, and less than 10% of new treatments reach the clinic, suggesting we need better models
for anticipating drug effects. Intriguingly, cells have multiplexed, and cascading effects in response to stimuli,
such as drug therapies. For instance, a drug stimulus can induce multiple outcomes including modifying disease
symptoms or causing undesirable side-effects, and further, drugs can influence proteins downstream of their
intended targets. Yet, drug therapies are not routinely designed with their multiplexed, cascading effects or multi-
protein binding properties in mind because we lack quantitative mechanistic models for understanding these
interesting cellular effects. Protein-protein interaction (PPI) network models have identified downstream proteins
associated with diseases and side-effects relevant to drug therapies, demonstrating the ability to link multiplexed
outcomes with a stimulus. These associative models are insufficient for prioritizing drug target proteins because
PPI networks lack mechanistic detail to describe the magnitude or relative contribution of cellular responses to
multi-protein stimuli. The overall objective of this research is to derive quantitative relationships between multi-
protein stimuli and downstream response proteins using clinical data. This proposal is innovative because of the
context-specific interaction (CSI) approach: compared to other PPI network approaches, CSI analysis
demonstrated a 50% and 76-95% improvement in prediction accuracy and precision, respectively, when
identifying severe adverse events using PPIs downstream of drug targets and the emphasis on clinical data
integration. This approach emphasized the importance of learning PPI network parameters using phenotype-
specific data to better understand all network-associated phenotypes and demonstrated the feasibility of deriving
mechanistic details in PPI network models. This program is significant because it stands to reduce overall side-
effects and increase therapeutic efficacy by advancing a better understanding of cellular multiplexed responses
to multi-protein stimuli. Further, I have demonstrated consistent productivity and the flexibility and adaptability in
pursuing research projects to establish a distinguished independent career.

## Key facts

- **NIH application ID:** 10500802
- **Project number:** 1R35GM147114-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Jennifer Lynn Wilson
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $343,907
- **Award type:** 1
- **Project period:** 2022-09-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10500802

## Citation

> US National Institutes of Health, RePORTER application 10500802, Understanding cascading cellular protein responses following multi-protein stimuli using network modeling and real-world evidence (1R35GM147114-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10500802. Licensed CC0.

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