# Stochastic Dynamic MOdeling of Cellular Protein Interactions

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $107,760

## Abstract

Stochastic methods for modeling molecular-protein interactions are entirely new approaches to the
important biological goal of simulating cellular biology in silico. Though great progress has been made in
this direction by computational biologists over the past 15 years, the goal of "siliconizing" cellular molecular
interactions still remains remote. More precisely, the current level of model realism has led to a plateau in
the prediction accuracy of molecular interactions. This motivates the development of novel dynamic models
that incorporate more extensive biological details and can add layers of realism to the simulations.
However, such models are computationally daunting, so that it is critical to develop efficient and accurate
computational methods. The purpose is to augment molecular-level understanding and simulation
of biological interactions. In particular, by exploiting novel developments in stochastic optimi?ation, the
investigators shall significantly improve the prediction of interactions by adding a new dimension of realism.
However, for many practical cases the stochastic objective function will become a high dimensional, nonGaussian,
nonlinear random field that will be computationally very challenging to optimize. This is a hard
problem that the investigators plan to address by developing novel Uncertainty Quantification (UQ)
mathematical theory. The specific aims are to: (i) Develop a compact dynamic surrogate model of the
stochastic objective function that incorporates the molecular structure uncertainty and molecular properties
such as the electrostatic fields by solving the nonlinear Poisson Boltzmann (PB) equation. The stochastic
optimization is solved efficiently with a surrogate model. (ii) Analyze the complex analytic regularity
properties of the solution of the nonlinear Poisson-Boltzmann equation (and the other molecular properties)
with respect to the probabilistic molecular conformation model. (iii) Develop convergence rates of the
surrogate model from the complex analytic regularity with respect to the number of realizations of the
protein structure (computational complexity). Most protein interactions models based on molecular.
structure assume a rigid shape thus leading to erroneous predictions. The investigators propose to
significantly improve the prediction of protein interactions by incorporating dynamic uncertainty of the
molecular conformational shape. The theory and application of UQ to protein interactions is at its infancy.

## Key facts

- **NIH application ID:** 9916770
- **Project number:** 5R01GM131409-03
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Mark Andrew Kon
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $107,760
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9916770

## Citation

> US National Institutes of Health, RePORTER application 9916770, Stochastic Dynamic MOdeling of Cellular Protein Interactions (5R01GM131409-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9916770. Licensed CC0.

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