# Models  and Algorithms for Beta-Barrel Membrane Proteins and Stochastic Networks

> **NIH NIH R35** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2021 · $512,489

## Abstract

Summary
Our project address fundamental problems of structures and mechanisms of protein molecules
and their interaction networks. At the protein level, we will continue our NIGMS supported
studies and focus on A) -Barrel Membrane Proteins (MPs). We will develop models and
computational tools for predicting their structure, understanding their mechanism, and
formulating design of novel MPs. At the network level, we will explore a new research
direction. We will B) develop models and exact algorithms by solving the discrete chemical
master equation (dCME) to compute exact probability landscape and discrete probability
flux of networks for studying stochastic control of cellular behavior. Furthermore, we will
examine how phenotype switching arise in networks, starting from commonly occurring
network motifs and comprehensively characterize the universe of their multistabilities.
In Project A), we will focus on MPs found in the outer membrane of gram-negative bacteria,
eukaryotic mitochondria, and in exotoxins. MPs are involved in fundamental processes such
as transport, translocation, energy metabolism, and apoptosis induction. They are also
important therapeutic targets against infectious diseases. In addition, there are significant
engineering interests in developing MPs as bionanopores for single molecule detection and
other biotech applications. Despite recent progress, our knowledge of MPs is limited: only a
few dozens of structures of non-homologous MPs are known. Importantly, we lack general
understanding of the organizing principles of MPs and their functioning mechanism. We
propose to develop models and algorithms for A1) predicting structures of MPs, A2)
deciphering the mechanism of gating in OmpG, and A3) designing novel MPs with desired
geometry and stability towards broad biotech applications.
Our approach will be based on the reduced state model we developed, the MHIP empirical
potential function we obtained through extensive combinatorial analysis, the (m)DiSGro loop
structure prediction and sampling algorithms, with significant new development.
In Project B), we will focus on the fundamental problem of constructing exact stochastic
probability landscape of networks of interacting molecules. Many important biological reactions
involve only a small copy number of molecules. Stochasticity arising from such low copy events
as well as rare events are important for fundamental processes such as embryonic development,
stem cell differentiation and nongenetic heterogeneity. While the discrete chemical master
equation (dCME) provides a generate framework for understanding stochasticity in mesocopic
systems, many foundational problems remain. Despite significant progress, the exact time-
evolving probability landscapes for many networks of interests are computationally inaccessible,
except for a few simple toy problems (e.g. those with <4 nodes). One has to rely on Gillespie
simulation or approximation of Langevin/Fokker-Planck form...

## Key facts

- **NIH application ID:** 10145723
- **Project number:** 5R35GM127084-04
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Jie Liang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $512,489
- **Award type:** 5
- **Project period:** 2018-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145723

## Citation

> US National Institutes of Health, RePORTER application 10145723, Models  and Algorithms for Beta-Barrel Membrane Proteins and Stochastic Networks (5R35GM127084-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10145723. Licensed CC0.

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