publications

2024

1. If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents

   Ke Yang, Jiateng Liu, John Wu, and 9 more authors

   2024

2023

1. BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

   John Wu, William C. L. Stewart, Ciriyam Jayaprakash, and 1 more author

   npj Systems Biology and Applications, Sep 2023

   Abs

   Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

2. AAAI

   A Reflection on AI Model Selection for Digital Agriculture Image Datasets

   Seth Ockerman, John Wu, Christopher Stewart, and 1 more author

   In 2nd AAAI Workshop on AI for Agriculture and Food Systems , Sep 2023

2022

1. Generalized Method of Moments improves parameter estimation in biochemical signaling models of time-stamped single-cell snapshot data

   Wu John, William CL Stewart, Jayaprakash Ciriyam, and 1 more author

   bioRxiv, Sep 2022

   Abs

   Motivation Ordinary differential equations are commonly used to model the sub-cellular dynamics of average values of proteins and mRNAs. New single-cell technologies provide cell-to-cell differences in protein/mRNA abundances that allow for the evaluation of higher order moments. However, using this additional information to improve parameter estimation can be challenging since the magnitudes of single-cell abundances can vary widely between proteins/mRNA.Results We employ Generalized Method of Moments (GMM) and Particle Swarm Optimization to address the above challenges in mechanistic modeling of signaling kinetics data. Using synthetic data from linear and non-linear models, we show that the proposed method improves parameter estimation and enables construction of approximate confidence intervals. Furthermore, our approach exploits parallel computation to scale with increasing data size and dimensions. We apply our software CyGMM to estimate parameters in a linear ODE model for publicly available longitudinal single-cell cytometry data for CD8+ T cells. Our results demonstrate substantial improvements for modeling data from single-cell cytometry and RNA-seq experiments.Availability We also make freely available our estimation software CyGMM written in C++ on github (https://github.com/jhnwu3/CyGMM).Contact jayajitatgmail.comSupplementary information Supplementary data are available at Bioinformatics online.Competing Interest StatementThe authors have declared no competing interest.

2. A Case for Dataset Specific Profiling

   Seth Ockerman, John Wu, and Christopher Stewart

   Sep 2022