Linda J. Broadbelt Metabolic network analysis and kinetic modeling

Research Interests

Reaction pathway analysis is a powerful tool to design novel routes to chemicals, identify optimal processing conditions, and suggest strategies for catalyst design.   We have developed methods for the assembly of kinetic models of substantive detail to be built that enable the atomic scale to be linked with the process scale.  We have applied our methodology to a wide range of different problems, including production of silicon nanoparticles, biochemical transformations, polymerization and depolymerization, and tropospheric ozone formation.  While the chemistries we have studied are seemingly very disparate, applying a common methodology to study them reveals that there are many features of complex reaction networks that are ubiquitous, and a kinetic modeling framework can be a tool that unifies understanding of chemical and biological catalytic systems.

Our work in the area of biological engineering and systems biology has focused on two specific thrusts.  First, we have developed methods for the identification of novel biochemical pathways.  Enzymes have applications in a wide variety of fields including industrial biotechnology, medicine, and bioremediation. However, despite their importance, the discovery of new and relevant enzymatic activity remains challenging. A massive number of enzymatic reactions have yet to be discovered, but it is often difficult to manually identify potential reactions that are both relevant for a given application and likely to occur. To rectify this problem, we have developed an approach that formulates generalized reaction rules to predict enzymatic reactions which are probable but have not yet been observed.  Second, we have developed approaches to enable kinetic and regulatory modeling of cellular metabolism, which is a major challenge in metabolic engineering and systems biology. Constraint-based stoichiometric modeling greatly aids in characterizing and improving strain designs, but without kinetic information, it is difficult to identify rate-limiting steps and interrogate regulatory behavior. Unfortunately, kinetic parameters derived from in vitro studies of enzymes do not necessarily reflect true in vivo behavior and are often determined under varying experimental conditions. Consequently, a single kinetic model combining these in vitro derived parameters is often unable to resolve experimentally observed in vivo data. The ensemble modeling (EM) framework was previously developed to address these hurdles by sampling kinetic parameters for the entire metabolic network simultaneously and screening them against a single experimental dataset.  Furthermore, the EM method constrains the large kinetic parameter sample space using readily available thermodynamic, stoichiometric, and steady state flux data. However, despite its numerous advantages, EM becomes computationally limiting with increasing network size and complexity. In collaboration with the Tyo laboratory, we have built on the previous developments in EM by optimizing parameter screening techniques and introducing methods to reduce structural model complexity and are leveraging these advances to understand the complex behavior of a variety of biological systems.

Selected Publications

Pickaxe: a Python library for the prediction of novel metabolic reactions. Shebek KM, Strutz J, Broadbelt LJ, and Tyo KEJ. BMC Bioinformatics. 2023 March 22;24:106.

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics. Strutz J, Shebek KM, Broadbelt LJ, and Tyo KEJ. Bioinformatics. 2022 July;38(13):3484-3487.

A Robust Strategy for Sustainable Organic Chemicals Utilizing Bioprivileged Molecules. Shanks BH and Broadbelt LJ. ChemSusChem. 2019 July 5;12(13):2970-2975.

Computational Framework for the Identification of Bioprivileged Molecules. Zhou X, Brentzel ZJ, Kraus GA, Keeling PL, Dumesic JA, Shanks BH, and Broadbelt LJ. ACS Sustainable Chemistry & Engineering. 2019 January 22;7(2):2414-2428.

Acceleration Strategies to Enhance Metabolic Ensemble Modeling Performance. Greene JL, Wäechter A, Tyo KEJ, and Broadbelt LJ. Biophysical Journal. 2017 September 5;113(5):1150-1162.

View all publications by Linda J. Broadbelt listed in the National Library of Medicine (PubMed).