Niall Mangan Data-driven mathematical modeling for understanding complex systems

Research Interests

For many biological systems we can write down a comprehensive set of equations that captures all possible mechanisms of interest – diffusion, active transport, chemical reactions of varying forms, "stickiness," etc. However, only a few of these mechanisms are expected to be important at a given time. I develop methods to discover reduced models in a data-driven way by combining statistical and machine-learning approaches with mechanistic modeling. I have a few ongoing projects in this area:
  • Modeling the natural and bioengineered spatial organization of enzymes in cells to enhance throughput of biochemical pathways, especially for the production of biofuels, alternatives to plastic, and other high-value products
  • Discovering the "minimal" biochemical network to describe an organism's metabolism across varying external and developmental conditions
  • Inferring the structure and dynamics of biological networks using methods that bridge the gap between -omic level studies and models for small metabolic or regulatory networks

Selected Publications

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory. Jakobson CM, Tullman-Ercek D, and Mangan NM. Scientific Reports. 2018 May 29;8:8196.

Model selection for dynamical systems via sparse regression and information criteria. Mangan NM, Kutz JN, Brunton SL, and Proctor JL. PRSA: Mathematical, Physical and Engineering Sciences. 2017 August;437(2204):20170009.

A systems-level model reveals that 1,2-Propanediol utilization microcompartments enhance pathway flux through intermediate sequestration. Jakobson CM, Tullman-Ercek D, Slininger MF, and Mangan NM. PLoS Computational Biology. 2017 May 5;13(5):e1005525.

pH determines the energetic efficiency of the cyanobacterial COconcentrating mechanism. Mangan NM, Flamholz A, Hood RD, Milo R, and Savage DF. PNAS. 2016 September 6;113(36):E5354-E5362.

Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics. Mangan NM, Brunton SL, Proctor JL, and Kutz JN. IEEE Transactions on Molecular, Biological and Multi-Scale Communications. 2016 June;2(1):52-63.

View all publications by Niall M. Mangan listed in Google Scholar.