title: Spatiotemporal modeling of microbial metabolism.
abstract: BACKGROUND
  Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention.
  
  
  RESULTS
  We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution.
  
  
  CONCLUSIONS
  Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems.
authors.affiliation: Department of Chemical Engineering, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA. jchen3@umass.edu.
authors.forename: Jin
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Chemical Engineering, Process Systems Engineering Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. jagomezr@mit.edu.
authors.forename: Jose A
authors.initials: JA
authors.lastname: Gomez
authors.affiliation: Department of Chemical Engineering, Process Systems Engineering Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. hoeffner@mit.edu.
authors.forename: Kai
authors.initials: K
authors.lastname: Höffner
authors.affiliation: Department of Chemical Engineering, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA. pphalak@umass.edu.
authors.forename: Poonam
authors.initials: P
authors.lastname: Phalak
authors.affiliation: Department of Chemical Engineering, Process Systems Engineering Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. pib@mit.edu.
authors.forename: Paul I
authors.initials: PI
authors.lastname: Barton
authors.affiliation: Department of Chemical Engineering, University of Massachusetts, 240 Thatcher Way, Life Science Laboratories Building, Amherst, MA, 01003, USA. henson@ecs.umass.edu.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Henson
mesh_terms: D018441:Biofilms; D001692:Biological Transport; D003013:Clostridium / Q000166:cytology / Q000235:genetics / Q000378:metabolism* / Q000502:physiology; D004058:Diffusion; D005110:Extracellular Space / Q000378:metabolism; D005285:Fermentation; D023281:Genomics; D006801:Humans; D042541:Intracellular Space / Q000378:metabolism; D008954:Models, Biological; D011550:Pseudomonas aeruginosa / Q000166:cytology / Q000235:genetics / Q000378:metabolism* / Q000502:physiology; D062211:Spatio-Temporal Analysis