Mathematical modelling of tooth demineralisation and pH profiles in dental plaque

Olga Ilie, Mark C.M. van Loosdrecht, Cristian Picioreanu

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


A mathematical model of dental plaque has been developed in order to investigate the processes leading to dental caries. The one-dimensional time-dependent model integrates existing knowledge on biofilm processes (mass transfer, microbial composition, microbial conversions and substrate availability) with tooth demineralisation kinetics. This work is based on the pioneering studies of Dibdin who, nearly two decades ago, build a mathematical model roughly describing the metabolic processes taking place in dental plaque. We extended Dibdin's model with: multiple microbial species (aciduric and non-aciduric Streptococci, Actinomyces and Veillonella), more metabolic processes (i.e., aerobic and anaerobic glucose conversion, low and high glucose uptake affinity pathways, formation and consumption of storage compounds), ion transport by Nernst-Planck equations, and we coupled the obtained pH and chemical component gradients inside the plaque with tooth demineralisation. The new model implementation was complemented with faster and more rigorous numerical methods for the model solution. Model results confirm the protective effect of Veillonella due to lactate consumption. Interestingly, on short term, the storage compounds may not necessarily have a negative effect on demineralisation. Individual feeding patterns can also be easily studied with this model. For example, slow ("social") consumption of sugar-containing drinks proves to be more harmful than drinking the same amount over a short period of time. © 2012 Elsevier Ltd.
Original languageEnglish (US)
Pages (from-to)159-175
Number of pages17
JournalJournal of Theoretical Biology
StatePublished - Sep 21 2012
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General Biochemistry, Genetics and Molecular Biology
  • Modeling and Simulation
  • Applied Mathematics
  • Statistics and Probability
  • General Immunology and Microbiology
  • General Medicine


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