A model reduction approach to the kinetics of the monocarboxylate transporter MCT1 and carbonic anhydrase II

J. Almquist, H. Schmidt, P. Lang, D. Prätzel-Wolters, J. W. Deitmer, M. Jirstrand, and H. M. Becker. The 9th Int. Conf. on Systems Biology, Gothenburg, Sweden, August 2008.

Objective: According to the astrocyte-neuron lactate shuttle hypothesis, monocarboxylate transporters (MCTs) play a crucial role in brain energy metabolism. The monocarboxylate transporter isoform I (MCT1) facilitates transport of lactate and other energetic compounds together with protons across the glial cell membrane. As also reported for other acid/base transporting proteins, the transport activity of MCT1 is increased by the enzyme carbonic anhydrase isoform II (CAII). To describe the transport kinetics of MCT1, and the impact of CAII, we apply a combination of electrophysiological techniques and mathematical tools such as modeling using ordinary differential equations and model reduction.

Results: Model reduction techniques aim at simplifying models to reach an appropriate level of detail for experimental validation. We have explored a range of model reduction assumptions based on substrate binding order and timescale separation. Each assumption resulted in a unique, explicit transport rate expression, constituting a model for the substrate flux of MCT1. Simulations of the different models were compared with experimental data obtained from MCT1-expressing Xenopus oocytes injected with different amounts of CAII. Based on single substrate inhibition experiments we propose a binding mechanism that is ordered and symmetric, and that association, and dissociation, of the proton is the step that limits the turnover-rate of MCT1. The model suggests that CAII increases the effective rate constants of the proton reactions, possibly by working as a proton antenna.

Conclusions: The dependencies and the particular form of the rate expressions obtained by the model reduction served as guidelines in designing experiments that contained the necessary information to discriminate between models. Furthermore, it was insights gained from the modeling and the model reduction that led to an extension of the MCT1-model to also incorporate the effect of CAII. This illustrates that modeling itself is a valuable source of new ideas and hypothesis.


  • Joachim Almquist, Fraunhofer-Chalmers Centre
  • Henning Schmidt, Rostock University
  • Patrick Lang, Fraunhofer-ITWM
  • Dieter Prätzel-Wolters, Fraunhofer-ITWM
  • Joachim W. Deitmer, Fraunhofer-ITWM
  • Mats Jirstrand, Fraunhofer-Chalmers Centre
  • Holger M. Becker, Fraunhofer-ITWM


Photo credits: Nic McPhee