๐Ÿ“’ Cortassa 2012

Computational modeling of mitochondrial function1



  • biochemistry, cellular, and molecular biology
  • thermodynamic models: agreement with the fundamental principles, lack of mechanistic details
  • Stoichiometric models: large networks of biological processes without any possibility of transient behavior or regulatory interactions
  • Kinetic models: kinetic mechanisms, many parameters to find
  • Modular building of models (piece by piece), each module represents the known or hypothesized kinetic scheme available for that process
    • Allows zooming in and out in a network of processes
    • Spatio-temporal organization
  • The reliability of a computational model
    1. sound physicobiochemical basis
    2. ability to reproduce qualitative and quantitative experimental data
    3. provision of meaningful explanation of the simulated experimental behavior
    4. predictive power


Basic biochemical information

  1. KEGG:
  2. BRENDA:

Repository databases

  1. biomodels
  2. CellML repo
  3. ModelDB (Neurons)

Experimental data

  • to constrain the modulesโ€™ kinetics

Computational tools

  • Matlab , Mathematica, Maple, C++, Python, JAVA, Julia, …


  • Clear identification of the level of organization (molecular, (sub)cellular, (multi)cellular) => SDE, ODE, PDE, ABM, …
  • Identification of the set of processes of interest along with observables (experimental variables)
  • Choice of the kinetic expressions
    • Goldmanโ€“Hodgkinโ€“Katz voltage equation (membrane potential)
    • Fickโ€™s laws (non-charged simple diffusion)
    • (Not mechanistic) power-law formalism, fractal (non-integer) kinetics
  • Using your favorite program to represent the kinetic behaviors to code

Choosing the right set of parameters

  • first hint about parameter values can be obtained from experimental data or literature
  • may need some adjustment since the conditions in which they have been measured may not correspond to physiological ones

The sensitivity of the curve to substrate or effector

  • provide information about ways to modify a module behavior in the fully assembled model when the simulated output differs from experimental data

individual behavior of a module should be compared with experimental data obtained in vitro

  • in vivo experimental data can be obtained for some biological processes,

The modules may be assembled once their individual behavior is proved satisfactory according to the above criteria 1โ€“6

  • which of the variables participating in the equations will become state variables , and which will be adjustable parameters
  • Rate of change = rate of production +/- rate of transport - rate of consumption
  • the rate in ODEs may be subjected to scaling factors to account for volume or buffering effects
  • the format with which to write the differential equations
  • the choice of the integration algorithm
    1. Time scale: ms ~ secs
    2. Stiff ODE solvers
  • Simulate short term first: To watch is whether some state variables take negative or extreme (unphysiological) values. Will provide key insights into which and how (extent and direction of change: increasing or decreasing) parameters may be adjusted to render reasonable simulations of the model behavior.
  • run a model simulation till it reaches a steady state (dx/dt < tolerance), may take a while
  • comparing the model output with experimental data (e.g., steady-state values of variables, or fluxes). => fine-tuning of parameters
  • a model is able to predict if it can simulate a behavior that has not yet been observed. Oscillatory behavior of reduced glutathione, GSH, was first observed in the model, and later confirmed experimentally


  • the physical unit
  • in vitro condition provides an initial guess rather than an accurate figure of a parameter value
  • control analysis to know if the process in question exerts control over the fluxes in the network of interest
  • Or, test by model simulations which consequences bring about a parameter variation representing that activity


  1. Cortassa S, Aon MA. Computational modeling of mitochondrial function. Methods Mol. Biol. 2012;810:311-326. doi:10.1007/978-1-61779-382-0_19. PMC3350335↩︎