| Summary: | A variety of techniques used to model metabolic networks are examined, both kinetic (ODE) models and flux balance (FB) models. These models are applied to a case study network describing CO and CO2 metabolism in Clostridium autoethanogenum, bacteria which can produce both ethanol and butanediol from a source of carbon monoxide. ODE and FB methods are also used to model a variety of simpler networks. By comparing the results from these simpler networks, the strengths and weaknesses of each examined method are highlighted, and ultimately, insight is gained into the conclusions that can be drawn from each model. ODE models have commonly been used to model metabolism in both in vivo and in vitro contexts, allowing the dynamic behaviour of wildtype bacteria to be examined, as well as that of mutants. An ODE model is formed for the C. autoethanogenum network. By exploring a range of parameter schemes, the possible long timescale behaviours of the model are fully determined. The model is able to exhibit both steady states, and also states in which metabolite concentrations grow indefinitely in time. By considering the scalings of these concentrations in the long timescale, six different non-steady behaviours are categorised and one steady. For a small range of parameter schemes, the model is able to exhibit both steady and unsteady behaviours in the long timescale, depending on initial conditions. FB methods are also applied to the same network. First flux balance analysis (FBA) is used to model the network in steady state. By imposing a range of constraints on the model, limits on levels of flux in the network that are required for a steady-state are found. In particular, boundaries on the ratio of inputs into the network are calculated, outside of which steady states cannot exist. Comparing the steady state regions predicted by FBA and our ODE model, it is found that the FBA model predicts a wider range of conditions leading to steady state. FBA is only able to observe a network in steady state, so an extension of FBA, known as dynamic flux balance analysis (dFBA), is used to examine non-steady-state behaviours. dFBA predicts similar long term non-steady behaviour to the ODE models, with states in which concentrations of some metabolites are able to grow indefinitely in time. These dFBA states do not precisely match those found by the ODE model, and states that cannot be observed in the ODE model are also found, suggesting that other ODE models for the same network could exhibit different long timescale behaviours. The examples considered clarify the strengths and weaknesses of each approach and the nature of insight into metabolic behaviour each provides.
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