The interest of modelling biological processes with dynamically changing external conditions (temperature, relative humidity, gas conditions) increases. Several modelling approaches are currently available. Among them are approaches like modelling under standard conditions, temperature sum models and dynamic modelling. While the first two approaches require huge simplifications that endanger the applicability of the results, the latter requires a substantial modelling and computational effort. In this paper the often very successful method of temperature sum is improved and enhanced to reflect fundamental insights in biochemical processes. Knowing that reaction rates depend on temperature according to Arrhenius' law, a rate sum calculation for each active process is proposed. While the temperature sum approach is in practice restricted to polynomial models, the rate sum approach allows the building and application of more fundamental and process-oriented models. The method is computationally feasible. Model calculations on simulated data show that this approach is at least equivalent to existing approaches, and often outperforms them in terms of statistical fit (R2adj of over 90%, and often 99.5%). Moreover, it has the major advantage of estimating parameters that have an interpretation in the biochemical reality. Another major advantage is that all the normal rules, techniques and procedures of statistics remain applicable.
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