The interest of modelling biological processes with dynamically changing ex
ternal conditions (temperature, relative humidity, gas conditions) increase
s. Several modelling approaches are currently available. Among them are app
roaches like modelling under standard conditions, temperature sum models an
d dynamic modelling. While the first two approaches require huge simplifica
tions that endanger the applicability of the results, the latter requires a
substantial modelling and computational effort. In this paper the often ve
ry 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 t
he building and application of more fundamental and process-oriented models
. The method is computationally feasible. Model calculations on simulated d
ata show that this approach is at least equivalent to existing approaches,
and often outperforms them in terms of statistical fit (R-adj(2) of over 90
%, and often 99.5%). Moreover, it has the major advantage of estimating par
ameters that have an interpretation in the biochemical reality. Another maj
or advantage is that all the normal rules, techniques and procedures of sta
tistics remain applicable. (C) 2000 Elsevier Science Ltd. All rights reserv
ed.