Computational models of austenite decomposition should provide predictions of external shape and internal microstructure as functions of time and temperature. This information may be used directly, or it can be sent to other simulators in order to make predictions about material working properties. It is often the case that such decomposition models are constructed using kinematic information-i.e. data that relate external shape change to the internal microstructural state. These models are based on the supposition that a differential shape change can be related to a differential microstructural change given that the base state is known. This is a purely kinematic link that does not depend on the rate of decomposition. A starting point is often to estimate shape change using a weighted average of the lattice parameters of each phase present, and this is adopted in the present work. Based on a review of the literature values for lattice parameters associated with low carbon steels, an optimal set of lattice values is obtained. These thermophysical functions are then used in a new forward fitting algorithm to obtain kinetic parameters for an internal state variable model of austenite decomposition. The key idea here is that the kinetic parameters are optimized so as to match dilatometry data without the need to back out any phase fraction data prior to fitting. The model is applied to a class of industrial steels to demonstrate the accuracy of the lattice parameters and the viability of the new forward fitting methodology.