Significance: 'A simple method for modeling amyloid kinetics featuring position biased fiber breakage' A kinetic model of amyloid formation is presented based on two inter-related ordinary differential equations. The model can simply account for different rates of breakage at the fiber ends vs. internal regions. In the former case, breakage releases a monomer unable to maintain its amyloid structure. In the latter case, breakage produces two smaller fibers each able to act as 'seeds' able to facilitate amyloid growth. Thus position dependent susceptibility to breakage could determine whether breakage encourages or discourages amyloid growth. These outcomes are crucial when considering the role amyloid growth plays in disease aetiology, such as for Alzheimer's disease.Abstract: A mathematical model of amyloid fiber formation is described that is able to simply specify different rates of fiber breakage at internal versus end regions. This Note presents the derivation of the relevant equations and provides results showing the dramatic effects of position biased fiber breakage on the kinetics of amyloid growth.Protein helical polymerization reactions, such as those constituted by actin and tubulin assembly, have been a much studied topic in biophysics [1,2]. Amyloid, a fibril-like homopolymer capable of being formed from many different protein monomer building blocks, has been shown to exhibit both helical polymerization kinetics and steady state behavior capable of quantitative description using helical polymerization models of Oosawa and colleagues [3][4][5][6]. A number of important extensions to these models been developed over the last twenty years to better describe amyloid formation [7][8][9][10][11][12][13][14]. These extensions are based on a consensus chemical schema (Fig. 1) featuring the following three general processes, (i.) Reversible fiber nucleation; (ii.) Fiber growth and dissolution via monomer addition and loss; and (iii) Fiber breakage and joining. A simple kinetic description containing some aspects of the consensus mechanism was developed by Smith et al. [15] who employed an approach cast in terms of average quantities. Although providing less information about the time dependent evolution of the polymer distribution, this reduced model proved particularly simple to code and produced easily interpretable output. However, due to the approximations involved in its derivation, the Smith et al. [15]