A 3-D Model of a Perennial Ryegrass Primary Cell Wall and Its Enzymatic Degradation

• Main Authors: Indrakumar Vetharaniam, William J. Kelly, Graeme T. Attwood, Philip J. Harris
• Format: Article
• Language: English
• Published: MDPI AG 2014-05-01
• Series: Computation
• Subjects: plant cell wall; lignocellulose digestion; enzymatic degradation; enzymes; perennial ryegrass (Lolium perenne); rumen; fermentation; agent-based modelling
• Online Access: http://www.mdpi.com/2079-3197/2/2/23

We have developed a novel 3-D, agent-based model of cell-wall digestion to improve our understanding of ruminal cell-wall digestion. It offers a capability to study cell walls and their enzymatic modification, by providing a representation of cellulose microfibrils and non-cellulosic polysaccharides and by simulating their spatial and catalytic interactions with enzymes. One can vary cell-wall composition and the types and numbers of enzyme molecules, allowing the model to be applied to a range of systems where cell walls are degraded and to the modification of cell walls by endogenous enzymes. As a proof of principle, we have modelled the wall of a mesophyll cell from the leaf of perennial ryegrass and then simulated its enzymatic degradation. This is a primary, non-lignified cell wall and the model includes cellulose, hemicelluloses (glucuronoarabinoxylans, 1,3;1,4-β-glucans, and xyloglucans) and pectin. These polymers are represented at the level of constituent monosaccharides, and assembled to form a 3-D, meso-scale representation of the molecular structure of the cell wall. The composition of the cell wall can be parameterised to represent different walls in different cell types and taxa. The model can contain arbitrary combinations of different enzymes. It simulates their random diffusion through the polymer networks taking collisions into account, allowing steric hindrance from cell-wall polymers to be modelled. Steric considerations are included when target bonds are encountered, and breakdown products resulting from enzymatic activity are predicted.