title: Extending rule-based methods to model molecular geometry and 3D model resolution.
abstract: Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects.
  
  
  
  We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model.
  
  
  
  We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, 87131, New Mexico, USA.
authors.forename: Brittany
authors.initials: B
authors.lastname: Hoard
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, 87131, New Mexico, USA.
authors.forename: Bruna
authors.initials: B
authors.lastname: Jacobson
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, 87131, New Mexico, USA.
authors.forename: Kasra
authors.initials: K
authors.lastname: Manavi
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, 87131, New Mexico, USA. tapia@cs.unm.edu.
authors.forename: Lydia
authors.initials: L
authors.lastname: Tapia
mesh_terms: D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D008968:Molecular Conformation; D009010:Monte Carlo Method; D011336:Probability; D015398:Signal Transduction
keywords: Antigen-antibody interactions; Geometric model; Molecular assembly; Rule-based model