Simulating Anti-adhesive and Antibacterial Bifunctional Polymers for Surface Coating using BioScape

Traditionally biomaterials development consists of designing a surface and testing its properties experimentally. This trial-and-error approach is limited because of the resources and time needed to sample a representative number of configurations in a combinatorially complex scenario. Therefore, computational modeling is of significant importance in identifying best antibacterial materials to prevent and treat implant related biofilm infections. In this paper we focus on bifunctional surface with polymer brushes and Pluronic-Lysozyme conjugates developed by Henk Busscher's group in Groningen, The Netherlands. The bifunctional brushes act as anti-adhesive due to the unmodified polymer brushes and antibacterial, because of the Pluronic-Lysozyme conjugates. They developed and studied three different surfaces with varying proportions of antibacterial and anti-adhesive properties. In order to aid the development of optimal bifunctional surfaces, we build a three dimensional computational model using BioScape, an agent-based modeling and simulation language developed by Compagnoni's group at Stevens. We model two different experimental phases: adhesion and growth. We use the results of experiments on two surfaces as training data, and we validate our model by reproducing the experimental results from the third surface. The resulting model is able to simulate varying configurations of surface coatings both at adhesion and growth phases at a fraction of the time necessary to perform in-vitro experiments. The output of the model not only plots populations over time, but it also produces 3D-rendered videos of bacteria-surface interactions enhancing the visualization of the system's behavior.

[1]  Corrado Priami,et al.  Application of a stochastic name-passing calculus to representation and simulation of molecular processes , 2001, Inf. Process. Lett..

[2]  Ezio Bartocci,et al.  Shape Calculus. A Spatial Mobile Calculus for 3D Shapes , 2010, Sci. Ann. Comput. Sci..

[3]  Russ B. Altman,et al.  Research Paper: Using Petri Net Tools to Study Properties and Dynamics of Biological Systems , 2004, J. Am. Medical Informatics Assoc..

[4]  Adelinde M. Uhrmacher,et al.  Spatial modeling in cell biology at multiple levels , 2010, Proceedings of the 2010 Winter Simulation Conference.

[5]  Jeremy T. Bradley,et al.  Spatial extension of stochastic Pi calculus , 2009 .

[6]  Nicolas Le Novère,et al.  STOCHSIM: modelling of stochastic biomolecular processes , 2001, Bioinform..

[7]  Jane Hillston,et al.  Bio-PEPA: A framework for the modelling and analysis of biological systems , 2009, Theor. Comput. Sci..

[8]  Maria Grazia Vigliotti,et al.  BAM: BioAmbient machine , 2008, 2008 8th International Conference on Application of Concurrency to System Design.

[9]  Marcus J Schultz,et al.  Biomaterial-Associated Infection: Locating the Finish Line in the Race for the Surface , 2012, Science Translational Medicine.

[10]  Livio Bioglio,et al.  BioScape: A Modeling and Simulation Language for Bacteria-Materials Interactions , 2013, CS2Bio.

[11]  Karin Sauer,et al.  The genomics and proteomics of biofilm formation , 2003, Genome Biology.

[12]  Ole Lund,et al.  Immune system simulation online , 2011, Bioinform..

[13]  Cristian Picioreanu,et al.  Multi-scale individual-based model of microbial and bioconversion dynamics in aerobic granular sludge. , 2007, Environmental science & technology.

[14]  Massimo Bernaschi,et al.  C-ImmSim : playing with the immune response , 2004 .

[15]  Andreas Herrmann,et al.  Pluronic-lysozyme conjugates as anti-adhesive and antibacterial bifunctional polymers for surface coating. , 2011, Biomaterials.

[16]  B. Kholodenko Cell-signalling dynamics in time and space , 2006, Nature Reviews Molecular Cell Biology.

[17]  Peter Tang,et al.  Dynamic cellular automata : an alternative approach to cellular simulation , 2007 .

[18]  Carla Renata Arciola,et al.  The significance of infection related to orthopedic devices and issues of antibiotic resistance. , 2006, Biomaterials.

[19]  Yan‐Yeung Luk,et al.  Anti-fouling chemistry of chiral monolayers: enhancing biofilm resistance on racemic surface. , 2011, Langmuir : the ACS journal of surfaces and colloids.

[20]  Yashwant Gupta,et al.  Biofilms--a microbial life perspective: a critical review. , 2007, Critical reviews in therapeutic drug carrier systems.

[21]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[22]  Luca Cardelli,et al.  Efficient, Correct Simulation of Biological Processes in the Stochastic Pi-calculus , 2007, CMSB.

[23]  Mariangiola Dezani-Ciancaglini,et al.  Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions , 2012, MeCBIC.

[24]  Andreas F Widmer,et al.  Infections associated with orthopedic implants , 2006, Current opinion in infectious diseases.

[25]  M. Parsek,et al.  Quorum sensing and microbial biofilms. , 2008, Current topics in microbiology and immunology.

[26]  Chris North,et al.  PathSim visualizer: an Information-Rich Virtual Environment framework for systems biology , 2004, Web3D '04.

[27]  Adriana B. Compagnoni,et al.  Computational Modeling for the Activation Cycle of G-proteins by G-protein-coupled Receptors , 2010, MeCBIC.

[28]  Johannes S. Vrouwenvelder,et al.  Modeling the effect of biofilm formation on reverse osmosis performance: Flux, feed channel pressure drop and solute passage , 2010 .

[29]  Mathias John,et al.  A Spatial Extension to the pi Calculus , 2008, Electron. Notes Theor. Comput. Sci..

[30]  Vincent Danos,et al.  Rule-Based Modelling of Cellular Signalling , 2007, CONCUR.

[31]  Luca Cardelli,et al.  A Process Model of Rho GTP-binding Proteins in the Context of Phagocytosis , 2009, FBTC@CONCUR.

[32]  Matthew Libera,et al.  Polymer multilayers with pH-triggered release of antibacterial agents. , 2010, Biomacromolecules.