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Multiscale Models for New Antibiotic Technologies

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SDB - Stochastic dynamical systems in biology: numerical methods and applications

Antibiotic-resistant bacterial infections are significant causes of morbidity and mortality worldwide. These infections kill over 700,000 people globally every year. Because of the paucity of new antibiotics, this number may increase to over 10 million by 2050, surpassing deaths from cancer and diabetes combined ( Using synthetic biology techniques, we engineer probiotic bacteria to produce antimicrobial peptides (AMPs) and deliver the potent antibiotic proteins in the gastrointestinal tract of hosts. We test this new technology and demonstrate that it safely reduces multi-drug resistant pathogenic bacteria in the gut of animals [1,2]. At the heart of biological engineering efforts are multiscale models that guide explanations and predictions of the antagonistic activity of recombinant LAB against pathogenic strains [3]. Models are developed to quantify how AMPs kill bacteria at distinct but tied scales. Using atomistic simulations the various interaction steps between peptides and cell membranes are explored. Mesoscopic models are developed to study ion transport and depolarization of membranes treated with AMPs [4]. Stochastic kinetic models are developed to quantify the strength of synthetic promoters and AMPs expression [5]. In this presentation, we will discuss how modeling facilitates biological engineering and stress important theoretical and numerical challenges.

  1. Volzing K, Borrero J, Sadowsky MJ, Kaznessis YN. “Antimicrobial Peptides Targeting Gram-negative Pathogens, Produced and Delivered by Lactic Acid Bacteria.” ACS Synth Biol. 2013. 15;2(11):643-50. doi: 10.1021/sb4000367 .
  2. Geldart K., Borrero J., Kaznessis Y.N., “A Chloride-Inducible Expression Vector for Delivery of Antimicrobial Peptides Against Antibiotic-Resistant Enterococcus faecium”, Applied and Environmental Microbiology, 2015 81:11 3889-3897.3.
  3. Kaznessis Y, “Multiscale Models of Antibiotic Probiotics”, Curr Opin Chem Eng, 2014, 1;6:18-24
  4. Bolintineanu D, Kaznessis YN. “Computational studies of protegrin antimicrobial peptides: a review.” Peptides. 2011 Jan;32(1):188-201
  5. Kaznessis YN. Computational methods in synthetic biology. Biotechnol J. 2009 Oct;4(10):1392-405. doi: 10.1002/biot.200900163. 

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