1 póster presentado al Cell Size Regulation EMBO Workshop, 14-18 September 2016Funding project DPI2014-54085-JINPeer reviewe

Alonso, Antonio A.

García, Miriam R.

Digital.CSIC

Experiments: Flow cytometry  Light scattering relates with cell size       Forward scatter (mainly for big cells)  Side scatter (mainly for small cells)  It requires a calibration with size reference beads [2]     Flow cytometry data provides population statistics  • Shape and time evolution of the probability density function   Theory is tested using size pdfs of Pediococcus acidilactici at different times during the exponential phase             (transparent histograms)  Model: Fokker-Planck eq.       SDEs are transformed into a partial derivative equation • allowing simulation of small and large populations  • better estimations of the probability function • efficient calibrations and validations of the theory   Validation using Pediococcus data showing for example  • estimated growth velocity of the cell-size(µ=0.25 d-1) coincides with population growth velocity (µ=0.24 d-1)    Motivation  Optimisation of antimicrobial treatments using Individual-based modelling (IBM) • IBM is the natural mathematical formalisms to describe emergence and selection of antimicrobial resistance (AMR)  • In IBM the essential first step is to describe division with simple equations  Is size a good descriptor of division? [1]  • IBM needs knowledge of individual parameters  Can we extract individual parameters (like division size) from population statistics?   Application: controlling AMR in the food industry Model: Stochastic size growth  Assumption: size is subject to a stochastic fluctuation                 δW characterized by a Wiener process [3]    Simulation using stochastic differential equations (SDE)    Simulations starting from one bacteria     Comparison with deterministic simulations (ξ=0) ESTIMATION OF SINGLE-CELL PARAMETERS FROM A DISTRIBUTION OF BACTERIAL SIZE Míriam R. García (miriamr@iim.csic.es) and Antonio A. Alonso Bioprocess Engineering group, IIM-CSIC (Vigo, Spain) ACKNOWLEDGEMENTS:  We thank I.G. Teixeira and J. A. Vázquez (CSIC) for the helping us to obtain the experimental data.  Funding project DPI2014-54085-JIN     http://resistance.iim.csic.es/  Population size distribution 0.2 µm (R4) 0.5 µm  (R3) 1.0 µm (R2) 2.0 µm (R1)   Distribution mode Offprint size (xson)=Half mother size (xdiv/2) Probability density function (pdf) Size Distribution maximum Mother size (xdiv) Distribution minimum Minimum alive size (xmin) The shape of the distribution depends on • Growth velocity (µ) • Fluctuation intensity (ξ) REFERENCES:  [1] Taheri-Araghi, et al. "Cell-size control and homeostasis in bacteria." Current Biology 25.3 (2015): 385-391 [2] Chandler,  et al."A new microparticle size calibration standard for use in measuring smaller microparticles using a new flow cytometer." Journal of Thrombosis and Haemostasis 9.6 (2011): 1216-1224. [3] Alonso, et al. "Modeling bacterial population growth from stochastic single-cell dynamics." Applied and environmental microbiology 80.17 (2014): 5241-5253. Size Time Size CONCLUSIONS:  The proposed model based on stochastic cell size growth shows promising  features (efficient, simple) for individual-based modeling. Single-cell parameters were recover from Pediococcus population statistics  Working now on extending the model to include a descriptor of antimicrobial resistance (probably stochastic membrane permeability)  Pediococcus data transparent histograms   Fokker-Plank model Continuous red line  Stochastic IBM Transparent histograms  Fokker-Plank model Continuous red line 

Estimation of single-cell parameters from a distribution of bacterial size

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