Modelling and simulation of heterogeneous growth dynamics in bacterial populations using a novel multiphasic growth method

0044 English OPEN
Du Lac, Melchior;
  • Subject: QH301

The cell cycle is an inevitable source of population heterogeneity, that creates predictable discontinuities. By summarising the canonical understanding of the major steps within the bacterial cell cycle into a mechanistic model, the Cooper-Helmstetter model is able to ... View more
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    Chapter 1 Introduction 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Biological Background . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 The Central Dogma of the Cell Division Cycle . . . . . . . . 3 1.2.2 Population Heterogeneity and Biological Noise . . . . . . . . 14 1.3 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.1 Cooper-Helmstetter Model . . . . . . . . . . . . . . . . . . . 16 1.3.2 Individual Based Simulation . . . . . . . . . . . . . . . . . . . 20 1.3.3 Genetic Optimisation . . . . . . . . . . . . . . . . . . . . . . 22 1.4 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

    Chapter 2 Materials and Methods 24 2.1 Media and Bacterial Strains . . . . . . . . . . . . . . . . . . . . . . . 24 2.1.1 Chromosomal DNA Quanti cation . . . . . . . . . . . . . . . 25 2.2 Computational Methods and Packages . . . . . . . . . . . . . . . . . 26 2.2.1 Normalising Fluorescence to Genomic Content . . . . . . . . 26 2.2.2 Computational Packages . . . . . . . . . . . . . . . . . . . . . 28

    Chapter 3 Model Development 29 3.1 Growth Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.1.1 Single Cell Growth Dynamics . . . . . . . . . . . . . . . . . . 30 3.1.2 Balanced Growth . . . . . . . . . . . . . . . . . . . . . . . . . 33 Chapter 4 Model Examination and Optimisation to recA1 Mutants 67 4.1 HMG Examination Experiments . . . . . . . . . . . . . . . . . . . . 67 4.1.1 Wild Type Cells . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.1.2 Nutritional Shift-up Experiment . . . . . . . . . . . . . . . . 72 4.2 Optimisation to recA1 Mutants . . . . . . . . . . . . . . . . . . . . . 81 4.2.1 The Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.2.3 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.3 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 98

    Chapter 5 Predicting Cell Cycle and SGC Properties Throughout Disparate Growth Regimes 100 5.1 Determining Chromosomal Gene Copy Number . . . . . . . . . . . . 100 5.1.1 Predicting Gene Copy Number . . . . . . . . . . . . . . . . . 102 5.1.2 Calculating Gene Copy Number . . . . . . . . . . . . . . . . 104 5.1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.2 HMG ODE Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.2.1 Partition Extrinsic Noise . . . . . . . . . . . . . . . . . . . . 107 5.2.2 Transient Chromosomal Gene Copy Number . . . . . . . . . 112 5.3 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 118 3.8 Flow chart describing the replication algorithm for WT and the aberrant chromosome copy number phenotype (see Figure 1.3) in HMG. 54 3.9 Concatenation of the measured D periods for the bacterial strain K-12 MG1655. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.10 Flow chart describing the segregation and division algorithms in HMG. 60 3.11 Injection-based strategy for connecting the HMG simulator to empirical growth data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.12 Flow chart of the single cell model. . . . . . . . . . . . . . . . . . . . 63

    4.1 Spline t to the measured OD from K-12 cells grown in LB with a shaking rate of 230 rpm and 23 rpm. . . . . . . . . . . . . . . . . . .

    4.2 Measured WT K-12 MG1655 bacteria grown in LB at two di erent shaking rates, measured against simulated population using the HMG framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

    4.3 Spline t and analysis of the nutritional shift-up OD data incubated at a shaking rate of 230 rpm and 37 C. . . . . . . . . . . . . . . . .

    4.4 Measured against simulated DNA distributions of the three di erent states from the nutritional shift-up experiment. . . . . . . . . . . . .

    4.5 Population distribution for di erent parameters of the simulated model using the HMG framework with input the nutritional shift-up growth curve as presented in Figure 4.3. . . . . . . . . . . . . . . . . . . . .

    4.6 Total-order index sensitivity analysis for the optimized parameters. .

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