CDEV Subgroup Contributed Talks

Amanda M. Alexander

University of Houston

"Mathematical Models of Plasmid Partitioning and Loss in Dividing Cell Populations"

Plasmid DNA is common in bacterial populations and is used by synthetic biologists to alter the genetic makeup, and therefore function, of cells. At each cell division in a plasmid containing population, there is a probability of plasmid loss, giving rise to a differentiated population. The plasmid loss rate is difficult to measure, because it is small and quickly overshadowed by exponential growth of the subsequent plasmid free population. In addition, biologists observe complex intracellular plasmid dynamics, involving 1) formation of plasmid clusters that reduce plasmid diffusion, 2) plasmid localization to cell poles, and 3) plasmid replication with negative feedback. Mathematical models are useful in understanding how the plasmid loss rate is determined by these dynamics, but no previous works have incorporated this level of mechanistic detail. We will discuss simulation studies on the influence of the three mechanisms on the probability of plasmid loss, and under what conditions these effects can be captured by tractable mathematical models.
Additional authors: Mark Jayson Cortez, University of the Philippines Los Banos Charilaos Giannitsis, Rice University Oleg A. Igoshin, Rice University Krešimir Josić, University of Houston

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Joseph Pollacco

University of Oxford

"Predicting the effects of antibiotics on the bacterial SOS response"

Many types of antibiotics are believed to cause DNA damage in bacteria. The bacterial SOS response is known to promote bacterial survival during antibiotic treatment by inducing the expression of proteins that repair DNA damage. However, the mechanisms by which antibiotics generate DNA damage and trigger the SOS response remain unclear. Here, we propose a delay differential equation model that predicts the temporal dynamics of the SOS response, under action of ciprofloxacin, a DNA-damaging antibiotic. We calibrate the model using ABC-SMC, with data from time-resolved single-molecule and single-cell microscopy experiments. The model allows us to grants insight into how antibiotic treatments induce complex cell behaviour, such as temporal variation and cell-to-cell heterogeneity in the SOS response.
Additional authors: Thomas Haygarth, Department of Biochemistry, University of Oxford; Prof. Stephan Uphoff, Department of Biochemistry, University of Oxford; Prof. Philip Maini, Wolfson Centre for Mathematical Biology, University of Oxford; Prof. Ruth Baker, Wolfson Centre for Mathematical Biology, University of Oxford

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Kieran Boniface

University of Surrey

"Mechanotransduction in organoid development"

Organoids, mini engineered tissues, have become increasingly popular in recent years1. Indeed, we are experiencing an explosion of interest in organoids as three-dimensional test beds for biological experiments due to their complex structure and ability to mimic in-vivo tissues2. As a result, work must be done to accurately grow and develop organoids. However, the development of organoids, like all biological tissues, is sensitive to the mechanical signals that can influence behaviour from cell growth to determining cell type and shape3. These mechanical cues can even override biochemical signalling in directing type specification of stem cells4. This is made more complex in multicellular structures where mechanical signals operate over multiple length scales. Thus, mathematical models can provide an elegant framework to shed light on the underlying mechanics. One class of models are those founded in a consideration of continuum elasticity5 as applied to soft tissue mechanics, providing the opportunity to investigate the role of key mechanical factors6. The challenge is to produce models that can capture the active behaviour of cells and their ability to generate force as well as to describe the passive mechanical interactions of the system. We present here a model that captures key force generating mechanisms of organoids, namely cell contractility and cell growth. We describe the interaction between contractility and tissue growth and how their antagonistic behaviour can introduce key mechanical signals that may influence behaviour. As a final step, we consider the potential mechanisms by which mechanical feedback into cell control can be incorporated into our model and the impact this will have. References 1. Schutgens, F. & Clevers, H. Human Organoids: Tools for Understanding Biology and Treating Diseases. https://doi.org/10.1146/annurev-pathmechdis-012419-032611 15, 211–234 (2020). 2. Kim, J., Koo, B. K. & Knoblich, J. A. Human organoids: model systems for human biology and medicine. Nature Reviews Molecular Cell Biology vol. 21 571–584 Preprint at https://doi.org/10.1038/s41580-020-0259-3 (2020). 3. Orr, A. W., Helmke, B. P., Blackman, B. R. & Schwartz, M. A. Mechanisms of Mechanotransduction. Dev Cell 10, 11–20 (2006). 4. Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix Elasticity Directs Stem Cell Lineage Specification. Cell 126, 677–689 (2006). 5. Taber, L. Alan. Nonlinear theory of elasticity applications in biomechanics. Nonlinear theory of elasticity applications in biomechanics (World Scientific). 6. Littlejohns, E. & Dunlop, C. M. Mechanotransduction mechanisms in growing spherically structured tissues. New J Phys 20, 043041 (2018).

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