Bio-Physics Seminar with Prathitha Kar on Cell growth and division in bacteria

All cells grow and divide maintaining a finite size. How cells regulate their cell cycle is a central question in cell biology. Technological advances over the last decade have allowed us to collect large datasets of single-cell growth data. Statistical constructs such as binning and linear regression are used to quantify relationships between the measured variables and hypothesize underlying biological cell cycle mechanisms based on it. However, we show examples where what appears to be ordinary use of these statistical methods leads to incorrect biological conclusions about single-cell growth. On applying the correct growth rate analysis methods to Escherichia coli and the important pathogen Mycobacterium tuberculosis, we find that E. coli length, surprisingly, grows super-exponentially (i.e., faster than exponential growth). In contrast, M. tuberculosis appears to grow linearly at the single-cell level. Further, we use conditional independence tests in conjunction with cell size data at key cell cycle events (cell birth, start of DNA replication, and cell division) to select between the competing cell cycle regulation models. In slow growth conditions, we corroborate a model where DNA replication-related processes control when the cell divides. In faster-growth media, we find that the cell division is affected by additional cues beyond DNA replication. The use of conditional independence tests is a different approach in the context of understanding cell cycle regulation and it can be used in future studies to further explore the causal links between cell events.