Embedded Files
Research during my postdoctoral stint
Research during my postdoctoral stint
VID_20191215_014830204.mp4VID_20200201_165153360.mp4Liquid handling robots for massively parallel evolution experiments with budding yeast.
Liquid handling robots for massively parallel evolution experiments with budding yeast.
Genetic basis of differential autodiploidization propensity in evolving budding yeast, Saccharomyces cerevisiae populations.
Often laboratory cultures of haploid Saccharomyces cerevisiae used in experimental evolution experiments undergo spontaneous whole genome duplication, a phenomenon known as autodiploidization.
Typically, these autodiploids were found to have greater evolutionary fitness than the corresponding haploids, and eventually monopolize the dynamics of adaptation by outcompeting the latter. This can lead to erroneous interpretation of the results due to several reasons including contrasting outcome owing to the difference in genetics of haploids and diploids, autodiploidization can affect genetic constructs and it eliminates the possibility of comparing the adaptive dynamics of haploids and diploids.
Although several previous studies had suffered from and reported this phenomenon, a detailed understanding of the underlying mechanism largely unknown. Here, we have investigated the genetic basis of autodiploidization though QTL mapping approach using the F1 spores of a cross between two laboratory strains of Saccharomyces cerevisiae¸ which showed drastic difference in the frequency of autodiploidization in a preliminary study. 500 generation of massively parallel evolution of >600 spores along with their parental controls reveal an intriguing pattern of autodiploidization.
During my PhD I worked on three broad themes:
During my PhD I worked on three broad themes:
Schematic diagram of the processes in the individual based model for Drosophila melanogaster
a) Modelling the eco-evolutionary dynamics of Drosophila melanogaster and verifying them through laboratory experiments: I have been working on building a stage-structiured, stochastic, pattern-oriented model of Drosophila population dynamics that explicitly involves different life-history traits of the species. This model incorporates details of the various density-dependent feedback loops that determine the dynamics of Drosophila cultures. The model does a fair job of capturing both the qualitative and the quantitative nature of the dynamics of two independent laboratory experiments under four different nutritional regimes. One of the insights obtained from this modelling exercise is that even in the highly simplified dynamics under laboratory conditions, the environment can interact with the life-history and demographic parameters of the organisms to give rise to very counter-intuitive effects on the dynamics. The results of this study were documented here:
Tung, S., Rajamani, M., Joshi, A., Dey, S. 2019. Complex interaction of resource availability, life-history and demography determines the dynamics and stability of stage-structured populations. Journal of Theoretical Biology 460. 1-12. [pdf]
CLICK HERE to reach the website below to play with the range of parameters of this model to visualize their impact on population dynamic timeseries.
b) Stabilizing the dynamics of populations and meta-populations: My current doctoral work also involves theoretical and empirical investigation of a finding a means of stabilizing extinction prone populations and meta-populations of laboratory populations of Drosophila. This study resulted in three following publications
Tung, S., Mishra, A., Dey, S. 2016. Simultaneous enhancement of multiple stability properties using two-parameter control methods in Drosophila melanogaster. Ecological Complexity 26, 128–136.
Tung, S., Mishra, A., Dey, S. 2016. Stabilizing the dynamics of laboratory populations of Drosophila melanogaster through upper and lower limiter controls. Ecological Complexity 25, 18-25.
Tung, S., Mishra, A. and Dey, S. 2014. A comparison of six methods for stabilizing population dynamics. Journal of Theoretical Biology 356, 163-173.
Stabilizing effect of Both Limiter Control. Red: w/o any control method; green: w/ Both Limiter Control
Common fruit flies, Drosophila melanogaster
c) Experimental evolution of dispersal and its life-history and behavioural correlates: I have also performed an artificial selection experiment for increased dispersal in the common fruit-fly, Drosophila melanogaster(>75 generations, ongoing). This study shows that several components of dispersal like propensity, ability and rate of traveling, evolve very rapidly and simultaneously, leading to the evolution of the population's dispersal kernel. Also, we found correlated evolution of a suite of behavioral traits including locomotor activity, exploration and aggression, for which there was no obvious selection pressure in the system. With the help of a collaborator, I have also explored the metabolomic changes related to the evolution of increased dispersal using NMR.The results of these studies are documentaed in the following manuscripts.
Tung, S., Mishra, A., Shreenidhi, P. M., Sadiq, M. A., Joshi, S., Sruti, V. S., Dey, S. 2018. Simultaneous evolution of multiple dispersal components and kernel. Oikos 127, 34–44. [pdf]
Tung, S., Mishra, A., Gogna, N., Sadiq, M. A., Shreenidhi, P. M., Sruti, V. S., Dorai, K., Dey, S. 2018. Evolution of dispersal syndrome and its corresponding metabolomics changes. Evolution 72, 1890-1903. [pdf]
5. Mishra, A.*, Tung, S.*, Shreenidhi, P. M., Sadiq, M. A., Sruti, V. S., Chakraborty, P. P., and Dey, S. 2018. Sex differences in dispersal syndromes are modulated by environment and evolution. Philosophical Transactions of the Royal Society B 373, 20170428. *Equal contribution.
Fly Dispersal Gallery
Fly Dispersal Gallery
Google Sites
Report abuse