State and parameter estimation of ensemble dynamics

MAPK and similar Cascades, which controls context-specific cell-fate in normal and cancer tissues, can be modeled as a set of (bio)chemical reactions. Experimentally, level of the proteins involved in these cascades in an ensemble of cells are now available via the high-throughput single-cell experimentation in the lab. Experimental limitations lead to only measurement of time-snapshot data. Availability of the trajectories of protein levels in an ensemble of cells can offer insights into the signatures that decide cell-fate and the variability in these signatures. State estimation, filtering and time series modeling based approaches, widely used in systems and control for engineered-systems, can be employed to predict the single-cell trajectories. The goal of this project is to develop mechanistic kinetic model of the cascades and employ state and parameter estimation tools to predict the experimental data. Models will first be developed for the test case of MAPK cascade system for which extensive experimental data, both in-house generated and from literature, on mammalian cells is already available. The developed model will be used to predict the dynamical states of proteins in an ensemble of cells and the parameters that may be governing the underlying dynamics. The systematic approach will then be used to arrive at models for similar systems for which data is available in literature. Chemical engineering background preferred. No background in biology is required for this project. Interest in learning the necessary biology is sufficient. (1 TA position)

Topic posted on 8 Nov 2018

Proposing Faculty
Research Area
  • Computational Biology
  • Data Analysis
  • Identification
  • Modelling
  • Optimisation
  • Systems Biology