Integrative modelling of stochasticity noise heterogeneity and measurement error in the study of model biological systems

点击翻译按钮获取中文摘要

Recent breakthroughs in experimental technology have allowed the study of the dynamics of cell biochemistry at single-cell resolution. These studies confirm earlier theoretical predictions that such dynamics would be intrinsically stochastic. Such randomness in cellular behaviour has now been confirmed to be an important component in the observed heterogeneity of genetically identical cells cultured in a uniform environment. However, intrinsic stochasticity is just one source of heterogeneity in biological data, and others, such as minor unavoidable variations in environment and limitations in the measurement technology, can have an equally important effect. A major goal of modern biology is to build dynamic, predictive, quantitative models of the behaviour of biological systems. Computational models enable in silico testing of plausible biological hypotheses and help to establish a clearer understanding of the complex genetic and biochemical mechanisms at play. Despite technological advances, much biological data used to build, refine and test models consists of measurements on cell populations. Unless the models that we build properly reflect the multiple sources of heterogeneity in such data, it is difficult to use them in order to test model adequacy or refine the model parameters or structure to more accurately reflect the underlying biology. The only coherent framework for mathematically modelling noise and heterogeneity is based on probability theory, and especially the theory of stochastic processes. Single-cell stochastic kinetic models are already established as a powerful tool for modelling intrinsic noise in simple genetic and biochemical networks. However, in such cases the emphasis is almost exclusively focused on the effect of noise on single-cell dynamics. It is important to now move beyond this and build integrated stochastic models that incorporate multiple sources of noise and heterogeneity. Stochastic models which integrate multiple levels of organisation are therefore a vital part of the vision for Systems Biology. This proposal comes from a statistician already expert in the necessary mathematical, computational and stochastic modelling techniques. The plan is to train him in modern molecular and cell biology, and associated experimental techniques. The idea is not to 'convert' him into a bench biologist, but by spending time in the lab he will better appreciate the practical issues confronted by 'wet lab' scientists, and will be in a much better position to be able to accurately model the multiple sources of heterogeneity that give rise to experimental data. This will allow the development of a number of examples of integrative stochastic models of biological systems, in order to demonstrate the utility of the approach.