Instituto Gulbenkian de Ciência (Theoretical Immunology)

The Stochastic Basis of Somatic Variation

Isogenic cell populations exhibit a surprising amount of heterogeneity, such as variability in protein copy numbers or in methylation patterns, in situations where this could hardly be attributed to genetic or external environmental factors. Moreover, this variability was found to be somewhat heritable leading to the concept of non-genetic individuality. Very soon, it became apparent that the ultimate cause of this variability in isogenic cells was the stochastic nature of chemical reactions within the cell, particularly at the level of gene transcription. However, it is not clear how stochastic gene expression, per se, leads to these manifestations of somatic variation and what impact this variability has for the population.

In particular, we turn our attention to a manifestation of non-genetic individuality at the level of the gene: stochastic monoallelic expression. We mathematically formalize and challenge against quantitative experimental data several proposed mechanisms that aim to explain this phenomenon and, as result, we propose a general model of transcription regulation that relates stochastic transcriptional activation and epigenetic chromatin modifications, which provide another source of somatic variation.

Next, we show how variability in cellular components leads to the observed distributions of protein copy numbers in cell populations. Then, in order to seek the implications for the response of the population, we model several common signalling modules and analyze their sensitivity to changes in total concentration of key components. We identify mechanisms that, due to their structure, promote an uniform response from each cell in the population, thereby negating the effects of heterogeneity and mechanisms that enable this heterogeneity to be manifested in novel ways, such as level and timing of response and how many cells actually respond.

Since this heterogeneity of protein levels is created by dynamical mechanisms and hence they fluctuate, we ask if different fluctuation rates and structures confer a competitive advantage in a competition model between populations with different fluctuation characteristics of a receptor for a growth factor. We conclude that higher variances confer an advantage for the same fluctuation structure (i.e., steady state distribution). We also compare different fluctuation structures showing that they have different impacts on the fitness of the population providing a basis for the selection of the generative mechanism of these fluctuations.

Finally, we show how the individual history of a cell in terms of stochastic interactions with its environment can lead to variability of intracellular components and how this can be used as a homeostasis mechanism in a population.

In light of these results we argue that stochastic effects inherent to the cells metabolism generate non-genetic individuality in an isogenic population. Moreover, we investigated the implications of such heterogeneity at the level of signal transduction, gene expression and population dynamics. We tried to convey a new way to look at cell populations and their relation to their environment based on the stochastic events at the single cell level. This view represents an emerging perspective in which determinism is based not on the dynamics of the single cell but on the evolution of a distribution of probabilities, in which each cell is a realization of a stochastic process. Within this perspective, programs of differentiation, cellular identity and individuality acquire new meanings.