  Cellular functions crucially depend on the precise execution of complex
biochemical reactions taking place on the chromatin fiber in the tightly packed
environment of the cell nucleus. Despite the availability of large data sets
probing this process from multiple angles, we still lack a bottom-up framework
which can incorporate the sequence-specific nature of biochemistry in a unified
model of 3D chromatin dynamics. Here we propose SEMPER (Sequence Enhanced
Magnetic PolymER), a novel stochastic polymer model which naturally
incorporates observational data about sequence-driven biochemical processes,
such as binding of transcription factor proteins, in a 3D model of chromatin
structure. By introducing a new algorithm for approximate Bayesian inference,
we discuss how to estimate in a robust manner the relative importance of
biochemical vs. polymer signals in the determination of the chromatin
epigenetic states which is leading to a significant revision of the
interpretation of previous models. Furthermore we show that, without additional
input from the genome 3D structure, our model can predict with reasonable
accuracy some notable and non trivial conformational features of chromatin
folding within the nucleus. Our work highlights the importance of introducing
physically realistic statistical models for predicting chromatin states from
epigenetic data, and opens the way to a new class of more systematic approaches
to interpret epigenomic data.
