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Nacho Molina

Publications and source records attributed to Nacho Molina.

2 recordsLinked to original sources

Chromatin structure shapes the search process of transcription factors

The diffusion of regulatory proteins within the nucleus plays a crucial role in the dynamics of transcriptional regulation. The standard model assumes a 3D plus ID diffusion process: regulatory proteins either move freely in solution or slide on DNA. This model however does not considered the 3D structure of chromatin. Here we proposed a multi-scale stochastic model that integrates, for the first time, high-resolution information on chromatin structure as well as DNA-protein interactions. The dynamics of transcription factors was modeled as a slide plus jump diffusion process on a chromatin network based on pair-wise contact maps obtained from high-resolution Hi-C experiments. Our model allowed us to uncover the effects of chromatin structure on transcription factor occupancy profiles and target search times. Finally, we showed that binding sites clustered on few topological associated domains leading to a higher local concentration of transcription factors which could reflect an optimal strategy to efficiently use limited transcriptional resources.

Systems Biology

Stochasticity and negative feedback lead to pulsed dynamics and distinct gene activity patterns

Gene expression is an inherently stochastic process that depends on the structure of the biochemical regulatory network in which the gene is embedded. Here we study the interplay between stochastic gene switching and the widespread negative feedback regulatory loop. Using a simple hybrid model, we find that stochasticity in gene switching by itself can induce pulses in the system. Furthermore, we find that our simple model is able to reproduce both exponential and peaked distributions of gene active and inactive times similar to those that have been observed experimentally. Our hybrid modelling approach also allows us to link these patterns to the dynamics of the system for each gene state.

Systems Biology