Martin is an Honorary Group Leader, currently based at the . He is working with Group Leaders in the Epigenetics Programme. The Howard group combines simple, predictive mathematical modelling with long-lasting experimental collaborations, to dissect biological mechanisms too complex to unravel by experiments alone. In many cases we are able rationalise complex biological dynamics into simple underlying mechanisms, with few components and interactions.
Our approach is highly interdisciplinary and relies heavily on the techniques of statistical physics and applied mathematics, as well as on close collaboration with experimental groups. This truly interdisciplinary approach allows us to get to the heart of biological mechanisms more speedily.
At present the main focus of the group is epigenetic dynamics, probing how epigenetic memory states are set up and then stably maintained. In this context, we work with both histone modification memory systems, as well as on DNA methylation, collaborating with experimentalists in systems ranging from plants to mammalian stem cells. A particular focus has been the Polycomb epigenetic system, where we have proposed an all-or-nothing epigenetic switching mechanism, with epigenetic gene silencing directly antagonised by transcription. Overall, as epigenetic systems are central to ageing and health, understanding how they work at a fundamental level is of vital importance.
Understanding the mechanistic basis of epigenetic memory has proven to be a difficult task due to the underlying complexity of the systems involved in its establishment and maintenance. Here, we review the role of computational modeling in helping to unlock this complexity, allowing the dissection of intricate feedback dynamics. We focus on three forms of epigenetic memory encoded in gene regulatory networks, DNA methylation, and histone modifications and discuss the important advantages offered by plant systems in their dissection. We summarize the main modeling approaches involved and highlight the principal conceptual advances that the modeling has enabled through iterative cycles of predictive modeling and experiments. Lastly, we discuss remaining gaps in our understanding and how intertwined theory and experimental approaches might help in their resolution. Expected final online publication date for the , Volume 75 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
The shuffling of genetic material facilitated by meiotic crossovers is a critical driver of genetic variation. Therefore, the number and positions of crossover events must be carefully controlled. In , an obligate crossover and repression of nearby crossovers on each chromosome pair are abolished in mutants that lack the synaptonemal complex (SC), a conserved protein scaffold. We use mathematical modelling and quantitative super-resolution microscopy to explore and mechanistically explain meiotic crossover pattering in lines with full, incomplete or abolished synapsis. For mutants, which lack an SC, we develop a coarsening model in which crossover precursors globally compete for a limited pool of the pro-crossover factor HEI10, with dynamic HEI10 exchange mediated through the nucleoplasm. We demonstrate that this model is capable of quantitatively reproducing and predicting experimental crossover patterning and HEI10 foci intensity data. Additionally, we find that a model combining both SC- and nucleoplasm-mediated coarsening can explain crossover patterning in wild-type and in mutants, which display partial synapsis. Together, our results reveal that regulation of crossover patterning in wild-type and SC defective mutants likely act through the same underlying coarsening mechanism, differing only in the spatial compartments through which the pro-crossover factor diffuses.
The maintenance of transcriptional states regulated by histone modifications and controlled switching between these states are fundamental concepts in our understanding of nucleosome-mediated epigenetic memory. Any approach relying on genome-wide bioinformatic analyses alone offers limited scope for dissecting the molecular mechanisms involved in maintenance and switching. Mechanistic mathematical models-describing the dynamics of histone modifications at individual genomic loci-offer an alternative way to investigate these mechanisms. These models, in conjunction with quantitative experimental data-ChIP data, quantification of mRNA levels, and single-cell fluorescence tracking in clonal lineages-can generate predictions that drive more targeted experiments, allowing us to understand mechanisms that would be challenging to unravel by a purely experimental approach. In this chapter, we describe a generic stochastic modeling framework that can be used to capture histone modification dynamics and associated molecular processes-including transcription and read-write feedback by chromatin modifying complexes-at individual genomic loci. Using a specific example-transcriptional silencing by Polycomb-mediated H3K27 methylation-we demonstrate how to construct and simulate a stochastic histone modification model. We provide a step-by-step guide to programming simulations for such a model and discuss how to analyze the simulation output.