Workpackage E: Digital tools for plant synthetic biology
Software tools play an increasingly important role in Synthetic Biology experiments, as we automate experiments, and the systems we construct increase in scale. In order to accurately predict the behaviour of biological systems, which are governed by multiscale parallel and feedback regulated genetic, physical and chemical interactions, we need computational models.
Software tools play an increasingly important role in Synthetic Biology experiments, as we automate experiments, and the systems we construct increase in scale. In order to accurately predict the behaviour of biological systems, which are governed by multiscale parallel and feedback regulated genetic, physical and chemical interactions we need computational models. OpenPlant aims to provide software to automate DNA assembly and the quantification of gene expression in plant in addition to providing models for gene expression and cell growth.
In addition to its role as a gene-centric database for finding plant DNA parts, the Marchantia genome database MarpoDB (Delmans et al., 2017) contains gene models with predicted transcripts, encoded proteins and phylogenetic comparison data. It maintains links to the Tak1 Marchantia reference genome and nomenclature, and allows interpretation of transcriptomic data. It has also formed the basis for analysis of differential gene expression in germinating Marchantia spores.
Future versions of the database will incorporate more features for describing characterisation of parts and gene expression using Plant Ontology terms. The online database can be found at: http://www.marpodb.io
Quantitative image analysis and model building
The combination of imaging data and high-speed computing allows the generation of software models for complex genetic networks and physico-genetically coupled multicellular systems. The behaviour of these systems cannot be predicted easily, and software models are essential tools for both understanding their properties, and for building new networks. In particular, DNA-based reprogramming of cellular networks would allow rational design of new crops, feedstock and cellular therapies.
CellModeller is a Python-based framework for modelling large-scale multi-cellular systems, such as biofilms, plant and animal tissue. Members of the Haseloff Lab have developed models of cellular biophysics, gene regulation and other intracellular processes, and intercellular signalling. The idea of CellModeller is to create a system to simulate these models together in populations of growing and dividing cells. The latest features include cellcell adhesion and cell shape, as well as algorithms for whole colonyscale segmentation from confocal microscopy datasets.
Dynamic software models and gene expression
The combination of specific labeling with gene markers and advanced microscopy allows extraction of quantitative information from intact biological samples. Gene expression can be integrated with cellular geometry, combined with other markers and mapped over time. This can produce parameter sets that directly inform executable models of biological systems. This a fruitful area for collaboration between biologists and physicists and mathematicians.
The Haseloff lab has developed three-parameter measurement techniques for quantifying gene expression in cell suspensions in such as way that extrinsic noise is minimised and a reliable estimate of the intrinsic properties of gene promoters can be made (Rudge et al., 2016; Grant et al., 2016). This relies on software models for gene expression, cell growth, and the use of a co-expressed marker to reduce variation. A computational framework has been established to allow automated analysis of microplate reader data, and this has been made available on Github.
Delmans, M.*, Pollak, B.* and Haseloff, J., 2017. MarpoDB: An open registry for Marchantia polymorpha genetic parts. Plant Cell Physiol. pcw201. DOI: https://doi.org/10.1093/pcp/pcw201
Grant, P.K., Dalchau, N., Brown, J.R., Federici, F., Rudge, T.J., Yordanov, B., Patange, O., Phillips, A. and Haseloff, J., 2016. Orthogonal intercellular signaling for programmed spatial behavior. Molecular systems biology, 12(1), p.849. DOI 10.15252/msb.20156590
Rudge, T.J., Brown, J.R., Federici, F., Dalchau, N., Phillips, A., Ajioka, J.W. and Haseloff, J., 2016. Characterization of intrinsic properties of promoters. ACS synthetic biology, 5(1), pp.89-98. DOI: 10.1021/acssynbio.5b00116