@article{sulis_jiang_yang_marques_matthews_miller_lan_cofre-vega_liu_sun_et al._2023, title={Multiplex CRISPR editing of wood for sustainable fiber production}, volume={381}, ISSN={["1095-9203"]}, url={http://europepmc.org/abstract/med/37440632}, DOI={10.1126/science.add4514}, abstractNote={The domestication of forest trees for a more sustainable fiber bioeconomy has long been hindered by the complexity and plasticity of lignin, a biopolymer in wood that is recalcitrant to chemical and enzymatic degradation. Here, we show that multiplex CRISPR editing enables precise woody feedstock design for combinatorial improvement of lignin composition and wood properties. By assessing every possible combination of 69,123 multigenic editing strategies for 21 lignin biosynthesis genes, we deduced seven different genome editing strategies targeting the concurrent alteration of up to six genes and produced 174 edited poplar variants. CRISPR editing increased the wood carbohydrate-to-lignin ratio up to 228% that of wild type, leading to more-efficient fiber pulping. The edited wood alleviates a major fiber-production bottleneck regardless of changes in tree growth rate and could bring unprecedented operational efficiencies, bioeconomic opportunities, and environmental benefits.}, number={6654}, journal={SCIENCE}, author={Sulis, Daniel B. and Jiang, Xiao and Yang, Chenmin and Marques, Barbara M. and Matthews, Megan L. and Miller, Zachary and Lan, Kai and Cofre-Vega, Carlos and Liu, Baoguang and Sun, Runkun and et al.}, year={2023}, month={Jul}, pages={216-+} }
 @article{matthews_williams_2021, title={Multiscale Modeling of Cross-Regulatory Transcript and Protein Influences}, volume={2328}, ISBN={["978-1-0716-1533-1"]}, ISSN={["1940-6029"]}, DOI={10.1007/978-1-0716-1534-8_7}, abstractNote={With the popularity of high-throughput transcriptomic techniques like RNAseq, models of gene regulatory networks have been important tools for understanding how genes are regulated. These transcriptomic datasets are usually assumed to reflect their associated proteins. This assumption, however, ignores post-transcriptional, translational, and post-translational regulatory mechanisms that regulate protein abundance but not transcript abundance. Here we describe a method to model cross-regulatory influences between the transcripts and proteins of a set of genes using abundance data collected from a series of transgenic experiments. The developed model can capture the effects of regulation that impacts transcription as well as regulatory mechanisms occurring after transcription. This approach uses a sparse maximum likelihood algorithm to determine relationships that influence transcript and protein abundance. An example of how to explore the network topology of this type of model is also presented. This model can be used to predict how the transcript and protein abundances will change in novel transgenic modification strategies.}, journal={MODELING TRANSCRIPTIONAL REGULATION}, author={Matthews, Megan L. and Williams, Cranos M.}, year={2021}, pages={115–138} }
 @inproceedings{matthews_williams_2012, title={Region of attraction estimation of biological continuous Boolean models}, DOI={10.1109/icsmc.2012.6377982}, abstractNote={Quantitative analysis of biological systems has become an increasingly important research field as scientists look to solve current day health and environmental problems. The development of modeling and model analysis approaches that are specifically geared toward biological processes is a rapidly growing research area. Continuous approximations of Boolean models, for example, have been identified as a viable method for modeling such systems. This is because they are capable of generating dynamic models of biochemical pathways using inferred dependency relationships between components. The resulting nonlinear equations and therefore nonlinear dynamics, however, can present a challenge for most system analysis approaches such as region of attraction (ROA) estimation. Continued progress in the area of biosystems modeling will require that computational techniques used to analyze simple nonlinear systems can still be applied to nonlinear equations typically used to model the dynamics associated with biological processes. In this paper, we assess the applicability of a state of the art ROA estimation technique based on interval arithmetic to a subnetwork of the Rb-E2F signaling pathway modeled using continuous Boolean functions. We show that this method can successfully be used to provide an estimate of the ROA for dynamic models described using Hillcube continuous Boolean approximations.}, booktitle={Ieee international conference on systems man and cybernetics conference}, author={Matthews, M. L. and Williams, Cranos}, year={2012}, pages={1700–1705} }