@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} }