We have worked in several areas of cellular information processing, with a broad interest in trying to uncover the reality behind experimental data and to thereby interpret such data more accurately (for more on this perspective, see the 2019 video on Beyond Big Data and Big Models: the role of abstraction in biology). The lab has always used a mixture of theoretical and experimental approaches, with more emphasis on theory in recent years, as our experimental work has shifted into collaborations and the theoretical problems have become more challenging. (We expect to return to experiments to study the problem of cellular learning.) The brief descriptions that follow, which are broadly in reverse chronological order, give pointers to a recent paper or review. For more information, take a look through our papers.
Learning in cells and the integration of cognitive science and systems biology, Eckert et al, Current Biology, 2024; see also the 2023 video on Cell learning: a new paradigm for biology.
Development of the linear framework, a graph-theory based approach to biochemical systems, used for many of the theoretical analyses described below, Nam et al, Interface Focus, 2022, and related software.
Hopfield barriers and the role of energy expenditure in information processing, Martinez-Corral et al, PNAS, 2024; see also the 2022 video on Thermodynamic limits in cellular information processing.
Analysis of molecular mechanisms of information processing, including
Cellular interrogation, an experimental approach to uncovering molecular mechanisms, Estrada et al, PLoS Computational Biology, 2016.
Algebraic geometric approaches to biochemical systems and the method of invariants, Dexter et al, Integrative Biology, 2015; see also this overview.
The role of theory in biology, Gunawardena, BMC Biology, 2014 and the essays, Gunawardena, Mol Biol Cell 2012, 2013 and 2014.
Programming with models and developmment of the littleb language, Mallavarapu et al, Journal of the Royal Society Interface, 2009.
Chemical Reaction Network Theory, Gunawardena, Preprint, 2003.