Project Description

Phone: (206)685-2119
Office: Foege N410A

Our long term goal is to develop credible and reliable computational models of disease states that could by used by clinicians to more effectively treat disease.
Credible predictive models of disease
Supporting best practicing in computational modeling
Control theory applied of cellular networks
Develop new approaching to mechansistic modeling of cells
My research includes the foillowing broad objectives:

1. Use mathematics and computation to help understand the dynamics and operation of cellular processes.
2. Develop new more reliable approaches to modeling complex systems such that the models can be used with confidence in clinical settings.
3. Disseminate the work to the wider scientific community and support best practices in systems biology modeling multi-scale modeling of subcellular and multicellular systems.

BSc: Biochemistry/Microbiology, University of Canterbury, UK
MSc: Biological Computation, University of York
PhD: Systems Biology, Oxford brookes, UK
PGCE: Teaching Degree, University of Aberystwyth
University of Edinburgh, Department of Genetics, UK
2013 University of Washington College of Engineering, Community of Innovators Awards, Faculty Innovator: Teaching & Learning
BIOEN 336: Systems & Controls
BIOEN 499: Special Topics: Modeling in Systems Biology
Somogyi, ET., JM Bouteiller, JA. Glazier, M König, JK Medley, MH. Swat, and HM. Sauro. “libRoadRunner: a high performance SBML simulation and analysis library.” Bioinformatics 31, no. 20 (2015): 3315-3321.

Entus, R, B Aufderheide, and HM. Sauro. “Design and implementation of three incoherent feed-forward motif based biological concentration sensors.” Systems and synthetic biology 1, no. 3 (2007): 119-128.

Galdzicki, M, KP. Clancy, E Oberortner, M Pocock, JY. Quinn, CA. Rodriguez, N Roehner et al. “The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology.” Nature biotechnology 32, no. 6 (2014): 545.

Fell, DA., and HM. Sauro. “Metabolic control and its analysis.” The FEBS Journal 148, no. 3 (1985): 555-561.

Kim, KH., and HM. Sauro. “Adjusting phenotypes by noise control.” PLoS computational biology 8, no. 1 (2012): e1002344.

Kim, KH, H Qian, and HM. Sauro. “Nonlinear biochemical signal processing via noise propagation.” The Journal of chemical physics 139, no. 14 (2013): 10B608_1.

Sleight, SC., BA. Bartley, JA. Lieviant, and HM. Sauro. “In-Fusion BioBrick assembly and re-engineering.” Nucleic acids research 38, no. 8 (2010): 2624-2636.

Paladugu, SR., V. Chickarmane, A. Deckard, J. P. Frumkin, M. McCormack, and H. M. Sauro. “In silico evolution of functional modules in biochemical networks.” IEE Proceedings-Systems Biology 153, no. 4 (2006): 223-235.

Sauro, HM. Enzyme kinetics for systems biology. 2nd Edition, Future Skill Software, 2012.

Sauro, HM. Systems Biology: Introduction to Pathway Modeling, Future Skill Software, 2012.

Hucka, M, A Finney, HM. Sauro, H Bolouri, JC. Doyle, H Kitano, AP. Arkin et al. “The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.” Bioinformatics 19, no. 4 (2003): 524-531.

Medley, M, Choi K, Matthias K, Smith L, Gu S, Hellerstein J, Sealfon SC, Sauro HM Tellurium Notebooks – An Environment for Dynamical Model Development, Reproducibility, and Reuse (2018), PLoS Comp Bio,.


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