Hiroyuki Kuwahara got his Ph.D. degree in Computer Science from the University of Utah in 2008. He was then a junior researcher at Microsoft Research - Trento during 2007 and 2009. During 2009 and 2012, he was a Ray and Stephanie Lane Fellow at the School of Computer Science at Carnegie Mellon University. Since 2012, he joined the SFB group as a Research Scientist. 

Research Interests

​Hiroyuki's area of research is broadly in computational systems and synthetic biology. His main research focus is on the theoretical understanding of how various uncertainties contribute to the relation between genotypes and phenotypes through the development and the use of quantitative modeling and analysis methods. In particular, he is interested in gaining quantitative insights into how biological functions have emerged from stochastic interactions of molecules, how they might have evolved, and how they can be modified for specific objectives.

Selected Publications

​1. H. Kuwahara, X. Cui, R. Umarov, R. Grunberg, C. Myers, and X. Gao, "BOLme: a repository of SBOL parts for metabolic engineering," ACS Synthetic Biology, 2017 (doi: 10.1021/acssynbio.6b00278).
2. H. Kuwahara, R. Umarov, I. Almasri, and X. Gao, "ACRE: absolute concentration robustness exploration in module-based combinatorial networks," Synthetic Biology, Oxford University Press, 2017 (doi: 10.1093/synbio/ysx001).
3. H. Kuwahara, M. Alazmi, X. Cui, and X. Gao, "MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind," Nucleic Acids Research, 2016 (doi:10.1093/nar/gkw342).
4. C. Fujii, H. Kuwahara, G. Yu, L. Guo, and X. Gao "Learning gene interaction networks from gene expression data using weighted consensus," Neurocomputing, 2017 (doi: 10.1016/j.neucom.2016.02.087). equal contribution.
5. H. Kuwahara, S Arold, and X. Gao, "Beyond initiation-limited translational bursting: the effects of burst size distributions on the stability of gene expression," Integrative Biology, 2015 (doi: 10.1039/C5IB00107B). In the top 25% of all research outputs scored by Altmetric.
6. X. Wang, H. Kuwahara, and X. Gao, "Modeling DNA atiny landscape through two-round support vector regression with weighted degree kernels," BMC Systems Biology, 8(Suppl 5):S5, 2014.
7. P. Chen, J. Li, L. Wong, H. Kuwahara, J. Huang, and X. Gao, "Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences," Proteins, 81(8), 2013.
8. H. Kuwahara and R. Schwartz, "Stochastic steady-state gain in a gene expression process with mRNA degradation control," Journal of the Royal Society Interface, 2012 (doi: 10.1098/rsif.2011.0757).
9. H. Kuwahara and O. Soyer, "Bistability in feedback circuits as a byproduct of the evolution of evolvability," Molecular Systems Biology, 8, 2012. F1000Prime recommended.
10. H. Kuwahara, C. Myers, and M. Samoilov, "Temperature control of mbriation circuit switch in uropathogenic Escherichia coli : quantitative analysis via automated model abstraction," PLoS Computational Biology, 6(3): e1000723, 2010.

Education Profile

  • Ph.D. Computer Science, University of Utah, Utah, U.S.A., 2008.
  • B.S. Computer Science, University of Utah, Utah, U.S.A., 2001.

Professional Memberships

  • 2012-current: Research Scientist, KAUST, Thuwal, Saudi Arabia.
  • 2009-2012: Ray and Stephanie Lane Fellow, Carnegie Mellon University, Pittsburgh, USA.
  • 2008-2009: Junior Researcher, Microsoft Research - University of Trento CoSBi, Trento, Italy.

Awards and Distinctions

  • Ray and Stephanie Lane Fellowship, School of Computer Science, Carnegie Mellon University, 2009-2012

Selected Publications

Fan, M., Kuwahara, H., Wang, X., Wang, S., & Gao, X. (2015). Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study. Briefings in Bioinformatics, 16(6), 987–999. doi:10.1093/bib/bbv015
Kuwahara, H., & Gao, X. (2013). Stochastic effects as a force to increase the complexity of signaling networks. Scientific Reports, 3(1). doi:10.1038/srep02297
Kuwahara, H., Fan, M., Wang, S., & Gao, X. (2013). A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics, 29(13), i98–i107. doi:10.1093/bioinformatics/btt232
Fujii, C., Kuwahara, H., Yu, G., Guo, L., & Gao, X. (2017). Learning gene regulatory networks from gene expression data using weighted consensus. Neurocomputing, 220, 23–33. doi:10.1016/j.neucom.2016.02.087