Phone: (480) 965-5481, Lab: (480) 965-5481
Office: LSA 256
Education: PhD-Biology, 1997, Yale University; PhD-History, Princeton University (forthcoming)
Curriculum Vitae: laubichler_m_cv.pdf
Faculty Group: Human Dimensions
Manfred D. Laubichler is President’s Professor of Theoretical Biology and History of Biology at ASU. He and his collaborators study theoretical biology and the history of biology using both empirical and conceptual approaches. Laubichler's multi-faceted research involves tracing the role of gene regulatory networks in development and evolution, as well as studying the conceptual structure of modern and historical biology. He also studies the Theory of Complex Adaptive Systems, focusing on complexity as a unifying principle in the social and life sciences, including applications in biomedicine and the study of innovations.
In addition to a variety of international appointments, Laubichler serves as Director of the Center for Social Dynamics and Complexity, Associate Director of the Origins Project,and Director of the Evolutionary Theory Core of Complex Adaptive Systems at ASU.
- Laubichler MD, Maienschein J, Renn J. 2013. Computational Perspectives in the History of Science. Isis. 104:119-130.
- Linksvayer TA, Fewell J, Gadau J, Laubichler MD. 2012. Developmental Evolution in Social Insects - Regulatory Networks from Genes to Societies. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution. 318:159-169.
- Krakauer D, Collins J, Flack JC, Fontana W, Laubichler MD, Prohaska S, Stadler P, West G. 2011. The Challenges and Scope of Theoretical Biology. Journal of Theoretical Biology. 276:269-276.
- Laubichler MD, Davidson EH. 2008. Boveri's long experiment: sea urchin merogons and the establishment of the role of nuclear chromosomes in development. Developmental Biology. 314:1-11.
- Laubichler MD, Maienschein J. 2007. From Embryology to Evo Devo: A History of Developmental Evolution. MIT Press
A bibliographic-coupling network built from a sub-set of the Davidson bibliographic dataset. Each node is a scientific publication, and edges indicate which nodes share multiple bibliographic references. The colored nodes indicate the distribution of a “topic” from an LDA-generated topic model.