Skip navigation

Faculty Biographies

Chris Wiggins, Ph.D.

Associate Professor, Department of Applied Physics and Applied Mathematics

Contact Information
Department of Applied Physics and Applied Mathematics
School of Engineering and Applied Science
Columbia University
200 S. W. Mudd (Mail Code 4701)
500 West 120th Street
New York, NY 10027 USA
Tel: +1 212-854-1114
Fax: +1 212-854-8257
Web site:


Research Theme

Within the last decade, the need for mathematical and computational expertise to address questions of biological origin has transformed the nature of biological research. Novel biotechnologies, the unprecedented abundance and character of their data, and the promise of these approaches (in understanding both problems from basic biology as well as clinical research into disease and medicine) have demanded mathematical innovation, and consequently transformed mathematics as well. Biology now poses mathematical modeling challenges unlike those faced in mathematical biology before 10 years ago, and, in some cases, unlike those faced in any prior mathematical application.

As an example, the recent sequencing of the genomes of every principal model organism in biology, along with DNA microarray data and other high-throughput novel technologies, suggests the possibility of inferring the causal interactions among the genes, which in turn requires identifying the short sequence elements to which proteins bind (thus regulating the genetic expression).

Our lab has developed mathematical approaches for the inference (learning predictive models of transcriptional regulation and discovering regulatory sequence elements from microarray data), organization (inference of modules in biological networks and quantification of modularity), and analysis (predicting evolutionary mechanisms from network architecture) of biological networks. Mathematical approaches include the use of large deviation / large margin-based classifiers (machine learning), statistical inference, network analysis and graph theory, and information theory.

Within the context of the nanomedicine center, we are most interested in developing related approaches for 'high throughput microscopy'-statistical inference and machine learning analyses of images and movies of microscopic origin-both for extracting quantitative information from these images and for inferring biological networks. NoVel, an original software tool have been developed for automated segmentation of cells in movies (figure 1,; movie 1) and measurement and analysis of normal velocity (figures 2 and 3). Also a machine learning approach to detection of multiprotein complexes in cell motility is under development (movie 2). The associated mathematical methods are helping direct future experiments by the collaborators.

The significance of this research is not only in the application, where, via direct collaboration with biologists and clinicians, potential areas of impact include basic science, pharmacology, research into cellular mechanisms underlying immunology and cancer, and biomedicine. In addition, by developing 'data-driven' approaches to microscopy (building on experience with microarray data) and thus to cellular biology (building on experience with molecular biology), we hope to forge a much-needed bridge between the 'new biomathematics,' informed by bioinformatics and statistical learning theory, with more traditional biomathematics of microscopic and mechanistic modeling (building on our experience in such PDE-based modeling at the cellular scale). Knowledge of the underlying dependences or independences among the components of the signaling pathways (i.e, the graph structure), for example, is the prerequisite for such mechanistic modeling; a high-throughput approach to microscopy yields abundant data, which are necessary to constrain and guide these models. We contend that the resulting methods will drive new relationships among data, modeling, and biomedicine.

Background and Education

Chris Wiggins is an assistant professor of mathematical sciences in the Department of Applied Physics and Applied Mathematics at Columbia University. He is also affiliated with Columbia's Center for Computational Biology and Bioinformatics and one of the co-P.I.s in the NIH-funded MAGNet (Multiscale Analysis of Genomic and Cellular Networks) National Center for Biomedical Computation. Wiggins obtained his Ph.D. from Princeton University in theoretical physics. He was a Courant instructor at NYU, and has held visiting positions at Institut Curie (Paris), the Hahn-Meitner Institut (Berlin), and the Kavli Institute for Theoretical Physics (Santa Barbara). He moved to Columbia in 2001.

Honors and Awards

  • Division of Mathematical Sciences Postdoctoral Research Fellow (NSF)
  • I. Rabi Scholar (Columbia)

Selected Publications

Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C.
Predicting genetic regulatory response using classification.
Bioinformatics, 2004;20(suppl 1):I232-I240. q-bio/0411028.

Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C.
Predicting genetic regulatory response using classification: yeast stress response. 2004.
Proceedings of the First Annual RECOMB Regulation Workshop. q-bio/0406016.

Middendorf M, Ziv E, Adams C, Hom J, Koytcheff R, Levovitz C, Woods G, Chen L, Wiggins C.
Discriminative topological features reveal biological network mechanisms.
BMC Bioinformatics. 2004;5:181. q-bio/0402017.

Kundaje A, Middendorf M, Wiggins C, Leslie C.
Combining sequence and time series expression data to learn transcriptional modules.
IEEE Trans Computational Biol and Bioinformatics, 2005. In press.

Middendorf M, Kundaje A, Shah M, Freund Y, Wiggins C, Leslie C.
Motif discovery through predictive modeling of gene regulation. 2005.
Proceedings of Ninth Annual International Conference on Research in Computational Molecular Biology (RECOMB 2005), Lecture Notes in Bioinformatics.
Berlin: Springer-Verlag; 2005.

Middendorf M, Ziv E, Wiggins CH.
Inferring network mechanisms: the Drosophila melanogaster protein interaction network.
PNAS. 2005;102:3192-3197. q-bio/0408010.

Ziv E, Middendorf M, Wiggins CH.
Information-theoretic approach to network modularity.
Phys Rev E Stat Nonlin Soft Matter Phys, 71(4 Pt 2), Apr 2005. q-bio/0411033.

Ziv E, Koytcheff R, Middendorf M, Wiggins C.
Systematic identification of statistically significant network measures.
Physical Review E, 2005;71(1 pt 2). cond-mat/0306610.

NoVel: Cell Edge Detection

Detection of Multiprotein Complexes

Research Examples