My group applies advanced computational and mathematical techniques to problems in modern biology and public health. We develop analysis algorithms, software tools and models that help biologists to characterize biological systems. For example consider the Plasmodium falciparum parasite, which is responsible for an estimated 350–500 million annual episodes of clinical malaria, with more than 1 million deaths per year. The life cycle of this parasite is extraordinarily complex and exhibits stage-specific regulation of nearly all its genes. The regulatory mechanisms of gene expression, if they can be identified and characterized, are a potentially rich source of targets for drugs and vaccines.

How the parasite performs such extensive regulation remains largely unknown, and may be quite unconventional. Several lines of evidence suggest that non-coding RNAs (ncRNAs) may play a significant role in regulating genome-wide expression patterns in this parasite. With funding from a Malaria Research Institute pilot grant, our group is developing an ab initio computational strategy for detecting rapidly evolving ncRNA genes.

Toxoplasma gondii is an obligate intracellular parasite that causes encephalitis, pneumonia, myocarditis, ocular disease, and congenital birth defects. We have collaborated with the Carruthers lab to develop mathematical models of the host-parasite interaction during the host-cell invasion stage.

Bioinformatics, Computational Biology, Mass Spectrometry, Protein Identification, Proteomics, Neural Networks, Machine Learning, Signal Processing, Statistical Physics

Fernando J. Pineda, Miquel D. Antoine, Plamen A. Demirev, Andrew B. Feldman, Joany Jackman, Melissa Longenecker and Jeffrey S. Lin, "Microorganism Identification by Matrix-Assisted Laser/Desporption Ionization Mass Spectrometry and Model-Derived Ribosomal Protein Biomarkers", Analytical Chemistry, (in Press)

Fernando J. Pineda, G. Cauwenberghs, and R. T. Edwards, U.S. Patent no. 6,389,377, "Methods and apparatus for acoustic transient processing", May 14, (2002).

Plamen A. Demirev, Jeffrey S. Lin, Fernando J. Pineda and Catherine Fenselau, Bioinformatics and mass spectrometry for microorganism identification: Proteome-wide post-translational modifications and database search algorithms for characterization of intact H. pylori, Analytical Chemistry, 73, pp. 4566-4573, (2001).

Fernando J. Pineda, I-Jeng Wang, Cheryl Resch, Saddle-point methods for Approximate Inference in Bayesian Belief Networks, Book Chapter in David Saad and Manfred Opper (Eds.), Advanced Mean Field Methods - Theory and Practice, MIT Press, (2001).

Fernando J. Pineda, Jeffrey S. Lin, Catherine Fenselau and Plamen A. Demirev, Testing the Significance of Microorganism Identification by Mass Spectrometry and Proteome Database Search, Analytical Chemistry, 72, pp. 3739-3744, (2000).

Fernando J. Pineda, Mean-Field Theory for Batched TD-lambda, Neural Computation, 9, pp.1403-1420, (1997).

Fernando J. Pineda, Recurrent Back-propagation Networks, book chapter in Y. Chauvin & D. Rumelhart (Eds.) , Back-propagation: Theory, Architectures and Applications, Earlbaum Associates, (1995).

Fernando J. Pineda and J.C. Sommerer, A Fast Algorithm for Estimating Generalized Dimensions and Choosing Time Delays, book chapter in Andreas Weigend and Neil Gershenfeld, (Eds.) Time series Prediction: Forecasting the Future and Understanding the Past, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, (1994).

Pierre Baldi and Fernando J. Pineda, Contrastive Learning and Neural Oscillations, Neural Computation, 3, pp. 526-545, (1991).

Fernando J. Pineda, Dynamics and Architecture in Neural Computation, Journal of Complexity, (Special issue on Neural Networks), 4, pp. 216-245, (1988).

Fernando J. Pineda, Generalization of Backpropagation to Recurrent Neural Networks, Physical Review Letters, 18, pp. 2229-2232, (1987).