Computational Biology is the research field concerned with solving biological problems using mathematical and computational methods. It is grounded in theories and concepts of life, physical and quantitative sciences. The methods have their origins in various scientific computing traditions including those in physics, chemistry, engineering, and computer science. Researchers in this field must be comprehensively trained on the current state-of-the-art to acquire the quantitative background and skills to advance the field, and be in a position to appreciate the potential, strength and limitations of computational, mathematical, and engineering tools for tackling biological problems.
New initiatives are now taking shape after the completion of genome sequencing projects, such as structural genomics, functional genomics or proteomics. There is now a shift in emphasis – from sequence to structure, from genes to proteins and their complexes, from interacting pairs to interaction networks. There is also a change in the scale of the explored processes, from atomic/molecular to supramolecular, cellular and systems levels. It is now clear that researchers should develop theories and methods that extend
- beyond DNA and protein sequences, but decipher sequence-structure-function relations;
- beyond static images/structures of biomolecules, but simulate their dynamics;
- beyond single molecules/pairs, but model networks/cascades of interactions.
Furthermore there is a need for characterizing structure and function at multiple scales to establish the link between genotype and phenotype. Experiments cannot meet this goal unless conducted in coordination with theoretical, mathematical and/or computational models and methods that allow for
- high throughput analysis and organization of biological data;
- realistic visualization and simulation of complex processes.
Computational biology is being recognized as an essential and indispensable field in biomedical research. This has gained tremendous popularity in recent years due to the availability of high computing power to scientists, development of user-friendly graphical-user interfaces in commonly used software applications, and the efficiency with which these studies can be carried out relative to traditional methods.
History of training in computational science in Pittsburgh
1989 – First degrees awarded in undergraduate computational biology program at Carnegie Mellon. Courses developed for this program stimulated interest among graduate students, as well.
1999 – Mellon College of Science receives grant from the Merck Company Foundation to create a new program in computational biology and chemistry, which supported both undergraduate and graduate students, and thereby helped to stimulate development of interdisciplinary, collaborative projects. A major limitation of the Merck program was that students had to be enrolled in one of the traditional Ph.D. programs.
2001 – In March, the University of Pittsburgh School of Medicine establishes the Center for Computational Biology and Bioinformatics (CCBB), through the initiative of the Senior Vice Chancellor for the Health Sciences. The Center’s charge was to advance and disseminate computational biology methods and results, integrate the diverse activities in these areas that were ongoing across Pitt, and build additional expertise and capacity in computational solutions to key biological questions.
2004 – Built on the premise of the founding CCBB, the Department of Computational Biology (DCB) was created at the University of Pittsburgh, School of Medicine, in October 2004, to establish a home uniquely welcoming to highly interdisciplinary faculty, and to firmly establish the University of Pittsburgh in a nationally recognized leadership role in a field of tremendous growth and excitement.
2005 – A new Ph.D. program in computational biology is established. Founding Directors were Ivet Bahar and Robert F. Murphy, with the first students enrolled in Fall 2005. This is a natural consequence of the educational goals and research progress of the CCBB. In November, the program is selected as one of only ten HHMI-NIBIB Interfaces Initiative Awardees in the country.
2007 – At Carnegie Mellon, the Ray and Stephanie Lane Center for Computational Biology (LCCB) is created as the culmination of many years of development and recruiting efforts, and with the goal of realizing the potential of machine learning for expanding understanding of complex biological systems. A primary focus of the center is developing the computational tools that will enable automated creation of detailed, predictive models of biological processes, including automated experiment design and data acquisition.
2009 – CPCB was selcted as one of ten programs nationwide to receive an NIH T32 Training Grant as part of the NIBIB-HHMI Interfaces Program (award T32-EB009403).