Selected examples of best practice in computational biology

1. A team of researchers from Case Western Reserve University (Cleveland, Ohio; http://www.csuohio.edu/mims/index.htm) is combining computational modeling with physiological experimentation to understand the relationship between metabolism of single human cells and organ and whole body metabolism. This work is yielding computer models of metabolism in liver, heart and brain that promote evidence-based methods for clinical decision support, including diagnosis and treatment [9].

2. An industrial team at United Devices, Inc. (Austin, Texas; http://ud.com/rescenter/ and http://ud.com/rescenter/files/ds_smallpox.pdf) developed technology for massive computational screening of lead drug compounds for drugs by accessing otherwise unused computer time in a global collaborative network of desktop computers. Recently they reported that this work yielded new compounds against a smallpox protein. This work will bring new drugs into animal and human testing cheaply and quickly, yielding more effective, less expensive drugs (United Devices, Inc. http://www-unix.gridforum.org/7_APM/LSG.htm; www.ud.com/rescenter/files/ds_smallpox.pdf.)

3. A team from the University of Connecticut in Storrs, Connecticut (http://www.cbit.uchc.edu/index.html) formed the National Resource for Cell Analysis and Modeling, a nationally accessible computational environment for modeling cell functions. This environment speeds the pace of research at the cellular level by permitting researchers to readily put experimental biochemical data in the context of a computational model of a cell to understand how individual biochemical reactions give rise to coordinated functions at the pathway and cellular level [10].

4. A team from Johns Hopkins University (http://www.bme.jhu.edu/labs/levchenko) is using Monte Carlo modeling to predict biochemical signaling pathways in heart muscle cells. By using the computer-driven random walk to simulate diffusion of signaling molecules in the cell, it is possible to model cellular behavior in great detail, and thus provide a more detailed view of cell signaling. Cell signaling relates to basic and clinical research [11].

5. A team from Indiana University (http://www.indiana.edu/neurosci/sporns.html and
http://www.indiana.edu/cortex/robots.html) is developing an autonomous computational robot with learning capabilities similar to the human brain. This research is aimed at understanding principles of brain function and also at understanding brain function to build automated intelligent systems and robots that can serve human needs [12].

6. A team based at Massachusetts General Hospital/Harvard Medical School is studying malignant brain tumors as self-organizing and adaptive biosystems. Their Tumor Complexity Modeling Project (TCMP) uses methods from various disciplines, such as tumor biology, bioengineering, materials science, mathematical biology, nonlinear physics as well as computational and complex systems science. The immediate aim of TCMP is to develop novel experimental, computational, mathematical and theoretical tumor models. The ultimate goal is to develop virtual treatment planning devices and strategies for malignant brain tumors (http://btc.mgh.harvard.edu/TumorModeling/)

From Trends in Biotechnology Volume 23, Issue 3 , March 2005, Pages 113-117