Systems Approach to Biology
The traditional approach to biology has focused on understanding individual parts of a biological system in great detail, without completely addressing how these components interact with one another. A systems approach to biology promises to address this important aspect by using a highly interdisciplinary strategy involving modeling, simulation and high-throughput experiments. The advances in genomic biology, biotechnology and bioinformatics have resulted in an explosion of data and tools to analyze them.
One such example of a high-level approach to modeling gene regulation is Bayesian Network analysis. Bayesian networks are a class of tools used in machine learning that can automatically structure complex and noisy datasets into a (sometimes) human interpretable network. This network can be used to make predictions about the future, generate error estimates, and identify locations of low data density or data ambiguity. These models make no assumptions about the functional form of the phenomena, thus can capture linear as well as nonlinear, or even discontinuous phenomena equally well.
The key challenge with Bayesian network analysis is that learning Bayesian networks is incredibly computationally demanding. To get around this problem, the group is pioneering methods to handle topologically constrained networks–networks where we force part of the structure and search out the rest. Many problems in biology can be directly mapped to a topologically constrained Bayesian network problem. Many topologically constrained Bayesian network problems allow vast simplifications that allow them to be computed on only a small supercomputer.
2. Institute for Systems Biology, Systems Biology: the 21st Century Science.
3. Noble D. (2006). The Music of Life: Biology beyond the genome. Oxford University Press
One such example of a high-level approach to modeling gene regulation is Bayesian Network analysis. Bayesian networks are a class of tools used in machine learning that can automatically structure complex and noisy datasets into a (sometimes) human interpretable network. This network can be used to make predictions about the future, generate error estimates, and identify locations of low data density or data ambiguity. These models make no assumptions about the functional form of the phenomena, thus can capture linear as well as nonlinear, or even discontinuous phenomena equally well.
The key challenge with Bayesian network analysis is that learning Bayesian networks is incredibly computationally demanding. To get around this problem, the group is pioneering methods to handle topologically constrained networks–networks where we force part of the structure and search out the rest. Many problems in biology can be directly mapped to a topologically constrained Bayesian network problem. Many topologically constrained Bayesian network problems allow vast simplifications that allow them to be computed on only a small supercomputer.
Related Resources:
1. Kitano H. (2002), Systems Biology: A Brief Overview. Science2. Institute for Systems Biology, Systems Biology: the 21st Century Science.
3. Noble D. (2006). The Music of Life: Biology beyond the genome. Oxford University Press