A major open question of systems biology is how genetic and molecular components interact to create phenotypes at the cellular level. Although much recent effort has been dedicated to inferring effective regulatory influences within small networks of genes, the power of microarray bioinformatics has yet to be used to determine functional influences at the cellular level. In all cases of data-driven parameter estimation, the number of model parameters estimable from a set of data is strictly limited by the size of that set. Rather than infer parameters describing the detailed interactions of just a few genes, we chose a larger-scale investigation so that the cumulative effects of all gene interactions could be analyzed to identify the dynamics of cellular-level function. By aggregating genes into large groups with related behaviors (megamodules), we were able to determine the effective aggregate regulatory influences among 12 major gene groups in murine B lymphocytes over a variety of time steps. Intriguing observations about the behavior of cells at this high level of abstraction include: (i) a medium-term critical global transcriptional dependence on ATP-generating genes in the mitochondria, (ii) a longer-term dependence on glycolytic genes, (iii) the dual role of chromatin-reorganizing genes in transcriptional activation and repression, (iv) homeostasis-favoring influences, (v) the indication that, as a group, G protein-mediated signals are not concentration-dependent in their influence on target gene expression, and (vi) short-term-activating/long-term-repressing behavior of the cell-cycle system that reflects its oscillatory behavior.
Complex systems researchers have identified the key behavior of cells, paving the way to medical applications. Harvard University and New England Complex Systems Institute researchers Benjamin de Bivort and Yaneer Bar-Yam describe their findings in this weeks' Proceedings of the National Academy of Sciences. Using measurements of genetic activity, the researchers identified 12 major functional units of the cell and how they influence each other. These functional units bring about the energy production in the cell, the process of replication, cellular senses and other key functions.
Several years ago biologists were working hard to map the human genome. Today they are trying to understand how biological systems operate: how parts of the cell interact to make the cell function. The paper by de Bivort and Bar-Yam takes a major step forward by showing how genetic data can lead to understanding of how an entire cell works. They not only demonstrate this possibility, but actually determine the interactions between parts of the cell.
Biologists have been able to use the mapping of the genome to develop ways of seeing into the cell. The problem is that they get so much data it is hard to see what is what. For example, the data used for this study showed what 16,000 genes were doing. With all of those data, how can one figure out how genes are interacting with each other? For the first time, this paper showed how it can be done: First by grouping the genes together into modules by the similarity of their behavior; Then by looking at how the behavior of these groups changed when the cell was exposed to various chemicals.
The method used data that showed how various medically important chemicals changed the way cells behave. The researchers were able to demonstrate that some of the changes led to more changes later in time. By studying these changes, they were able to determine how the parts of the cell affect each other. Once they found this out, they could predict what the cell did when it was exposed to chemicals that were not part of the original data. Overall the experiments on which their study is based had 32 different chemical influences, but the researchers found that using 27 of these influences they could predict very accurately what happened with the rest of them. This shows that their results capture the actual behavior of the cell. Now they can predict what will happen with new chemicals.
Researchers have been optimistic that the massive amounts of data that are currently available from biological experiments will allow breakthroughs in medicine. A major obstacle, however, has been the ability to see how new drugs will affect the entire cell. The model that de Bivort and Bar-Yam developed may do just that.
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