The availability of genomic sequences and the technology that is rapidly developing to exploit them have opened up new opportunities and challenges for molecular genetics. For example, it has become routine experimental practice to study expression of all the genes of an organism at once, facilitating a level of biological inference at the "system level", well beyond what is possible from studying individual genes, gene assemblies or pathways. The rapid advance of technology for studying global responses of cellular metabolism as well as gene expression, both in populations and individual cells to perturbations in growth conditions has provided sensitive new tools for studying how cells respond and adapt to their environment. The goal of our research is to understand fully how the cellular growth rate is controlled by circumstances. Ultimately, we would like to be able to produce a theory from which the responses of cells to all kinds of environmental perturbation can be predicted.

To this end we are finding ways to perturb cells, while at the same time rigorously controlling the background environment. The idea is to introduce a defined perturbation, such as a pulse of additional nutrient, and observe (and ultimately predict) the global cellular responses, including gene expression patterns, metabolite concentrations and fluxes, entry and exit from the cell division cycle. One challenge in these studies is to devise experimental systems (such as steady-state growth in chemostats or culture on filters that can easily be transferred from one medium to the next) that allow the desired perturbation while minimizing unintended changes in conditions caused by sampling methods or other manipulations. Another challenge is the very large data volumes generated by the DNA microarray, DNA sequence, fluorescence (both in the microscope in the cell sorter) and mass spectrometry technologies that we use. Not only do the data have to be collected, stored and analyzed—they also have to be cast in a form that allows experimenters to understand them.
 

  Center for Quantitative Biology

Lewis-Sigler Institute for Integrative Genomics

Department of Molecular Biology at Princeton University