Michael B. Elowitz
Professor of Biology and Bioengineering; Investigator, Howard Hughes Medical Institute; Executive Officer for Biological Engineering
Research InterestsSynthetic and systems biology; gene circuit dynamics in cell and developmental circuits
Living cells respond to their environment, communicate with one another, and develop into multicellular organisms. Our lab is interested in how these tasks are accomplished using the network of interacting genes and proteins contained in the cell. We are equally interested in the opposite question of how novel networks can be engineered within cells to implement alternative cellular behaviors. We address these complementary questions together using a combination of experimental and theoretical techniques.
One example of this approach is the Repressilator, a synthetic oscillatory network constructed in the bacteria Escherichia coli (Elowitz & Leibler, 2000). The Repressilator is designed to cause oscillations in the level of gene expression over time in individual cells. It consists of a negative feedback loop of three transcriptional repressors. When combined with a green fluorescent reporter gene, the Repressilator causes growing E. coli cells to flash periodically, or twinkle, demonstrating that oscillations can be genetically programmed. Interestingly, these programmed oscillations are far less regular than those of natural cellular clocks, such as the circadian clock that operates in many organisms. We are interested in how natural biological clocks behave so reliably, and conversely, in understanding what, if anything, limits the accuracy of synthetic genetic clocks.
A second example is our recent studies of stochasticity, or "noise," in gene regulation (Elowitz et al, 2002). and (Rosenfeld et al, 2005). Because cells are small and contain few copies of certain molecules, stochastic fluctuations in intracellular reactions are expected to be significant, and may in fact be the origin of much cell-cell variability. Noise places fundamental limits on the accuracy with which a cell can control itself. Because it has been difficult to discriminate noise from other sources of variation, we recently developed an experimental technique that enables detection of gene expression noise in vivo, using two distinguishable alleles of green fluorescent protein under the control of identical regulatory sequences in the same cell (see figure). In this image, noise causes individual cells to appear reddish or greenish, rather than yellow, which is the color they would be without noise (yellow is equal parts red and green). This approach should contribute to a quantitative understanding of how genetic elements function in the intracellular milieu. In (Rosenfeld et al, 2005) we use time-lapse movies to understand the biochemistry of gene regulation at the single cell level. In this study, we found that extrinsic noise can have a very slow correlation time -- that is, a long memory. Fluctuations persist for timescales on the order of the cell cycle time, placing fundamental limits on the accuracy of gene regulation.
Besides working reliably here and now, biological networks must change their function over evolutionary timescales. We have therefore been interested in how "difficult" it is to create and perturb network-level functions using typical regulatory genes and response elements. Recently, libraries of genetic networks differing in their patterns of activation and repression were generated (Guet et al, 2002), and screened for their ability "compute" a number of logical functions inside cells. In this way, a variety of different networks that confer on host cells the ability to respond to specific combinations of two chemical inputs were identified. This work establishes a framework for investigating the range of behaviors that can be implemented with simple, modular biological components.
The lab will build on these methodologies and develop new techniques for improved understanding of the structure and function of the genetic networks produced by evolution. At the same time, we hope to learn how to create synthetic networks that generate novel behaviors in and among cells.