Growing (micro)organisms are subject to different types of environmental changes. Some of these fluctuations are regular: for example, daily variations of light intensity. Others are stochastic, such as the random appearance of predators or toxins. Bacteria have developed an astonishing panoply of survival strategies in varying environments. In this talk, Leibler will describe some recent experimental and theoretical studies connected with microbial behavior.
About the Speaker:
Stanislas Leibler is the Gladys T. Perkins Professor and Head of the Laboratory of Living Matter at The Rockefeller University. Dr. Leibler is interested in the quantitative description of microbial systems, both on cellular and population levels.
In recent years, the field of molecular biology has moved away from the study of individual components and toward the study of how they interact, creating a “systemic” approach that seeks an appropriate and quantitative description of cells and organisms. Dr. Leibler’s laboratory is developing both the theoretical and experimental methods necessary for conducting studies on the collective behavior of biomolecules, cells and organisms. By selecting a number of basic questions on how simple genetic and biochemical networks function in bacteria, his lab is beginning to understand how individual components can give rise to complex, collective phenomena.
Recent research topics in the laboratory include quantitative studies of interacting microorganisms. In particular, the question of the survival of microbial populations in varying environments is being addressed both experimentally and theoretically. Dr. Leibler and his collaborators are developing new experimental techniques that will facilitate quantitative analysis of long-time population dynamics in microbial populations. In parallel, they are developing statistical methods for the so-called inverse problems, in which the interactions between different components of a biological system are deduced from measured statistical correlations. Long-term dynamics of closed microbial ecosystems are being analyzed by these inverse methods. Similar theoretical approaches are also applied to other types of data, such as spiking activity of retinal neuron assemblies or evolution of protein families.