Department of Systems Biology
Harvard Medical School
Warren Alpert Building, Room 519
200 Longwood Avenue, Boston, MA 02115
tel: (617) 432-6390; fax: (617) 432-5012
Our lab is interested in understanding the system-level architecture
of genetic networks and the interplay between their design and the evolutionary
Gene and Drug Networks
We are combining theoretical and
experimental approaches to study epistasis networks - networks that describe
how perturbations (mutations or drugs) in a given biological system are
combined to aggravate or alleviate the phenotypic consequences of each
other. Such epistatic interactions, fundamental in a range of evolutionary
processes, also helps elucidate the functional organization of complex
genetic architectures. We are developing quantitative automated experimental
tools based on bioluminescence and fluorescence measurements to achieve
en mass, yet very accurate, quantification of epistatic interaction networks
in bacteria and yeast. In a systematic study of epistasis between mutations
and environmental stresses in Escherichia coli we found that, in contrast
to the perception that stress generally reduces the organism's ability
to tolerate mutations, there exist stresses that do the opposite –
that is they alleviate the average effect of deleterious mutations. More
recently, we have used the computational method of flux balance analysis
(FBA) to study the epistasis network of yeast metabolism (Segre' et al,
2004). Our results show that the epistasis network posses a very special
property, which we term "monochromaticity", whereby functional
gene modules interact with each other with purely aggravating or purely
alleviating epistatic links. This property extends the concept of epistasis
from the gene-gene level to the system level. This new definition for
identifying functional modules is implemented in a classification algorithm
that we developed - the Prism algorithm. In drug networks, the same conceptual
method could be applied to cluster drugs by their mechanism of actions
based only on the properties of their mutual interactions.
Our evolution research is concentrating on adaptation in asexual organisms.
We have demonstrated a simple equivalence principle which, at the limit
of high mutation rates and population size, provides a projection of the
complex adaptation process onto a simple two-dimensional parameter space
of an effective mutational size and an effective mutational rate.
R. Kishony and S. Leibler (2003). Environmental stresses can alleviate
the average deleterious effect of mutations. Journal of Biology
2, p. 14.
D. Segrè, A. DeLuna, G. M. Church, R. Kishony (2005). Modular
epistasis in yeast metabolism. Nature Genetics 3,
M. Hegreness, N. Shoresh, D. Hartl, R. Kishony (2006). An Equivalence
Principle for the Incorporation of Favorable Mutations in Asexual Populations.
Science 311, p. 1615.
P. Yeh, A. Tschumi, R. Kishony (2006). Functional classification of drugs
by properties of their pair-wise interactions. Nature Genetics