News and Publications
The Skaggs Institute for Chemical Biology
Scientific Report 1999-2000
Understanding the Complexity of Living Chemical Networks
M.R. Ghadiri, D.T.Y. Bong, M. Crnogorac, A. Chavochi, A. Saghatelian, K. Soltani,
M. Miyake, Y. Yokobayashi
Understanding the molecular basis of life has been the central goal of chemical
and biological sciences. The coupling of sequencing of the whole genome with
the new and emerging technologies of functional analyses of gene products is
expected to provide the basic chemical map of living systems. However, although
this information is necessary for complete understanding of the molecular basis
of life, it is not sufficient to answer the central question of how inanimate
chemical transformations manifest into a living being.
We think the answer can be found by understanding the self-organized nonlinear
chemical dynamics of a living cell. This fundamental level of knowledge is essential
for the continued ability to discover and produce novel therapeutic agents, identify
essential targets for genetic interventions, provide mechanisms for early disease
diagnostics, and formulate the basis of the aging process--essentially, every
imaginable process associated with living organisms.
Our research program has 2 main goals. The first is to discover and understand
the mechanisms that transform inanimate chemical transformations into the animate
chemical characteristics of living systems. The second is to develop and discover
chemical methods that can influence the self-organized dynamics of living cells
at the level of the protein network.
To reach the first goal, we are developing model informational nonlinear
chemical systems. Our approach has been to rationally design and re-create in
the laboratory various basic forms of autocatalytic chemical networks--the postulated
first step in the transition from inanimate to animate--and study how the interplay
of molecular information and nonlinear catalysis can lead to self-organization
of molecular systems and the expression of emergent properties. The required
basic method, an informational nonlinear chemical system, was recently established
through rational design of amide bond-forming synthetic peptide catalysts (ligases
and replicases; Fig. 1). These catalysts efficiently promote sequence-specific
condensation of peptide fragments under neutral aqueous solution conditions and
are useful in the design and study of the primary forms of self-organized chemical
networks (Fig. 2).
In one study, we showed that a small number of reactants can combine to spontaneously
form an "autocratic" network that has dynamic error-correction as its emergent
property. The system can sense the formation of mistakes (mutant peptides) and
respond by upregulating the production of the native replicator sequence. In
another study, a complex "mutualistic" network was formed in which replicators
joined to enhance each other's production and in turn each individual replicator's
chance of survival. This system is the first and only known example of symbiosis
at the molecular level.
More recently, we showed formation of a pure "reciprocal" network in which
each species in the autocatalytic set acts as a specific catalyst for the formation
of the other species (reciprocation). Although none of the species alone is capable
of self-replication, the overall network effectively reproduces itself. The reciprocal
network is the simplest example of how an emergent property (in this case, self-reproduction)
can arise out of coupled catalytic cycles. Current efforts focus on the design,
discovery, and characterization of more complex networks and molecular ecosystems.
To reach our second goal, we are using protein expression libraries to discover
molecular agents that can downregulate or upregulate gene functions at the protein
network level according to particular and selectable cell phenotypes. For example,
in one approach, we are directing our efforts to halt the cell cycle selectively
in transformed (cancerous) mammalian cell lines. We think that this strategy
will result in the identification of multiple pathways and protein targets that
can be used to develop novel therapeutic agents. We hope that by studying synthetic
model systems and analyzing protein networks in whole cells, we can produce a
blueprint for better understanding of living chemistry.
Bong, D.T., Janshoff, A., Steinem, C., Ghadiri, M.R. Membrane partitioning
of the cleavage peptide in flock house virus. Biophys. J. 78:839, 2000.
Bong, D.T., Steinem, C., Janshoff, A., Johnson, J.E., Ghadiri, M.R.
A highly membrane active peptide in flock house virus: Implications for the mechanism
of nodavirus infection. Chem. Biol. 6:473, 1999.
Janshoff, A., Bong, D.T., Steinem, C., Johnson, J.E., Ghadiri, M.R. An
animal virus-derived peptide switches membrane morphology: Possible relevance
to nodaviral transfection processes. Biochemistry 38:5328, 1999.