News and Publications

Understanding the Complexity of Living Chemical Networks

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.

Publications

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.