Design of a directed molecular network

Abstract

An ability to rationally design complex networks from the bottom up can offer valuable quantitative model systems for use in gaining a deeper appreciation for the principles governing the self-organization and functional characteristics of complex systems. We report herein the de novo design, graph prediction, experimental analysis, and characterization of simple self-organized, nonlinear molecular networks. Our approach makes use of the sequencedependant auto-and cross-catalytic functional characteristics of template-directed peptide fragment condensation reactions in neutral aqueous solutions. Starting with an array of 81 sequence similar 32-residue coiled-coil peptides, we estimated the relative stability difference between all plausible A2B-type coiled-coil ensembles and used this information to predict the auto-and crosscatalysis pathways and the resulting plausible network motif and connectivities. Similar to most complex systems, the generated graph displays clustered nodes with an overall hierarchical architecture. To test the validity of the design principles used, nine nodes composing a main segment of the graph were experimentally analyzed for their capacity in establishing the predicted network connectivity. The resulting self-organized chemical network is shown to display 25 directed edges in good agreement with the graph analysis estimations. Moreover, we show that by varying the system parameters (presence or absence of certain substrates or templates), its operating network motif can be altered, even to the extremes of turning pathways on or off. We suggest that this approach can be expanded for the construction of large-scale networks, offering a means to study and to understand better the emergent, collective behaviors of networks. N etworks appear in numerous aspects of the world we live in, from the large-scale ecological systems, social networks, and World Wide Web, to the microscopic biochemical networks of living cells (1-15). Recent breakthroughs in graph-theoretic analysis have provided a revealing global view of the architectural features of complex networks. Statistical analyses suggest that most complex networks, including metabolic and proteomic networks, have scale-free topology (1-5). Unlike regular or random network topologies, scale-free networks exhibit both relatively short average distances between any two nodes and high clustering coefficients by having a few highly connected nodes. For instance, in biological networks some proteins act as hubs to engage in a large number of interactions with other proteins, whereas the majority of proteins seem to behave as links and partake in only one or a few interactions. This top-down view of complex systems provides key boundary conditions on network topology and functional properties but gives relatively few details and only a static view of the system (6, 11). On the other hand, from the bottom-up perspective, it is often possible to gather detailed information about the properties of the individual components of a network. For insta...

Associative learning in biochemical networks

“…The simplest biochemical networks can be constructed from RNA (Kim and Joyce, 2004), DNA (Sievers and von Kiedrowski, 1994;Sievers and von Kiedrowski, 1998) or peptide (Ashkenasy et al, 2004;Lee et al, 1997b;Yao et al, 1998) molecules that exploit non-enzymatic replication as recurring elements (Lee et al, 1997a;Paul and Joyce, 2004). It was shown recently that modules of such networks perform (simple) computational algorithms such as reciprocal replication (Ashkenasy et al, 2004;Kim and Joyce, 2004;Sievers and von Kiedrowski, 1998) and Boolean logic functions (Ashkenasy et al, 2004).…”

“…It was shown recently that modules of such networks perform (simple) computational algorithms such as reciprocal replication (Ashkenasy et al, 2004;Kim and Joyce, 2004;Sievers and von Kiedrowski, 1998) and Boolean logic functions (Ashkenasy et al, 2004). The molecular setup described below is customized to include the different steps of the associative memory algorithm.…”

Lifeness signatures and the roots of the tree of life

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Autocatalytic Sets and the Origin of Life

“…Despite this criticism, the original claim (including its disputed argument) still seems to persist [30]. However, there is evidence that simple autocatalytic sets can actually be constructed experimentally [31][32][33]. This evidence, together with our own results reviewed below, make it at least plausible that autocatalytic sets indeed played a role in the emergence of proteins and DNA from an RNA world [34].…”