Building on our ability to design and synthesise molecules and our understanding of the noncovalent interactions between these molecules, the chemical sciences are currently entering the new territory of Systems Chemistry. This young field aims to develop complex molecular systems showing emergent properties; i.e. properties that go beyond the sum of the characteristics of the individual consituents of the system. This review gives an impression of the state of the art of the field by showing a diverse number of recent highlights, including out-of-equilibrium self-assembly, chemically fuelled molecular motion, compartmentalised chemical networks and designed oscillators. Subsequently a number of current challenges related to the design of complex chemical systems are discussed, including those of creating concurrent formation-destruction systems, continuously maintaining chemical systems away from equilibrium, incorporating feedback loops and pushing replication chemistry away from equilibrium. Finally, the prospects for Systems Chemistry are discussed including the tantalizing vision of the de novo synthesis of life and the idea of self-synthesising and self-repairing chemical factories.

Grafting organic molecules onto solid surfaces can transfer molecular properties to the solid. We describe how modifications of semiconductor or metal surfaces by molecules with systematically varying properties can lead to corresponding trends in the (electronic) properties of the resulting hybrid (molecule + solid) materials and devices made with them. Examples include molecule-controlled diodes and sensors, where the electrons need not to go through the molecules (action at a distance), suggesting a new approach to molecule-based electronics.

The fundamental roles that peptides and proteins play in today’s biology makes it almost indisputable that peptides were key players in the origin of life. Insofar as it is appropriate to extrapolate back from extant biology to the prebiotic world, one must acknowledge the critical importance that interconnected molecular networks, likely with peptides as key components, would have played in life’s origin. In this review, we summarize chemical processes involving peptides that could have contributed to early chemical evolution, with an emphasis on molecular interactions between peptides and other classes of organic molecules. We first summarize mechanisms by which amino acids and similar building blocks could have been produced and elaborated into proto-peptides. Next, non-covalent interactions of peptides with other peptides as well as with nucleic acids, lipids, carbohydrates, metal ions, and aromatic molecules are discussed in relation to the possible roles of such interactions in chemical evolution of structure and function. Finally, we describe research involving structural alternatives to peptides and covalent adducts between amino acids/peptides and other classes of molecules. We propose that ample future breakthroughs in origin-of-life chemistry will stem from investigations of interconnected chemical systems in which synergistic interactions between different classes of molecules emerge.

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...