Grip on complexity in chemical reaction networks.

Albert S Y Wong,Wilhelm T S Huck

doi:10.3762/bjoc.13.147

Abstract

A new discipline of “systems chemistry” is emerging, which aims to capture the complexity observed in natural systems within a synthetic chemical framework. Living systems rely on complex networks of chemical reactions to control the concentration of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. To truly understand how living systems function, we need a complete understanding of how chemical reaction networks (CRNs) create function. We propose the development of a bottom-up approach to design and construct CRNs where we can follow the influence of single chemical entities on the properties of the network as a whole. Ultimately, this approach should allow us to not only understand such complex networks but also to guide and control their behavior.

Highlights

Natural phenomena, such as the earth’s climate, ecosystems, animal group behavior, our brain, and living cells, are all systems that display dynamic behavior marked by an apparent complexity [1,2,3,4,5]
The development of the field of complex systems science will most likely follow a similar pattern, where we will get a grip on systems of increasing complexity
The rapid progress of computational methods will most probably allow us to tackle ever-larger complex systems. This perspective, urges an approach using a synthetic strategy based on the stepwise build-up of complex molecular systems

Summary

Introduction

Natural phenomena, such as the earth’s climate, ecosystems, animal group behavior, our brain, and living cells, are all systems that display dynamic behavior marked by an apparent complexity [1,2,3,4,5]. One of the ultimate aims for systems like a living cell, is to understand how the interplay between molecular level events and network topology determines the behavior that emerges from complex networks of chemical reactions [16].

Results

Conclusion