Emergence of realistic metabolic networks from artificial chemistry

Complex metabolic networks characterize the life of every cell. In this paper we review how such metabolic networks can emerge from a model that applies optimization pressure to artificial chemistries. This model can lead to metabolic networks that share many of the features of real networks, such as cycles and hierarchies. Notice: Cover figure reproduced from [1]. Metabolism is a collection of chemical reactions that imbue cell matter with life. All life forms use chemical reactions to grow, consume resources and procreate. For example, metabolic reactions allow photosynthetic organisms to consume water, energy and carbon dioxide to produce organic compounds. Similarly, metabolic reactions allow organisms to store energy in organic molecules and release that energy when it’s needed. Metabolism, interplaying with genetics and molecular cellular machinery, is a big part of the complexity of the living cell. Understanding of metabolism leads to a better understanding of the cell, and furthermore its evolution. Some aspects of metabolism are interesting from a physics perspective. Metabolic reactions in cells connect reactants in a variety of complex ways. They can form pathways, cycles and complex networks. For instance, metabolic networks are some of the most famous examples of scale-free networks in nature [2] [3]. Scale-free networks are networks in which the distribution of node degrees is a power law (node degree is the number of node’s neighbors). These networks are associated with having ”hubs”, that is nodes in the network that have high deegrees. These hubs make the network a ”small world”, in the sense that the distance between any two nodes in the network is much smaller than that in a random network of the same number of nodes and edges. Evidently, there is a lot of complexity in the metabolic reaction networks. It is observed in nature that while different species have differing metabolic networks, some core metabolic pathways and cycles are practically universal across all life [4]. Since there is many alternative ways to combine reactants and products in metabolic reactions, a question arises: What chooses one particular metabolic pathway over its alternatives? There is evidence that metabolic reactions observed in nature are optimal. In 1981 Baldwin and Krebs reported that the efficiency of the ubiquitous citric acid cycle is more than twice that of feasible alternatives [5]. This serves as evidence that metabolic reactions may undergo evolution towards becoming more efficient. Efficiency here refers to the amount of product of a reaction. In this paper, we will investigate how such evolutionary optimization pressure can lead to emergence of metabolic pathways that seem to share many of the features of pathways found in nature. We should note that in this paper we are interested in looking at opti-