Open questions in understanding life’s origins

PLOS Computational Biology

Prior work on abiogenesis, the emergence of life from non-life, suggests that it requires chemical reaction networks that contain self-amplifying motifs, namely, autocatalytic cores. However, little is known about how the presence of multiple autocatalytic cores might allow for the gradual accretion of complexity on the path to life. To explore this problem, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical “seeds.” We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks an...

Journal of Theoretical Biology, 2020

It is becoming widely accepted that very early in life's origin, even before the emergence of genetic encoding, reaction networks of diverse small chemicals might have manifested key properties of life, namely self-propagation and adaptive evolution. To explore this possibility, we formalize the dynamics of chemical reaction networks within the framework of chemical ecosystem ecology. To capture the idea that lifelike chemical systems are maintained out of equilibrium by fluxes of energy-rich food chemicals, we model chemical ecosystems in wellmixed containers that are subject to constant dilution by a solution with a fixed concentration of food chemicals. Modelling all chemical reactions as fully reversible, we show that seeding an autocatalytic cycle with tiny amounts of one or more of its member chemicals results in logistic growth of all member chemicals in the cycle. This finding justifies drawing an instructive analogy between an autocatalytic cycle and the population of a biological species. We extend this finding to show that pairs of autocatalytic cycles can show competitive, predator-prey, or mutualistic associations just like biological species. Furthermore, when there is stochasticity in the environment, particularly in the seeding of autocatalytic cycles, chemical ecosystems can show complex dynamics that can resemble evolution. The evolutionary character is especially clear when the network architecture results in ecological precedence ("survival of the first"), which makes the path of succession historically contingent on the order in which cycles are seeded. For all its simplicity, the framework developed here is helpful for visualizing how autocatalysis in prebiotic chemical reaction networks can yield lifelike properties. Furthermore, chemical ecosystem ecology could provide a useful foundation for exploring the emergence of adaptive dynamics and the origins of polymer-based genetic systems.

arXiv (Cornell University), 2011

Can we objectively distinguish chemical systems that are able to process meaningful information from those that are not suitable for information processing? Here, we present a formal method to assess the semantic capacity of a chemical reaction network. The semantic capacity of a network can be measured by analyzing the capability of the network to implement molecular codes. We analyzed models of real chemical systems (Martian atmosphere chemistry and various combustion chemistries), bio-chemical systems (gene expression, gene translation, and phosphorylation signaling cascades), as well as an artificial chemistry and random networks. Our study suggests that different chemical systems posses different semantic capacities. Basically no semantic capacity was found in the atmosphere chemistry of Mars and all studied combustion chemistries, as well as in highly connected random networks, i.e., with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the bio-chemical systems, as well as in random networks where the number of second order reactions is at the number of species. Hypotheses concern the origin and evolution of life. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g., at the origin of life.

Life, 2018

Life is more than the sum of its constituent molecules. Living systems depend on a particular chemical organization, i.e., the ways in which their constituent molecules interact and cooperate with each other through catalyzed chemical reactions. Several abstract models of minimal life, based on this idea of chemical organization and also in the context of the origin of life, were developed independently in the 1960s and 1970s. These models include hypercycles, chemotons, autopoietic systems, (M,R)-systems, and autocatalytic sets. We briefly compare these various models, and then focus more specifically on the concept of autocatalytic sets and their mathematical formalization, RAF theory. We argue that autocatalytic sets are a necessary (although not sufficient) condition for life-like behavior. We then elaborate on the suggestion that simple inorganic molecules like metals and minerals may have been the earliest catalysts in the formation of prebiotic autocatalytic sets, and how RAF...

2020

It is becoming widely accepted that very early in the origin of life, even before the emergence of genetic encoding, reaction networks of diverse small chemicals might have manifested key properties of life, namely self-propagation and adaptive evolution. To explore this possibility, we formalize the dynamics of chemical reaction networks within the framework of chemical ecosystem ecology. To capture the idea that life-like chemical systems are maintained out of equilibrium by fluxes of energy-rich food chemicals, we model chemical ecosystems in well-mixed containers that are subject to constant dilution by a solution with a fixed concentration of food chemicals. Modelling all chemical reactions as fully reversible, we show that seeding an autocatalytic cycle (AC) with tiny amounts of one or more of its member chemicals results in logistic growth of all member chemicals in the cycle. This finding justifies drawing an instructive analogy between an AC and the population of a biologic...

Nature

In a cell or microorganism the processes that generate mass, energy, information transfer, and cell fate specification are seamlessly integrated through a complex network of various cellular constituents and reactions 1. However, despite the key role these networks play in sustaining various cellular functions, their large-scale structure is essentially unknown. Here we present the first systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variances in their individual constituents and pathways, these metabolic networks display the same topologic scaling properties demonstrating striking similarities to the inherent organization of complex non-biological systems 2. This suggests that the metabolic organization is not only identical for all living organisms, but complies with the design principles of robust and errortolerant networks 2-5 , and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents. An important goal in biology is to uncover the fundamental design principles that provide the common underlying structure and function in all cells and microorganisms 6-13. For example, it is increasingly appreciated that the robustness of various cellular processes seen in a cell or microorganism is rooted in the dynamic interactions among its many constituents 14, 15 , such as proteins, DNA, RNA, and small molecules. Yet, the large-scale design principles that integrate these interactions into a complex system are poorly understood 1. Recent scientific developments, however, significantly improve our ability to identify such principles. Large-scale genome sequencing projects have provided complete sequence information for more than two dozen prokaryotes and a few

Life

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

Proceedings of the Royal Society B: Biological Sciences

Modern cells embody metabolic networks containing thousands of elements and form autocatalytic sets of molecules that produce copies of themselves. How the first self-sustaining metabolic networks arose at life's origin is a major open question. Autocatalytic sets smaller than metabolic networks were proposed as transitory intermediates at the origin of life, but evidence for their role in prebiotic evolution is lacking. Here, we identify reflexively autocatalytic food-generated networks (RAFs)—self-sustaining networks that collectively catalyse all their reactions—embedded within microbial metabolism. RAFs in the metabolism of ancient anaerobic autotrophs that live from H 2 and CO 2 provided with small-molecule catalysts generate acetyl-CoA as well as amino acids and bases, the monomeric components of protein and RNA, but amino acids and bases without organic catalysts do not generate metabolic RAFs. This suggests that RAFs identify attributes of biochemical origins conserved i...

Molecular dynamics leading to the emergence of life could have been implemented in synthesizing molecular imprints of the supporting reaction networks. What is unique to the occurrence of the network chemical reactions rests upon the network activity of molecular assimilation of the required resources into the network as recruiting them from its outside. Once the network reaction gets started, some of the underlying chemical reactions can become autocatalytic due to the presence of the network-catalytic operation round a chemical reaction cycle. An exponential growth of a certain molecular imprint of the supporting reaction cycle can proceed up to the point where the available resources to be assimilated turn out depleted. Then, the takeover by a de novo reaction network feeding upon the previous molecular imprints that the preceding network could synthesize through molecular assimilation would become inevitable, with a necessary consequence of an alternation of the molecular imprints of the reaction network successively available. An empirical example of the network chemical reactions in prebiotic conditions could have been found in hydrothermal circulation of seawater through hot vents in the Haedean ocean on the primitive Earth.

Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronised fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organisation of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualise RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide...