Journal of Systems Chemistry

Today most chemists think that the answer to how life on earth emerged is still unknown. They assume a gap between chemistry and biology that is still unbridged. For chemists, understanding the origin of life requires the experimental modeling of a process that bridges this gap. They will not consider the problem solved before they are able to perform such tasks. No gap appears when we are pursuing a less ambitious goal, namely, to present a sequence of hypothetical processes that lead to an apparatus with the basic structure and fundamental feature of the genetic apparatus of biosystems but strongly simplified. The modeled apparatus has the basic machinery of living entities. Its fundamental feature is Darwinian behavior. Living individuals have the power to evolve toward ever increasing complexity and intricacy if appropriate conditions are given. The task to understand life's origin as a rational process is closely related to the earlier attempts of the present author to design and construct supra-molecular machines. The skill of the experimentalist has to be replaced by the presence of very particular conditions given by chance in a very particular location. The resulting apparatus has a distinct basic structure and function. The essence of what happens is inevitable, not accidental. Thus the emergence of life is assumed to be described by a distinct theory. Today's great challenge is experimentally investigating chemical systems with the goal of creating artificial chemical life and the given theory provides a powerful stimulus. Life, from the perspective of physics, is the living state of matter and this view calls for a theory describing the fundamental requirements for the appearance of such a living state of matter (on the early earth and in the universe). The approach given here is an attempt in this direction. According to that approach the appearance of an entity with Darwinian behavior is instantaneous and linked with the creation of matter that carries information. Thus, Information (measured in bits according to Shannon) takes a meaning with that instant, the appearance of the first entity that evolves by multiplication, variation, selection and keeps that meaning during the entire evolution of the living (Information carrying) state of matter. Another consequence of this initial event is a spontaneous symmetry breaking due to the equal probabilities that the oligomer starting the process is right handed or left handed.

While studying fatty acid vesicles as model primitive cell membranes, we encountered a dramatic phenomenon in which light triggers the sudden rupture of micron-scale dye-containing vesicles, resulting in rapid release of vesicle contents. We show that such vesicle explosions are caused by an increase in internal osmotic pressure mediated by the oxidation of the internal buffer by reactive oxygen species (ROS). The ability to release vesicle contents in a rapid, spatio-temporally controlled manner suggests many potential applications, such as the targeted delivery of cancer chemotherapy drugs, and the controlled deposition of functionalized nanoparticles in microfluidic devices. Recent observations of light-triggered lysosome rupture in vivo suggest the possibility that a common mechanism may underlie light-triggered vesicle explosions and lysosome rupture.

A self-replicating system based on a cycloaddition of a fulvene derivative and a maleinimide is investigated using a two-pronged approach combining NMR spectroscopy with computer simulations. In the course of the reaction, two diastereomers are formed with identical rates in the absence of replication. When replication is enabled, a network emerges in which one diastereomer takes over the resources as a "selfish" autocatalyst while exploiting the competitor as a weak "altruist". The structure and dynamics of the reaction network is studied using 1 D and 2 D NMR techniques supported by dynamically averaged ab initio chemical shifts and ab initio molecular dynamics simulations. It is shown that this combination is a powerful means to understand the observed experimental behaviour in great detail.

A central unsolved problem in early evolution concerns self-organization towards higher complexity in chemical reaction networks. In theory, autocatalytic sets have useful properties to help model such transitions. Autocatalytic sets are chemical reaction systems in which molecules belonging to the set catalyze the synthesis of other members of the set. Given an external supply of starting molecules – the food set – and the conditions that (i) all reactions are catalyzed by at least one molecule, and (ii) each molecule can be constructed from the food set by a sequence of reactions, the system becomes a reflexively autocatalytic food-generated network (RAF set). Autocatalytic networks and RAFs have been studied extensively as mathematical models for understanding the properties and parameters that influence self-organizational tendencies. However, despite their appeal, the relevance of RAFs for real biochemical networks that exist in nature has, so far, remained virtually unexplored. Here we investigate the best-studied metabolic network, that of Escherichia coli, for the existence of RAFs. We find that the largest RAF encompasses almost the entire E. coli cytosolic reaction network. We systematically study its structure by considering the impact of removing catalysts or reactions. We show that, without biological knowledge, finding the minimum food set that maintains a given RAF is NP-complete. We apply a randomized algorithm to find (approximately) smallest subsets of the food set that suffice to sustain the original RAF. The existence of RAF sets within a microbial metabolic network indicates that RAFs capture properties germane to biological organization at the level of single cells. Moreover, the interdependency between the different metabolic modules, especially concerning cofactor biosynthesis, points to the important role of spontaneous (non-enzymatic) reactions in the context of early evolution.

The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reactions upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, a genetic system that encodes these enzymes, and a fitness function that can be selected for. We describe here a simulation environment that implements all these components in a simplified way so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results. The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy universe.

A classical problem in metabolic design is to maximize the production of a desired compound in a given chemical reaction network by appropriately directing the mass flow through the network. Computationally, this problem is addressed as a linear optimization problem over the flux cone. The prior construction of the flux cone is computationally expensive and no polynomial-time algorithms are known. Here we show that the output maximization problem in chemical reaction networks is NP-complete. This statement remains true even if all reactions are monomolecular or bi-molecular and if only a single molecular species is used as influx. As a corollary we show, furthermore, that the detection of autocatalytic species, i.e., types that can only be produced from the influx material when they are present in the initial reaction mixture, is an NP-complete computational problem. Hardness results on combinatorial problems and optimization problems are important to guide the development of computational tools for the analysis of metabolic networks in particular and chemical reaction networks in general. Our results indicate that efficient heuristics and approximate algorithms need to be employed for the analysis of large chemical networks since even conceptually simple flow problems are provably intractable.

Abstract Background The concept of an autocatalytic set of molecules has been posited theoretically and demonstrated empirically with catalytic RNA molecules. For this concept to have significance in a realistic origins-of-life scenario, it will be important to demonstrate the evolvability of such sets. Here, we employ a Gillespie algorithm to improve and expand on previous simulations of an empirical system of self-assembling RNA fragments that has the ability to spontaneously form autocatalytic networks. We specifically examine the role of serial transfer as a plausible means to allow time-dependent changes in set composition, and compare the results to equilibrium, or “batch” scenarios. Results We show that the simulation model produces results that are in close agreement with the original experimental observations in terms of generating varying autocatalytic (sub)sets over time. Furthermore, the model results indicate that in a “batch” scenario the equilibrium distribution is largely determined by competition for resources and stochastic fluctuations. However, with serial transfer the system is prevented from reaching such an equilibrium state, and the dynamics are mostly determined by differences in reaction rates. This is a consistent pattern that can be repeated, or made stronger or weaker by varying the reaction rates or the duration of the transfer steps. Increasing the number of molecules in the simulation actually strengthens the potential for selection. Conclusions These simulations provide a more realistic emulation of wet lab conditions using self-assembling catalytic RNAs that form interaction networks. In doing so, they highlight the potential evolutionary advantage to a prebiotic scenario that involves cyclic dehydration/rehydration events. We posit that such cyclicity is a plausible means to promote evolution in primordial autocatalytic sets, which could later lead to the establishment of individual-based biology.

Abstract Background The assessment of molecular similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an experimental method for similarity assessment based on dynamic combinatorial chemistry. Results In order to assess molecular similarity directly in solution, a dynamic molecular network was used in a two-step process. First, a clustering analysis was employed to determine the network’s innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic molecular network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related molecules. After training, it was also able to classify unknown molecules based on the presence or absence of an ethylamine group. Conclusions This is the first step in the development of molecular networks capable of predicting bioactivity based on an assessment of molecular similarity.