The Prebiotic Kitchen: A Guide to Composing Prebiotic Soup Recipes to Test Origins of Life Hypotheses

“Prebiotic soup” often features in discussions of origins of life research, both as a theoretical concept when discussing abiological pathways to modern biochemical building blocks and, more recently, as a feedstock in prebiotic chemistry experiments focused on discovering emergent, systems-level processes such as polymerization, encapsulation, and evolution. However, until now, little systematic analysis has gone into the design of well-justified prebiotic mixtures, which are needed to facilitate experimental replicability and comparison among researchers. This paper explores principles that should be considered in choosing chemical mixtures for prebiotic chemistry experiments by reviewing the natural environmental conditions that might have created such mixtures and then suggests reasonable guidelines for designing recipes. We discuss both “assembled” mixtures, which are made by mixing reagent grade chemicals, and “synthesized” mixtures, which are generated directly from diversity-generating primary prebiotic syntheses. We discuss different practical concerns including how to navigate the tremendous uncertainty in the chemistry of the early Earth and how to balance the desire for using prebiotically realistic mixtures with experimental tractability and replicability. Examples of two assembled mixtures, one based on materials likely delivered by carbonaceous meteorites and one based on spark discharge synthesis, are presented to illustrate these challenges. We explore alternative procedures for making synthesized mixtures using recursive chemical reaction systems whose outputs attempt to mimic atmospheric and geochemical synthesis. Other experimental conditions such as pH and ionic strength are also considered. We argue that developing a handful of standardized prebiotic recipes may facilitate coordination among researchers and enable the identification of the most promising mechanisms by which complex prebiotic mixtures were “tamed” during the origin of life to give rise to key living processes such as self-propagation, information processing, and adaptive evolution. We end by advocating for the development of a public prebiotic chemistry database containing experimental methods (including soup recipes), results, and analytical pipelines for analyzing complex prebiotic mixtures.

A structure–function based approach to floc hierarchy and evidence for the non-fractal nature of natural sediment flocs

Natural sediment flocs are fragile, highly irregular, loosely bound aggregates comprising minerogenic and organic material. They contribute a major component of suspended sediment load and are critical for the fate and flux of sediment, carbon and pollutants in aquatic environments. Understanding their behaviour is essential to the sustainable management of waterways, fisheries and marine industries. For several decades, modelling approaches have utilised fractal mathematics and observations of two dimensional (2D) floc size distributions to infer levels of aggregation and predict their behaviour. Whilst this is a computationally simple solution, it is highly unlikely to reflect the complexity of natural sediment flocs and current models predicting fine sediment hydrodynamics are not efficient. Here, we show how new observations of fragile floc structures in three dimensions (3D) demonstrate unequivocally that natural flocs are non-fractal. We propose that floc hierarchy is based on observations of 3D structure and function rather than 2D size distribution. In contrast to fractal theory, our data indicate that flocs possess characteristics of emergent systems including non-linearity and scale-dependent feedbacks. These concepts and new data to quantify floc structures offer the opportunity to explore new emergence-based floc frameworks which better represent natural floc behaviour and could advance our predictive capacity.

Systems biology is a method for analyzing massive amounts of multidimensional data generated by omics technologies. Cross-validation of the various technological platforms is critical

The value of systems biology in cardiology is becoming more recognized. There has been a tremendous rise in the number of articles in the last two decades, as publicly available datasets have been provided online and high-throughput tissue analysis has become more prevalent. In animal models, however, the future of cardiovascular medicine is less likely to be reanalyzing data and more likely to be investigating the function of GWAS-identified SNPs or network change using informatics and gene-editing technologies. These techniques, when combined with other omics interrogations and rigorous experimental design, have the potential to improve our understanding of gene-to-disease pathways.Systems biology is a method for studying large amounts of multidimensional data generated by omics technologies and, more broadly, the transition to big data in health care.Cross-validation of the various technological platforms is critical because omics studies are prone to bias and overinterpretation.Investigators must carefully determine which publicly accessible datasets, if any, to employ while conducting a systems analysis. Despite the fact that network theory and machine learning may yield amazing outcomes, these methods are not yet standardized. The studies mentioned here are excellent examples, in part because they use empirical models to support emergent systems biology results. In the few successful cases, careful experimental design, including interventional research and clinical trials, is required, in addition to the insights supplied by bioinformatics analysis of omics approaches. While it may be tempting to use emergent qualities to capture these new discoveries in more fundamental concepts, we agree with the English philosopher William of Ockham when he says, "It is futile to do with more things what can be done with fewer."

Communicating artificial neural networks develop efficient color-naming systems

Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. Focusing on the well-studied color domain, we show that artificial neural networks trained with deep-learning techniques to play a discrimination game develop communication systems whose distribution on the accuracy/complexity plane closely matches that of human languages. The observed variation among emergent color-naming systems is explained by different degrees of discriminative need, of the sort that might also characterize different human communities. Like human languages, emergent systems show a preference for relatively low-complexity solutions, even at the cost of imperfect communication. We demonstrate next that the nature of the emergent systems crucially depends on communication being discrete (as is human word usage). When continuous message passing is allowed, emergent systems become more complex and eventually less efficient. Our study suggests that efficient semantic categorization is a general property of discrete communication systems, not limited to human language. It suggests moreover that it is exactly the discrete nature of such systems that, acting as a bottleneck, pushes them toward low complexity and optimal efficiency.

Integrative emergence in contrast to separating modularity in plant biology: views on systems biology with information, signals and memory at scalar levels from molecules to the biosphere

Modularity is reductionism and materialism, where modules are considered as building blocks per se. By contrast self-organization of modules in living organisms, like plants, generates the emergence of integrated systems with new properties not predicted by the properties of the modules. This can occur at the hierarchy of a series of scalar levels, where emergent systems become modules for emergence of new systems on the next higher scalar level akin to a hierarchy of networks from molecules, cells and individuals up to the levels of ecosystems, biomes and the entire biosphere or Gaia. The systems on these levels are holobiont-like systems, i.e., central organisms in interaction with all their associated organisms as a unit for selection in evolution. Systems biology, now a modern aspect of plant biology, has started with the advancement of whole-plant physiology in the early 1970s unraveling the roles of signaling for integration and cooperation of parts or modules in the performance of entire plants. Fixation of information in plant memory and emergence from such storage rules the timing of events of emergence. With the enthusiasm promoted by the creative self-organization of modules into the emergence of exciting new systems, biology diverts from the reductionism and materialism of bare modularity. Understanding emergence helps to advance on the rocky paths towards understanding the complexity of life.

Burke, Edgeworth, and Ireland’s Discontents

This chapter re-examines the party-political career of Edmund Burke and the writings of Maria Edgeworth in relation to a deep history of Anglo-Irish ‘discontents’ and their challenges to the ‘count’ of politics. Complicating ‘Burkean’ appeals to hierarchy and order, the chapter uncovers the conflicted party identity that is apparent within writings by and about Edmund Burke, returning to view the various channels of feeling engaged, for example, during his involvement in debates over ‘absentee’ landlords. The chapter goes on to give a reading of The Absentee (1812) that calls attention to recalcitrant elements that exceed systems of representation in Edgeworth’s novel, which remains animated in this reading by those elements left behind, in both senses, by emergent systems of governance. The chapter’s opening section speculates about the role of biography in Lewis Namier’s History of Parliament and asks how the novel form, in the hands of women writers, provided unique vantage points on political systems organized around men.