Conceptualizing the origin of life in terms of evolution

WITHDRAWN: Structural Enzymology, Phylogenetics, Differentiation, and Symbolic Reflexivity at the Dawn of Biology

This manuscript was posted without the final consent of all authors. The authors have therefore withdrawn it. The authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author, carter@med.unc.edu .

Selectively advantageous instability in biotic and pre-biotic systems and implications for evolution and aging

Rules of biology typically involve conservation of resources. For example, common patterns such as hexagons and logarithmic spirals require minimal materials, and scaling laws involve conservation of energy. Here a relationship with the opposite theme is discussed, which is the selectively advantageous instability (SAI) of one or more components of a replicating system, such as the cell. By increasing the complexity of the system, SAI can have benefits in addition to the generation of energy or the mobilization of building blocks. SAI involves a potential cost to the replicating system for the materials and/or energy required to create the unstable component, and in some cases, the energy required for its active degradation. SAI is well-studied in cells. Short-lived transcription and signaling factors enable a rapid response to a changing environment, and turnover is critical for replacement of damaged macromolecules. The minimal gene set for a viable cell includes proteases and a nuclease, suggesting SAI is essential for life. SAI promotes genetic diversity in several ways. Toxin/antitoxin systems promote maintenance of genes, and SAI of mitochondria facilitates uniparental transmission. By creating two distinct states, subject to different selective pressures, SAI can maintain genetic diversity. SAI of components of synthetic replicators favors replicator cycling, promoting emergence of replicators with increased complexity. Both classical and recent computer modeling of replicators reveals SAI. SAI may be involved at additional levels of biological organization. In summary, SAI promotes replicator genetic diversity and reproductive fitness, and may promote aging through loss of resources and maintenance of deleterious alleles.

Artificial cell design: reconstructing biology for life science applications

Artificial cells are developed to redesign novel biological functions in a programmable and tunable manner. Although it aims to reconstitute living cell features and address 'origin of life' related questions, rapid development over the years has transformed artificial cells into an engineering tool with huge potential in applied biotechnology. Although the application of artificial cells was introduced decades ago as drug carriers, applications in other sectors are relatively new and could become possible with the technological advancement that can modulate its designing principles. Artificial cells are non-living system that includes no prerequisite designing modules for their formation and therefore allow freedom of assembling desired biological machinery within a physical boundary devoid of complex contemporary living-cell counterparts. As stimuli-responsive biomimetic tools, artificial cells are programmed to sense the surrounding, recognise their target, activate its function and perform the defined task. With the advantage of their customised design, artificial cells are being studied in biosensing, drug delivery, anti-cancer therapeutics or artificial photosynthesis type fields. This mini-review highlights those advanced fields where artificial cells with a minimalistic setup are developed as user-defined custom-made microreactors, targeting to reshape our future 'life'.

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life

The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values “supporting” a hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available). Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular “Methods” in a paper. Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters. With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful. In particular, based on this, it is expected that more near-reality (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field–hopefully leading to significant steps forward in respect to our understanding on the origin of life.

People have long been interested in the evolutionary processes through which life on our planet could have arisen from a non-life background. However, it seems that experimental studies in this field are proceeding slowly, perhaps owing to the complication of such processes. In the meantime, computer modeling has shown its potential to disclose the evolutionary mechanisms involved. Now a major difficulty of the computer modeling work is to justify the parameter-setting–on account of our limited knowledge on prebiotic chemistry and environments. Thus, people tend to explore the parameter space to seek parameter values in favor of the hypothetic scene and leave the parameter-justification a later job when sufficient knowledge is available. To date, the parameter-exploration is usually conducted manually (in many cases by trial and error), thus arduous and unpredictable. Inspired by the algorithm of machine-learning, we designed an automatic approach of parameter-exploration. The results showed that the approach is quite effective–that is, “good” parameter-sets in favor of hypothetic scenes in the origin of life can be found automatically. It is expected that such a machine-learning-like method would greatly enhance the efficiency of our evolutionary modeling studies on the origin of life in future.

Incorporating antagonistic pleiotropy into models for molecular replicators

In modern cells, chromosomal genes composed of DNA encode multi-subunit protein/RNA complexes that catalyze the replication of the chromosome and cell. One prevailing theory for the origin of life posits an early stage involving self-replicating macromolecules called replicators, which can be considered genes capable of self-replication. One prevailing theory for the genetics of aging in humans and other organisms is antagonistic pleiotropy, which posits that a gene can be beneficial in one context, and detrimental in another context. We previously reported that the conceptual simplicity of molecular replicators facilitates the generation of two simple models involving antagonistic pleiotropy. Here a third model is proposed, and each of the three models is presented with improved definition of the time variable. Computer simulations were used to calculate the proliferation of a hypothetical two-subunit replicator (AB), when one of the two subunits (B) exhibits antagonistic pleiotropy, leading to an advantage for B to be unstable. In model 1, instability of B yields free A subunits, which in turn stimulate the activity of other AB replicators. In model 2, B is lost and sometimes replaced by a more active mutant form, B'. In model 3, B becomes damaged and loses activity, and its instability allows it to be replaced by a new B. For each model, conditions were identified where instability of B was detrimental, and where instability of B was beneficial. The results are consistent with the hypothesis that antagonistic pleiotropy can promote molecular instability and system complexity, and provide further support for a model linking aging and evolution.

Thresholds in Origin of Life Scenarios

Thresholds are widespread in origin of life scenarios, from the emergence of chirality, to the appearance of vesicles, of autocatalysis, all the way up to Darwinian evolution. Here, we analyze the “error threshold,” which poses a condition for sustaining polymer replication, and generalize the threshold approach to other properties of prebiotic systems. Thresholds provide theoretical predictions, prescribe experimental tests, and integrate interdisciplinary knowledge. The coupling between systems and their environment determines how thresholds can be crossed, leading to different categories of prebiotic transitions. Articulating multiple thresholds reveals evolutionary properties in prebiotic scenarios. Overall, thresholds indicate how to assess, revise, and compare origin of life scenarios.

Applications of phage-derived RNA-based technologies in synthetic biology

As the most abundant biological entities with incredible diversity, bacteriophages (also known as phages) have been recognized as an important source of molecular machines for the development of genetic-engineering tools. At the same time, phages are crucial for establishing and improving basic theories of molecular biology. Studies on phages provide rich sources of essential elements for synthetic circuit design as well as powerful support for the improvement of directed evolution platforms. Therefore, phages play a vital role in the development of new technologies and central scientific concepts. After the RNA world hypothesis was proposed and developed, novel biological functions of RNA continue to be discovered. RNA and its related elements are widely used in many fields such as metabolic engineering and medical diagnosis, and their versatility led to a major role of RNA in synthetic biology. Further development of RNA-based technologies will advance synthetic biological tools as well as provide verification of the RNA world hypothesis. Most synthetic biology efforts are based on reconstructing existing biological systems, understanding fundamental biological processes, and developing new technologies. RNA-based technologies derived from phages will offer abundant sources for synthetic biological components. Moreover, phages as well as RNA have high impact on biological evolution, which is pivotal for understanding the origin of life, building artificial life-forms, and precisely reprogramming biological systems. This review discusses phage-derived RNA-based technologies terms of phage components, the phage lifecycle, and interactions between phages and bacteria. The significance of RNA-based technology derived from phages for synthetic biology and for understanding the earliest stages of biological evolution will be highlighted.

Roadmap to Building a Cell: An Evolutionary Approach

Laboratory synthesis of an elementary biological cell from isolated components may aid in understanding of the fundamental principles of life and will provide a platform for a range of bioengineering and medical applications. In essence, building a cell consists in the integration of cellular modules into system's level functionalities satisfying a definition of life. To achieve this goal, we propose in this perspective to undertake a semi-rational, system's level evolutionary approach. The strategy would require iterative cycles of genetic integration of functional modules, diversification of hereditary information, compartmentalized gene expression, selection/screening, and possibly, assistance from open-ended evolution. We explore the underlying challenges to each of these steps and discuss possible solutions toward the bottom-up construction of an artificial living cell.

Re-conceptualizing the origins of life

Over the last several hundred years of scientific progress, we have arrived at a deep understanding of the non-living world. We have not yet achieved an analogous, deep understanding of the living world. The origins of life is our best chance at discovering scientific laws governing life, because it marks the point of departure from the predictable physical and chemical world to the novel, history-dependent living world. This theme issue aims to explore ways to build a deeper understanding of the nature of biology, by modelling the origins of life on a sufficiently abstract level, starting from prebiotic conditions on Earth and possibly on other planets and bridging quantitative frameworks approaching universal aspects of life. The aim of the editors is to stimulate new directions for solving the origins of life. The present introduction represents the point of view of the editors on some of the most promising future directions.This article is part of the themed issue 'Reconceptualizing the origins of life'.