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.

The Perennial Riddle of Life’s Origin

The origin of life is the most consequential problem in biology, possibly in all of science, and it remains unsolved. This chapter summarizes what has been learned and highlights questions that remain open, including How, Where, When, and especially Why. LUCA, some four billion years ago, already featured the basic capacities of contemporary cells. These must have evolved still earlier, at a nebulous proto-cellular stage. There is good reason to believe that enzymes, DNA, ribosomes, electron-transport chains, and the rotary ATP synthase all predate LUCA and were shaped by the standard process of variation and natural selection, but we know next to nothing about how cells ever got started. I favor the proposal that it began with a purely chemical dynamic network capable of reproducing itself, that may have originated by chance. Natural selection would have favored the incorporation of any ancillary factors that promoted its kinetic stability, especially ones that improved reproduction or gave access to energy. All the specifics are in dispute, including the role of a prebiotic broth of organic chemicals, the nature and origin of enclosure, the RNA world, and a venue in submarine hydrothermal vents. My sense is that critical pieces of the puzzle remain to be discovered.

On Life

Living things are truly strange objects. They stand squarely within the material world, but at the same time flaunt capacities that far exceed those of inanimate matter. Life is in some sense a singular phenomenon: astonishingly, all creatures from bacteria to elephants, redwoods and humans belong to a single enormous family. What life is, how living things work, how they mesh with the realm of physics and chemistry, and how they came to be as we find them—these are the questions that define the science of biology. A rational sense of the world requires finding in it a place for life. Many of the answers are known, but as knowledge expands relentlessly it becomes ever harder to grasp the phenomenon of life whole. This book aims to make the phenomenon of life intelligible to serious readers who are not professional biologists by giving them a sense of the biological landscape: presenting the principles as currently understood and the major issues that remain unresolved, as simply and concisely as may be. What emerges is a biology that is internally consistent and buttressed by a wealth of factual knowledge, but also inescapably historical and complex. The hallmark of life is organization, order that has purpose; and that sets biology apart from the physical sciences. Despite a century of spectacular progress the phenomenon of life remains tantalizingly beyond our grasp, bracketed by two stubborn mysteries: the origin of life at one end, the nature of mind at the other.

A mechanism of abiogenesis based on complex reaction networks organized by seed-dependent autocatalytic systems

The core of the origin-of-life problem is to explain how a complex dissipative system could emerge spontaneously from a simple environment, perpetuate itself, and complexify over time. This would only be possible, we argue, if prebiotic chemical reaction networks had autocatalytic features organized in a way that permitted the accretion of complexity even in the absence of genetic control. To evaluate this claim, we developed tools to analyze the autocatalytic organization of food-driven reaction networks and applied these tools to both abiotic and biotic networks. Both networks contained seed-dependent autocatalytic systems (SDASs), which are subnetworks that can use a flux of food chemicals to self-propagate if, and only if, they are first seeded by some non-food chemicals. Moreover, SDASs were organized such that the activation of a lower-tier SDAS could render new higher-tier SDASs accessible. The organization of SDASs is, thus, similar to trophic levels (producer, primary consumer, etc.) in a biological ecosystem. Furthermore, similar to ecological succession, we found that higher-tier SDASs may produce chemicals that enhance the ability of the entire chemical ecosystem to utilize food more efficiently. The SDAS concept explains how driven abiotic environments, namely ones receiving an ongoing flux of food chemicals, can incrementally complexify even without genetic polymers. This framework predicts that it ought to be possible to detect the spontaneous emergence of life-like features, such as self-propagation and adaptability, in driven chemical systems in the laboratory. Additionally, SDAS theory may be useful for exploring general properties of other complex systems.