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.

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.

BUILDING BLOCKS OF LIFE IN METEORITES MAY LEAD TO PANSPERMIA ORIGIN OF LIFE

The current hypothesis leads to the panspermia origin of life, which is based on the scientific principle of electromagnetic force interaction with matter. Electromagnetic force (Sunlight) interacts with inorganic chemistry available to us given out by the stars in the universe plausibly triggers the formation of extra-terrestrial biological molecules of proto cells under abiotic conditions, as evidenced by their presence in meteorites.' Proto cells’ might theoretically give rise to living organisms with a manifested soul, allowing 'Ribose' to be formed from ice grains hit by sunlight for RNA and DNA at the same time. The presence of life's building blocks and other important organic chemicals like ribose in meteorites, including some microscopic life forms that aren't native to Earth, may have led to the 'Panspermia Origin of Life' and the 'Evolution of Life on Earth' which is evidenced by the definition of 'Meteorites' in Vedic Scriptures, such as the "Bhagavad Gita" (3000 BC) and "Brihat Samhita" (520 AD) that they are the souls of righteous people who have returned to earth to be reborn.