VIII. Earth Earns: An Open Participatory Earthropocene to Ecosmocene CoCreativity

2. Second Genesis: Emergent LifeKinder Proceeds to an Aware BioGenetic Phase

Whitesides, George. Bioinspiration: Something for Everyone. Interface Focus. 5/4, 2015. In an issue on Bioinspiration of New Technologies, the Harvard University polychemist leads with a copious, procreative survey. At the outset, the concept and approach of drawing upon guidance from the natural wisdom of living systems to intentionally carry forward for a better world is extolled. The paper goes on about soft matter, self-assembly, mesoscale structures, information and energy, reaction networks, covalent synthesis, and so on, much from his own laboratory

Bioinspiration — using phenomena in biology to stimulate research in non-biological science and technology—is a strategy that suggests new areas for research. Beyond its potential to nucleate new ideas, bioinspiration has two other interesting characteristics. It can suggest subjects in research that are relatively simple technically; it can also lead to areas in which results can lead to useful function more directly than some of the more familiar areas now fashionable in chemistry. Bioinspired research thus has the potential to be accessible to laboratories that have limited resources, to offer routes to new and useful function, and to bridge differences in technical and cultural interactions of different geographical regions. (Abstract)

Woolfson, Adrian. Synthetic Life. Daedalus. Winter, 2008. The University of Cambridge physician and CEO of ProteinLogic explores, some eight years of genome sequencing technology after his book Life Without Genes, what august potentials are now within imagination as human ingenuity may take over metazoan creation.

With the basic universal algorithmic machine and synthetic tool in place, humanity will at that point enter a new age of mathematical cartography: the constructional, and principally computational, science of synthetic life will enable the delineation of qualitatively different types of maps than those created by conventional cartographers. These new virtual maps will allow us to catalog the creatures that, like Ebenezer Scrooge’s Christmas ghosts, inhabit both the past, present, and future, and which populate the knotted and twisted mathematical landscapes of the ‘library of all possible creatures’ – a single definitive and exhaustive inventory of all living possibility. (82)

Yang, Kevin, et al. Machine Learning in Protein Enginering. arXiv:1811.10775. Caltech biochemists including Frances Arnold, who co-received the 2018 Nobel Prize in Chemistry for this breakthrough work, explain in tutorial fashion the agile utility and procreative promise of this novel computational method.

Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved. In this review, we introduce the steps required to collect protein data, train machine-learning models, and use trained models to guide engineering. (Abstract)

Protein engineering seeks to design or discover proteins whose properties, useful for technological, scientific, or medical applications, have not been needed or optimized in nature. We can envision the mapping of protein sequence to a desired function or functions as a “fitness landscape” over the high-dimensional space of possible protein sequences. The fitness represents a protein’s performance: expression level, catalytic activity, or other properties of interest to the protein engineer. The landscape determines the range of properties available to different sequences as well as the ease with which they can be optimized. Protein engineering seeks to identify sequences corresponding to high fitnesses on these landscapes. (1)

Inspired by natural evolution, directed evolution climbs a fitness landscape by accumulating beneficial mutations in an iterative protocol of mutation and selection, as illustrated in Figure 1a. The first step is sequence diversification using techniques such as random mutagenesis, site-saturation mutagenesis, or recombination to generate a library of modified sequences starting from the parent sequence(s). The second step is screening or selection to identify variants with improved properties for the next round of diversification. The steps are repeated until fitness goals are achieved. (2)

Yewdall, Amy, et al. The Hallmarks of Living Systems: Towards Creating Artificial Cells. Interface Focus. 8/5, 2018. In a lead paper for this special issue, Eindhoven University of Technology and Radboud University biochemists including Jan van Hest consider an open frontier of life’s intentional human (re)creation and advance. Five phases are identified: energy transduction, information processing, growth and division, adaptability, and compartmentalization. Along with the first four, the last is seen as most important with regard to membrane complexity, shape, activity, mobility with biomimetic guidance from dimers, cytosols, and other protein and cellular assemblies. As the Abstract notes, this initiative going forward can be seen as a respectful passage to our intended evolitionary continuance. See also an issue introduction The Artificial Cell by its editors Paul Beales, Barbara Ciani and Stephen Mann.

Despite the astonishing diversity and complexity of living systems, they all share five common hallmarks: compartmentalization, growth and division, information processing, energy transduction and adaptability. In this review, we give not only examples of how cells satisfy these requirements for life and the ways in which it is possible to emulate these characteristics in engineered platforms, but also the gaps that remain to be bridged. The bottom-up synthesis of life-like systems continues to be driven forward by the advent of new technologies, by the discovery of biological phenomena through their transplantation to experimentally simpler constructs and by providing insights into one of the oldest questions posed by mankind, the origin of life on Earth. (Abstract)

Yilmaz, Suzan, et al. Towards Next-Generation Cell Factories by Rational Geome-Scale Engineering. Nature Catalysis. 5/9, 2022. Wageningen University, the Netherlands, Harvard Medical School (George Church) and MIT innovators look toward novel abilities and advances as this epic intentional phase begins life’s new cocreativity.

Metabolic engineering holds the promise to transform the chemical industry and transition into a circular bioeconomy by way of cellular biocatalysts. But to realize its potential, optimum synthetic networks, aka cell factories, need be made at system and genome-wide levels. Recent advances in genome-editing methods enable directed engineering for many relevant microorganisms and can benefit from machine learning. These approaches can achieve next-generation cell factories for efficient, sustainable production of a wide range of products. (Excerpt)

Zeymer, Cathleen and Donald Hilvert. Directed Evolution of Protein Catalysts. Annual Review of Biochemistry. Vol. 87, 2018. As the Abstract broaches, ETH Zurich biochemists scope out a novel beneficial genesis procreation by way of our globally intentional, and respectful human acumen.

Directed evolution is a powerful technique for generating tailor-made enzymes for a wide range of biocatalytic applications. Following the principles of natural evolution, iterative cycles of mutagenesis and screening or selection are applied to modify protein properties, enhance catalytic activities, or develop completely new protein catalysts for non-natural chemical transformations. This review briefly surveys the experimental methods used to generate genetic diversity and screen or select for improved enzyme variants. Emphasis is placed how to generate novel catalytic activities that expand the scope of natural reactions. (Abstract)

Zhang, Fei and Hao Yan. DNA Self-Assembly Scaled Up. Nature. 552/34, 2017. An introduction to several papers about the many structural and dynamic ways beyond genetics that nature’s nucleotides seem capable of. For a sample, Fractal Assembly of Micrometre-Scale DNA Origami Arrays, Programmable Self-Assembly of Three-Dimensional nanostructures, and Gigadalton-Scale Shape-Programmable DNA Assemblies, can convey the seemingly unlimited potentials that an animate material cosmos has gifted us with.

The biopolymers DNA, RNA and proteins have all been used as building blocks for the assembly of designer nanoscale architectures, to engineer bioinspired or biomimetic systems that can communicate with each other6 and to regulate the functions of living organisms7. DNA is the most useful nanoscale building block because it has several advantages — especially its programmability, which derives from the predictable and stable pairs that form between bases on complementary DNA strands. Moreover, DNA is structurally stable, the geometrical features of its double helix have been well studied, and it is compatible with other biological molecules, which should allow the construction of ‘hetero-biomaterials’ that have complex functions. Various DNA self-assembly methods (see ref. 8, for example) have been developed for constructing synthetic architectures that exhibit great geometrical complexity and nanoscale accuracy. (Extract)

Zimmer, Carl. Scientists are Designing Artisanal Proteins for Your Body. New York Times. December 26, 2017. The forefront achievements of the University of Washington biochemist David Baker and his laboratory to create custom, beneficial protein shapes and sizes is conveyed in this news report. With reference to 2017 Nature papers Evolution of a Designed Protein Assembly Encapsulating its Own RNA Genome (552/415) and Massively Parallel de novo Protein Design for Targeted Therapeutics (550/74) and more, by way of Rosetta Commons (Google) software, their work implies how amenable life’s biomolecules seem to be for tailored, beneficial modifications.

The human body makes tens of thousands of cellular proteins, each for a particular task. Now researchers have learned to create custom versions not found in nature. Scientists have studied proteins for nearly two centuries, and over that time they’ve worked out how cells create them from simple building blocks. They have long dreamed of assembling those elements into new proteins not found in nature. David Baker, 55, the director of the Institute for Protein Design at the University of Washington, has been investigating that enigma for a quarter-century. Now, it looks as if he and his colleagues have cracked it. Thanks in part to crowdsourced computers and smartphones belonging to over a million volunteers, the scientists have figured out how to choose the building blocks required to create a protein that will take on the shape they want. They have produced thousands of different kinds of proteins, which assume the shape the scientists had predicted. Often those proteins are profoundly different from any found in nature. “We can now build proteins from scratch from first principles to do what we want,” said Dr. Baker. (Excerpt)

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