Unstandard Standardization: The Case of Biology

How applicable are the approaches adopted by information and communication technology standards-setting organizations to biological standards? Most engineering-based industries construct products from standard, well understood components. By contrast, despite the early attachment of the moniker “genetic engineering” to biotechnology, standardization in the biological sciences has been relatively rare

Engineering of Soil Biological Quality From Nickel Mining Stockpile Using Two Earthworm Ecological Groups

Earthworms have the ability in modifying soil biological quality for plant growth. Their ability is mostly depending on its ecological groups. The objectives of the research were to study the influence of two ecological groups of earthworms on soil microbial activity and soil micro-fauna abundance, and to know the potential of soil modified by earthworms as plant growth medium. Eight combination of individual earthworm from epigeic and endogeic groups was applied into pot that was filled by soil from two years of nickel stockpile and each treatment was repeated by five times. The experiment was following complete randomize design procedure. After sixteen days of research, the soil sample from each pot was analyzed for soil FDA activity, number of flagellate and nematodes. Furthermore, one kg of the soil from each pot was taken and every pot was grown by Paraserianthes falcataria seedling with the age of five days and continued its growth for two months. The results indicated that the soil FDA activity, number of flagellate and nematodes among treatments were significantly differences. In addition, it indicated the significant differences in dry weight of shoot, root, total plant, and root to shoot ratio of P. falcataria seedlings. It concluded that the combination of an individual number of epigeic and endogeic earthworms improved soil biological quality of stock pile, amd most suitable for seedlings growth in nickel mining area

Developments in the tools and methodologies of synthetic biology.

Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a body of knowledge from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community

On engineering reliability concepts and biological aging

Some stochastic approaches to biological aging modeling are studied. We assume that an organism acquires a random resource at birth. Death occurs when the accumulated dam-age (wear) exceeds this initial value, modeled by the discrete or continuous random vari-ables. Another source of death of an organism is also taken into account, when it occurs as a consequence of a shock or of a demand for energy, which is a generalization of the Strehler-Mildwan’s model (1960). Biological age based on the observed degradation is also defined. Finally, aging properties of repairable systems are discussed. We show that even in the case of imperfect repair, which is certainly the case for organisms, aging slows down with age and eventually can even fade out. This presents another possible explanation for the human mortality rate plateaus.mortality

Synthetic biology—putting engineering into biology

Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis—synthetic biology’s system fabrication process—supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.

Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio