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Bridging the Gap Between Science and Design: Biologically Inspired Design

Bridging the Gap Between Science and Design: Biologically Inspired Design | SynBioFromLeukipposInstitute | Scoop.it
Gerd Moe-Behrens's insight:

by
Tanaya Joshi

"Being from a very different background than my fellow bloggers, it can be a challenge to find a topic to write about. I mean, I’m an Industrial Design major and that’s pretty far from science and labs and stuff, right? WRONG!

 A couple weeks ago, my studio mates and I were assigned a new project: To make a biologically inspired lamp with an alternate power source. Before I tell you more, let me distinguish between biologically inspired design and design based on biomimicry. The latter is a more straightforward approach. For example, I like the shape of a honeycomb and would like to make a light that mimics it. Biologically inspired design, on the other hand, digs deeper and looks to solve a problem. For example, a light that draws biological inspiration from honeycomb might use the honeycomb as a charging station for lights for, say, students studying in the library. They would come get a task light for their desk from the honeycomb and return it to recharge when done. The research for our project began with help of an expert from the Center for Biologically Inspired Design. He discussed numerous types of inspiration that exist in nature and it wasn’t until then that I realized how much design does and can borrow from science. For example, an Aerospace Engineer would never think to put ridges on a windmill blade, but after assessing the fins of whales, designers, scientists and engineers found that a blade with ridges would produce more lift and work better at steeper angles without stalling. With biologically inspired design, the use of science is being explored in numerous capacities in the field of Industrial Design. This interdisciplinary study is an especially successful approach because it has an advantage of being based on an existing solution. Furthermore, the fact that it bridges disciplines gives not only a larger database of information to borrow from, but also the flexibility to gain the best of both worlds. Oftentimes, our areas of study can get cornered off, but by bridging two disciplines like science and design, a world of possibilities is open. As we progress in the areas of engineering, science, and design, we are getting more and more interdisciplinary. For example, engineers, designers, and scientists merged to better understand flight of the herring gull. The result was Smart Bird, which “can start, fly and land autonomously – with no additional drive mechanism.” This technology fuels ideas to enhance hybrid drive technology while optimizing energy consumption. Future generations, especially, will be faced with the challenge of having to borrow heavily from natural resources. It is in our benefit, then, that we explore fields outside our area of expertise and integrate them with ours to solve these complex problems that do and will arise. After all, it is only for so long that we can rely on the techniques of traditional disciplines to solve current issues."


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Scientists ‘program’ living bacteria to store data

Scientists ‘program’ living bacteria to store data | SynBioFromLeukipposInstitute | Scoop.it
New method enables electronic conversion of data into DNA
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Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification - ScienceDirect

Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification - ScienceDirect | SynBioFromLeukipposInstitute | Scoop.it
Bacteria initiate complicated signaling cascades from the detection of intracellular metabolites or exogenous substances by hundreds of transcription …
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A deep learning approach to programmable RNA switches

A deep learning approach to programmable RNA switches | SynBioFromLeukipposInstitute | Scoop.it
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology. RNA can be used as a programmable tool for detection of biological analytes. Here the authors use deep neural networks to predict toehold switch functionality in synthetic biology applications.
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A machine learning Automated Recommendation Tool for synthetic biology

A machine learning Automated Recommendation Tool for synthetic biology | SynBioFromLeukipposInstitute | Scoop.it
Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing. Synthetic Biology often lacks the predictive power needed for efficient bioengineering. Here the authors present ART, a machine learning and probabilistic predictive tool to guide synthetic biology design in a systematic fashion.
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DNA-directed nanofabrication of high-performance carbon nanotube field-effect transistors | Science

DNA-directed nanofabrication of high-performance carbon nanotube field-effect transistors | Science | SynBioFromLeukipposInstitute | Scoop.it
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RNA nanotechnology in synthetic biology - ScienceDirect

RNA nanotechnology in synthetic biology - ScienceDirect | SynBioFromLeukipposInstitute | Scoop.it
We review recent advances in the design and expression of synthetic RNA sequences inside cells, to regulate gene expression and to achieve spatial loc…
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A scalable pipeline for designing reconfigurable organisms | PNAS

A scalable pipeline for designing reconfigurable organisms | PNAS | SynBioFromLeukipposInstitute | Scoop.it
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Quest to use CRISPR against disease gains ground

Quest to use CRISPR against disease gains ground | SynBioFromLeukipposInstitute | Scoop.it
As the first clinical-trial results trickle in, researchers look ahead to more sophisticated medical applications for genome editing.
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A tunable orthogonal coiled-coil interaction toolbox for engineering mammalian cells

A tunable orthogonal coiled-coil interaction toolbox for engineering mammalian cells | SynBioFromLeukipposInstitute | Scoop.it
Protein interactions guide most cellular processes. Orthogonal hetero-specific protein–protein interaction domains may facilitate better control of engineered biological systems. Here, we report a tunable de novo designed set of orthogonal coiled-coil (CC) peptide heterodimers (called the NICP set) and its application for the regulation of diverse cellular processes, from cellular localization to transcriptional regulation. We demonstrate the application of CC pairs for multiplex localization in single cells and exploit the interaction strength and variable stoichiometry of CC peptides for tuning of gene transcription strength. A concatenated CC peptide tag (CCC-tag) was used to construct highly potent CRISPR–dCas9-based transcriptional activators and to amplify the response of light and small molecule-inducible transcription in cell culture as well as in vivo. The NICP set and its implementations represent a valuable toolbox of minimally disruptive modules for the recruitment of versatile functional domains and regulation of cellular processes for synthetic biology. A set of orthogonal coiled-coil peptide heterodimers were developed to enable control of protein localization as well as transcriptional regulation by enhancing effector recruitment to TALE and CRISPR–dCas9 systems in mammalian cells and in vivo.
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Modular Analysis and Design of Biological Circuits - ScienceDirect

Modular Analysis and Design of Biological Circuits - ScienceDirect | SynBioFromLeukipposInstitute | Scoop.it
Modularity has been the subject of intense investigation in systems biology for more than two decades. Whether modularity holds in biological networks…
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Cell‐free protein synthesis: the transition from batch reactions to minimal cells and microfluidic devices - Hosein Ayoubi‐Joshaghani - - Biotechnology and Bioengineering

Cell‐free protein synthesis: the transition from batch reactions to minimal cells and microfluidic devices - Hosein Ayoubi‐Joshaghani - - Biotechnology and Bioengineering | SynBioFromLeukipposInstitute | Scoop.it
Thanks to the synthetic biology, the laborious and restrictive procedure for producing a target protein in living microorganisms by biotechnological approaches can now experience a robust, plian
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Modeling somatic computation with non-neural bioelectric networks

Modeling somatic computation with non-neural bioelectric networks | SynBioFromLeukipposInstitute | Scoop.it
The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.
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Reprogramming biological peptides to combat infectious diseases - Chemical Communications (RSC Publishing)

Reprogramming biological peptides to combat infectious diseases - Chemical Communications (RSC Publishing) | SynBioFromLeukipposInstitute | Scoop.it
With the rapid spread of resistance among parasites and bacterial pathogens, antibiotic-resistant infections have drawn much attention worldwide. Consequently, there is an urgent need to develop new strategies to treat neglected diseases and drug-resistant infections. Here, we outline several new strategies that ha
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Exploring the synthetic biology potential of bacteriophages for engineering non-model bacteria | Nature Communications

Non-model bacteria like Pseudomonas putida, Lactococcus lactis and other species have unique and versatile metabolisms, offering unique opportunities for Synthetic Biology (SynBio). However, key genome editing and recombineering tools require optimization and large-scale multiplexing to unlock the full SynBio potential of these bacteria. In addition, the limited availability of a set of characterized, species-specific biological parts hampers the construction of reliable genetic circuitry. Mining of currently available, diverse bacteriophages could complete the SynBio toolbox, as they constitute an unexplored treasure trove for fully adapted metabolic modulators and orthogonally-functioning parts, driven by the longstanding co-evolution between phage and host. Non-model bacteria offer unique and versatile metabolisms for synthetic biology. In this Perspective, the authors explore the limited availability of well-characterised biological parts in these species and argue that bacteriophages represent a diverse trove of orthogonal parts.
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Deep learning enables identification and optimization of RNA-based tools for myriad applications

Deep learning enables identification and optimization of RNA-based tools for myriad applications | SynBioFromLeukipposInstitute | Scoop.it
DNA and RNA have been compared to "instruction manuals" containing the information needed for living "machines" to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables—a task that is daunting to even the most intrepid human brains.
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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism | SynBioFromLeukipposInstitute | Scoop.it
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts. In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve prediction performance of tryptophan metabolism in baker’s yeast.
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Machine learning takes on synthetic biology: algorithms can bioengineer cells for you | EurekAlert! Science News

Machine learning takes on synthetic biology: algorithms can bioengineer cells for you | EurekAlert! Science News | SynBioFromLeukipposInstitute | Scoop.it
Scientists at Lawrence Berkeley National Laboratory have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it.
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The kill-switch for CRISPR that could make gene-editing safer

The kill-switch for CRISPR that could make gene-editing safer | SynBioFromLeukipposInstitute | Scoop.it
How anti-CRISPR proteins and other molecules could bolster biosecurity and improve medical treatments.
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Team Builds the First Living Robots | UVM Today | The University of Vermont

Team Builds the First Living Robots | UVM Today | The University of Vermont | SynBioFromLeukipposInstitute | Scoop.it
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A bacteriophage nucleus-like compartment shields DNA from CRISPR nucleases

A bacteriophage nucleus-like compartment shields DNA from CRISPR nucleases | SynBioFromLeukipposInstitute | Scoop.it
All viruses require strategies to inhibit or evade the immune pathways of cells that they infect. The viruses that infect bacteria, bacteriophages (phages), must avoid immune pathways that target nucleic acids, such as CRISPR–Cas and restriction-modification systems, to replicate efficiently1. Here we show that jumbo phage ΦKZ segregates its DNA from immunity nucleases of its host, Pseudomonas aeruginosa, by constructing a proteinaceous nucleus-like compartment. ΦKZ is resistant to many immunity mechanisms that target DNA in vivo, including two subtypes of CRISPR–Cas3, Cas9, Cas12a and the restriction enzymes HsdRMS and EcoRI. Cas proteins and restriction enzymes are unable to access the phage DNA throughout the infection, but engineering the relocalization of EcoRI inside the compartment enables targeting of the phage and protection of host cells. Moreover, ΦKZ is sensitive to Cas13a—a CRISPR–Cas enzyme that targets RNA—probably owing to phage mRNA localizing to the cytoplasm. Collectively, we propose that Pseudomonas jumbo phages evade a broad spectrum of DNA-targeting nucleases through the assembly of a protein barrier around their genome. The jumbo phage ΦKZ constructs a proteinaceous nucleus-like compartment around its genome that protects phage DNA from degradation by DNA-targeting immune effectors of the host, including CRISPR–Cas and restriction enzymes.
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Cell-free is growing up

Cell-free is growing up | SynBioFromLeukipposInstitute | Scoop.it
Cell-free technology can increase the ease and speed of discovery and better prepare the next generation of scientists for the challenges ahead.
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Multi-functional genome-wide CRISPR system for high throughput genotype–phenotype mapping

Multi-functional genome-wide CRISPR system for high throughput genotype–phenotype mapping | SynBioFromLeukipposInstitute | Scoop.it
Genome-scale engineering is an indispensable tool to understand genome functions due to our limited knowledge of cellular networks. Unfortunately, most existing methods for genome-wide genotype–phenotype mapping are limited to a single mode of genomic alteration, i.e. overexpression, repression, or deletion. Here we report a multi-functional genome-wide CRISPR (MAGIC) system to precisely control the expression level of defined genes to desired levels throughout the whole genome. By combining the tri-functional CRISPR system and array-synthesized oligo pools, MAGIC is used to create, to the best of our knowledge, one of the most comprehensive and diversified genomic libraries in yeast ever reported. The power of MAGIC is demonstrated by the identification of previously uncharacterized genetic determinants of complex phenotypes, particularly those having synergistic interactions when perturbed to different expression levels. MAGIC represents a powerful synthetic biology tool to investigate fundamental biological questions as well as engineer complex phenotypes for biotechnological applications. Genome-scale engineering is generally limited to single methods of alteration such as overexpression, repression or deletion. Here the authors present a tri-functional CRISPR system that can engineer complex synergistic interactions in a genome-wide manner.
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Artificial signaling in mammalian cells enabled by prokaryotic two-component system

Artificial signaling in mammalian cells enabled by prokaryotic two-component system | SynBioFromLeukipposInstitute | Scoop.it
Augmenting live cells with new signal transduction capabilities is a key objective in genetic engineering and synthetic biology. We showed earlier that two-component signaling pathways could function in mammalian cells, albeit while losing their ligand sensitivity. Here, we show how to transduce small-molecule ligands in a dose-dependent fashion into gene expression in mammalian cells using two-component signaling machinery. First, we engineer mutually complementing truncated mutants of a histidine kinase unable to dimerize and phosphorylate the response regulator. Next, we fuse these mutants to protein domains capable of ligand-induced dimerization, which restores the phosphoryl transfer in a ligand-dependent manner. Cytoplasmic ligands are transduced by facilitating mutant dimerization in the cytoplasm, while extracellular ligands trigger dimerization at the inner side of a plasma membrane. These findings point to the potential of two-component regulatory systems as enabling tools for orthogonal signaling pathways in mammalian cells. Bacterial two-component signaling machinery has been reprogrammed for orthogonal signaling in mammalian cells that is triggered by small-molecule-mediated dimerization or ligand-induced GPCR/β-arrestin signaling.
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CRISPR-Cas3 induces broad and unidirectional genome editing in human cells

https://www.nature.com/articles/s41467-019-13226-x.pdf

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