A Synthetic Microbial Operational Amplifier

Mapping Intimacies ◽

10.1101/161828 ◽

2017 ◽

Author(s):

Ji Zeng ◽

Jaewook Kim ◽

Areen Banerjee ◽

Rahul Sarpeshkar

Keyword(s):

Transcription Factor ◽

Synthetic Biology ◽

Operational Amplifier ◽

Biological Systems ◽

Logic Gates ◽

Living Cells ◽

Output Stage ◽

Cellular Circuits ◽

Differential Transcription ◽

Insight Into

AbstractSynthetic biology has created oscillators, latches, logic gates, logarithmically linear circuits, and load drivers that have electronic analogs in living cells. The ubiquitous operational amplifier, which allows circuits to operate robustly and precisely has not been built with bio-molecular parts. As in electronics, a biological operational-amplifier could greatly improve the predictability of circuits despite noise and variability, a problem that all cellular circuits face. Here, we show how to create a synthetic 3-stage inducer-input operational amplifier with a differential transcription-factor stage, a CRISPR-based push-pull stage, and an enzymatic output stage with just 5 proteins including dCas9. Our ‘Bio-OpAmp’ expands the toolkit of fundamental circuits available to bioengineers or biologists, and may shed insight into biological systems that require robust and precise molecular homeostasis and regulation.One Sentence SummaryA synthetic bio-molecular operational amplifier that can enable robust, precise, and programmable homeostasis and regulation in living cells with just 5 protein parts is described.

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Self-adaptive Biosystems Through Tunable Genetic Parts and Circuits

10.20944/preprints202007.0751.v1 ◽

2020 ◽

Author(s):

Vittorio Bartoli ◽

Mario di Bernardo ◽

Thomas E. Gorochowski

Keyword(s):

Synthetic Biology ◽

Biological Systems ◽

Living Cells ◽

Complex Environments ◽

Control Engineering ◽

Physiological Conditions ◽

Recent Advances ◽

The Face ◽

The Creation ◽

Self Adaptive

Biological systems often need to operate in complex environments where conditions can rapidly change. This is possible due to their inherent ability to sense changes and adapt by adjusting their behavior in response. Here, we detail recent advances in the creation of synthetic genetic parts and circuits whose behaviors can be dynamically tuned through a variety of intra- and extra-cellular signals. We show how this capability lays the foundation for implementing control engineering schemes in living cells and allows for the creation of biological systems that are able to self-adapt, ensuring their functionality is maintained in the face of varying environmental and physiological conditions. We end by discussing some of the broader implications of this technology for the safe deployment of synthetic biology.

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Self-adaptive Biosystems Through Tunable Genetic Parts and Circuits

10.20944/preprints202007.0751.v2 ◽

2020 ◽

Author(s):

Vittorio Bartoli ◽

Mario di Bernardo ◽

Thomas E. Gorochowski

Keyword(s):

Synthetic Biology ◽

Biological Systems ◽

Living Cells ◽

Complex Environments ◽

Control Engineering ◽

Physiological Conditions ◽

Recent Advances ◽

The Face ◽

The Creation ◽

Self Adaptive

Biological systems often need to operate in complex environments where conditions can rapidly change. This is possible due to their inherent ability to sense changes and adapt their behavior in response. Here, we detail recent advances in the creation of synthetic genetic parts and circuits whose behaviors can be dynamically tuned through a variety of intra- and extra-cellular signals. We show how this capability lays the foundation for implementing control engineering schemes in living cells and allows for the creation of biological systems that are able to self-adapt, ensuring their functionality is maintained in the face of varying environmental and physiological conditions. We end by discussing some of the broader implications of this technology for the safe deployment of synthetic biology.

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Biology Meets Computation

The Digital Mind ◽

10.7551/mitpress/9780262036030.003.0007 ◽

2017 ◽

Author(s):

Arlindo Oliveira

Keyword(s):

Synthetic Biology ◽

Computational Biology ◽

Systematic Approach ◽

Biological Systems ◽

Deep Understanding ◽

Decisive Role ◽

Computer Models ◽

Living Cells ◽

Open The Doors ◽

The Way

This chapter describes how computers played a decisive role in the sequencing and understanding of the genomes of humans and other organisms. The discovery of the structure of DNA, by Watson and Crick, opened the way to a systematic approach to biology, based on the study of the way DNA encodes the proteins in all living cells. The area that became known as computational biology, or bioinformatics, aims at modelling, simulating, and understanding the behaviour of biological systems, using computer models and algorithms. Such a deep understanding will enable us to create computer models of cells and of entire organisms and even, in the future, open the doors to the creation of entirely new lifeforms, a field known as synthetic biology.

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Engineering the Interface between Inorganic Nanoparticles and Biological Systems through Ligand Design

Nanomaterials ◽

10.3390/nano11041001 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1001

Author(s):

Rui Huang ◽

David C. Luther ◽

Xianzhi Zhang ◽

Aarohi Gupta ◽

Samantha A. Tufts ◽

...

Keyword(s):

Organic Synthesis ◽

Molecular Design ◽

Biological Systems ◽

Ligand Design ◽

Inorganic Nanoparticles ◽

Organic Ligands ◽

Biological Applications ◽

Wide Range ◽

Insight Into

Nanoparticles (NPs) provide multipurpose platforms for a wide range of biological applications. These applications are enabled through molecular design of surface coverages, modulating NP interactions with biosystems. In this review, we highlight approaches to functionalize nanoparticles with ”small” organic ligands (Mw < 1000), providing insight into how organic synthesis can be used to engineer NPs for nanobiology and nanomedicine.

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Synthetic neural-like computing in microbial consortia for pattern recognition

Nature Communications ◽

10.1038/s41467-021-23336-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ximing Li ◽

Luna Rizik ◽

Valeriia Kravchik ◽

Maria Khoury ◽

Netanel Korin ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Gradient Descent ◽

Biological Systems ◽

Logic Gates ◽

Structural Similarity ◽

Microbial Consortia ◽

Promoter Strength ◽

Chemical Inducers ◽

Artificial Neural

AbstractComplex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

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Quantitative linear dichroism imaging of molecular processes in living cells made simple by open software tools

Communications Biology ◽

10.1038/s42003-021-01694-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Alexey Bondar ◽

Olga Rybakova ◽

Josef Melcr ◽

Jan Dohnálek ◽

Petro Khoroshyy ◽

...

Keyword(s):

Biological Systems ◽

Linear Dichroism ◽

Quantitative Information ◽

Living Cells ◽

Software Tools ◽

Model Systems ◽

Membrane Properties ◽

Polarization Microscopy ◽

Molecular Processes ◽

Wide Range

AbstractFluorescence-detected linear dichroism microscopy allows observing various molecular processes in living cells, as well as obtaining quantitative information on orientation of fluorescent molecules associated with cellular features. Such information can provide insights into protein structure, aid in development of genetically encoded probes, and allow determinations of lipid membrane properties. However, quantitating and interpreting linear dichroism in biological systems has been laborious and unreliable. Here we present a set of open source ImageJ-based software tools that allow fast and easy linear dichroism visualization and quantitation, as well as extraction of quantitative information on molecular orientations, even in living systems. The tools were tested on model synthetic lipid vesicles and applied to a variety of biological systems, including observations of conformational changes during G-protein signaling in living cells, using fluorescent proteins. Our results show that our tools and model systems are applicable to a wide range of molecules and polarization-resolved microscopy techniques, and represent a significant step towards making polarization microscopy a mainstream tool of biological imaging.

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The apple C2H2-type zinc finger transcription factor MdZAT10 positively regulates JA-induced leaf senescence by interacting with MdBT2

Horticulture Research ◽

10.1038/s41438-021-00593-0 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Kuo Yang ◽

Jian-Ping An ◽

Chong-Yang Li ◽

Xue-Na Shen ◽

Ya-Jing Liu ◽

...

Keyword(s):

Mass Spectrometry ◽

Transcription Factor ◽

Liquid Chromatography ◽

Zinc Finger ◽

Leaf Senescence ◽

Transcriptional Activity ◽

Molecular Mechanisms ◽

Liquid Chromatography Mass Spectrometry ◽

Zinc Finger Transcription Factor ◽

Insight Into

AbstractJasmonic acid (JA) plays an important role in regulating leaf senescence. However, the molecular mechanisms of leaf senescence in apple (Malus domestica) remain elusive. In this study, we found that MdZAT10, a C2H2-type zinc finger transcription factor (TF) in apple, markedly accelerates leaf senescence and increases the expression of senescence-related genes. To explore how MdZAT10 promotes leaf senescence, we carried out liquid chromatography/mass spectrometry screening. We found that MdABI5 physically interacts with MdZAT10. MdABI5, an important positive regulator of leaf senescence, significantly accelerated leaf senescence in apple. MdZAT10 was found to enhance the transcriptional activity of MdABI5 for MdNYC1 and MdNYE1, thus accelerating leaf senescence. In addition, we found that MdZAT10 expression was induced by methyl jasmonate (MeJA), which accelerated JA-induced leaf senescence. We also found that the JA-responsive protein MdBT2 directly interacts with MdZAT10 and reduces its protein stability through ubiquitination and degradation, thereby delaying MdZAT10-mediated leaf senescence. Taken together, our results provide new insight into the mechanisms by which MdZAT10 positively regulates JA-induced leaf senescence in apple.

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Detection of Anomalous Diffusion with Deep Residual Networks

Entropy ◽

10.3390/e23060649 ◽

2021 ◽

Vol 23 (6) ◽

pp. 649

Author(s):

Miłosz Gajowczyk ◽

Janusz Szwabiński

Keyword(s):

Image Classification ◽

Anomalous Diffusion ◽

Numerical Experiments ◽

Driving Forces ◽

Living Cells ◽

Diffusion Type ◽

Training Time ◽

Initial Network ◽

Unseen Data ◽

Insight Into

Identification of the diffusion type of molecules in living cells is crucial to deduct their driving forces and hence to get insight into the characteristics of the cells. In this paper, deep residual networks have been used to classify the trajectories of molecules. We started from the well known ResNet architecture, developed for image classification, and carried out a series of numerical experiments to adapt it to detection of diffusion modes. We managed to find a model that has a better accuracy than the initial network, but contains only a small fraction of its parameters. The reduced size significantly shortened the training time of the model. Moreover, the resulting network has less tendency to overfitting and generalizes better to unseen data.

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