Recent far-reaching advances in synthetic biology have yielded exciting tools for the creation of new materials. Conversely, advances in the fundamental understanding of soft-condensed matter, polymers and biomaterials offer new avenues to extend the reach of synthetic biology. The broad and exciting range of possible applications have substantial implications to address grand challenges in health, biotechnology and sustainability. Despite the potentially transformative impact that lies at the interface of synthetic biology and biomaterials, the two fields have, so far, progressed mostly separately. This Perspective provides a review of recent key advances in these two fields, and a roadmap for collaboration at the interface between the two communities. We highlight the near-term applications of this interface to the development of hierarchically structured biomaterials, from bioinspired building blocks to ‘living’ materials that sense and respond based on the reciprocal interactions between materials and embedded cells. This Perspective reviews the complementary developments in synthetic biology and biomaterials and discusses how convergence of these two fields creates a promising design strategy for the fabrication of tailored living materials for medicine and biotechnology. 

/pdf/the-living-interface-between-synthetic-biology-and-3c0rty0m.pdf

The living interface between synthetic biology and biomaterial design

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences. Devices for droplet generation are at the heart of many microfluidic applications but difficult to tailor for specific cases. Lashkaripour et al. show how design customization can greatly be simplified by combining rapid prototyping with data-driven machine learning strategies.

/pdf/machine-learning-enables-design-automation-of-microfluidic-1vwna2efis.pdf

Machine learning enables design automation of microfluidic flow-focusing droplet generation.

Droplet-based microfluidics

Worldwide, the microfluidics industry has grown steadily over the last 5 years, with the market for microfluidic medical devices experiencing a compound growth rate of 22%. The number of submissions of microfluidic-based devices to regulatory agencies such as the U.S. Food & Drug Administration (FDA) has also steadily increased, creating a strong demand for the development of consistent and accessible tools for evaluating microfluidics-based devices. The microfluidics community has been slow, or even reluctant, to adopt standards and guidelines, which are needed for harmonization and for assisting academia, researchers, designers, and industry across all stages of product development. Appropriate assessments of device performance also remain a bottleneck for microfluidic devices. Standards reside at the core of mature supply chains generating economies of scale and forging a consistent pathway to match stakeholder expectations, thus creating a foundation for successful commercialization. This article provides a unique perspective on the need for the development of standards specific to the emerging biomedical field of microfluidics. Our aim is to facilitate innovation by encouraging the microfluidics community to work together to help bridge knowledge gaps and improve efficiency in getting high-quality microfluidic medical devices to market faster. We start by acknowledging the progress that has been made in various areas over the past decade. We then describe the existing gaps in the standardization of flow control, interconnections, component integration, manufacturing, assembly, packaging, reliability, performance of microfluidic elements and safety testing of microfluidic devices throughout the entire product life cycle.

Accelerating innovation and commercialization through standardization of microfluidic-based medical devices

Droplet based microfluidics (DBMF) has gained huge recognition in the recent years for performing micro-reactions in droplets with high throughput, sensitivity, specificity and minimum cross-contaminations. This technology enables the researchers to realize highly reliable and rapid detection and screening applications in various fields. The high-throughput nature of droplet microfluidics generates large amounts of valuable but complex droplet dataset. Deeper analysis of this intricate droplet data is very essential for detection, classification, characterization and quantification of reactions/content inside the droplets. This can be carried out by Machine Learning (ML), which has proven itself in processing and providing deeper insights and precise predictions of relatively large amounts of complex data with shorter analysis times and exceptional accuracy. The analytical tools of ML enable to imbibe automation and control of many such diagnostic platforms, including DBMF, with minimum human intervention. In recent times, the potential of ML has been explored in microfluidic technology as well to tackle challenges in biomedical and biotechnological applications. The synergy of both the fields, DBMF and ML, helps in development of optimized and automated tools with higher accuracy for numerous applications. Specifically, this enables complete comprehension of the field to eventually realize a truly microfluidic total analysis system (µTAS). This work comprehends a general review emphasizing the implementation of different ML models with DBMF to automate various activities such as fluid control, droplet size prediction, recognition of flow pattern and identification, classification and sorting of droplets in a microfluidic device.

Droplet based microfluidics integrated with machine learning

The lack of standardization in the specification and representation of microfluidic designs and their corresponding architectures is one of the largest hurdles faced by the developers of Microfluidic Design Automation (MDA) tools. In this paper, we introduce MINT, a Microfluidic Hardware Description Language (MHDL) for defining components and devices in a human readable manner, and ParchMint, an MDA interchange format and associated benchmark suite that can be used to compare the performance of different physical design algorithms. We further demonstrate how the introduction of MINT and ParchMint into the engineering workflow can bridge the gaps from the specification to the fabrication of microfluidic devices. While recent efforts to democratize microfluidics have been recognized by the community, there is an unfortunate lack of open source tools, design languages, and standards. Consequently, microfluidic designs shared on open platforms such as Metafluidics[15] leave conceptual gaps in terms of missing design information that are necessary to realize the “creative process flows” (Reproduce, Remix, and Test multiple systems). MINT and ParchMint are open source projects, which allows the community to contribute and extend their functionality to enable advanced algorithmic methodologies and new commercialization possibilities that differ from the vertically integrated industries we see today.

Specification, Integration, and Benchmarking of Continuous Flow Microfluidic Devices: Invited Paper

This article was accepted and presented at the 9th International Workshop on Bio-Design Automation, Pittsburgh, Pennsylvania (2017).

Design automation based on fluid dynamics

Continuous-flow based microfluidic laboratory-on-a-chip (LoC) devices have gained traction in recent years for their ability to automate biological experiments at micro and nano-liter scales. While the design automation algorithms for these devices have been maturing, there has yet to be any work to create a benchmark suite to enable the analysis of algorithmic quality or the exchange of device designs between researchers. We propose ParchMint, a new standard interchange format for continuous-flow based microfluidic LoCs and an associated set of benchmarks.

ParchMint: A Microfluidics Benchmark Suite

MINT - Microfluidic Netlist

Function-driven, graphical design tool for microfluidic chips: 3DuF

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Radhakrishna Sanka

Author Tools

Chat about Author