Engineering Models to Scale

Main Text The physicist Richard Feynman famously wrote, “What I cannot create, I do not understand,” at the top of his final blackboard. This philosophy has inspired many in the emerging field of synthetic biology, which harnesses the power of biology to rationally engineer biomolecular systems for a variety of purposes, such as whole-cell biosensing and in vivo diagnostics (Slomovic et al., 2015). The “build-to-understand” approach (Elowitz and Lim, 2010) is complementary to top-down systems biology approaches and borrows concepts and techniques from engineering and computer science. By creating biological systems with desired architectures and functions, it aims to test design principles in relative isolation by exploring how biology’s building blocks, such as DNA-encoded genes, can be rearranged and altered to produce different phenotypes. In this issue, Cao et al. use this approach to tackle the question of how self-organizing systems maintain a constant ratio of physical pattern features with changing size, a property known as scale invariance (Cao et al., 2016)

Fabricating Microfluidic Valve Master Molds in SU‐8 Photoresist

Multilayer soft lithography has become a powerful tool in analytical chemistry, biochemistry, material and life sciences, and medical research. Complex fluidic micro-circuits require reliable components that integrate easily into microchips. We introduce two novel approaches to master mold fabrication for constructing in-line micro-valves using SU-8. Our fabrication techniques enable robust and versatile integration of many lab-on-a-chip functions including filters, mixers, pumps, stream focusing and cell-culture chambers, with in-line valves. SU-8 created more robust valve master molds than the conventional positive photoresists used in multilayer soft lithography, but maintained the advantages of biocompatibility and rapid prototyping. As an example, we used valve master molds made of SU-8 to fabricate PDMS chips capable of precisely controlling beads or cells in solution

The Effect of Compositional Context on Synthetic Gene Networks

It is well known that synthetic gene expression is highly sensitive to how genetic elements (promoter structure, spacing regions between promoter and coding sequences, ribosome binding sites, etc.) are spatially configured. An important topic that has received far less attention is how the compositional context, or spatial arrangement, of entire genes within a synthetic gene network affects their individual expression levels. In this paper we show, both quantitatively and qualitatively, that compositional context significantly alters transcription levels in synthetic gene networks. We demonstrate that key characteristics of gene induction, such as ultra-sensitivity and dynamic range, strongly depend on compositional context. We postulate that supercoiling can be used to explain this interference and validate this hypothesis through modeling and a series of in vitro supercoiling relaxation experiments. This compositional interference enables a novel form of feedback in synthetic gene networks. We illustrate the use of this feedback by redesigning the toggle switch to incorporate compositional context. We show the context-optimized toggle switch has improved threshold detection and memory properties

Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks

Synthetic gene expression is highly sensitive to intragenic compositional context (promoter structure, spacing regions between promoter and coding sequences, and ribosome binding sites). However, much less is known about the effects of intergenic compositional context (spatial arrangement and orientation of entire genes on DNA) on expression levels in synthetic gene networks. We compare expression of induced genes arranged in convergent, divergent, or tandem orientations. Induction of convergent genes yielded up to 400% higher expression, greater ultrasensitivity, and dynamic range than divergent- or tandem-oriented genes. Orientation affects gene expression whether one or both genes are induced. We postulate that transcriptional interference in divergent and tandem genes, mediated by supercoiling, can explain differences in expression and validate this hypothesis through modeling and in vitro supercoiling relaxation experiments. Treatment with gyrase abrogated intergenic context effects, bringing expression levels within 30% of each other. We rebuilt the toggle switch with convergent genes, taking advantage of supercoiling effects to improve threshold detection and switch stability

Nucleic acid detection with CRISPR-Cas13a/C2c2

Rapid, inexpensive, and sensitive nucleic acid detection may aid point-of-care pathogen detection, genotyping, and disease monitoring. The RNA-guided, RNA-targeting clustered regularly interspaced short palindromic repeats (CRISPR) effector Cas13a (previously known as C2c2) exhibits a "collateral effect" of promiscuous ribonuclease activity upon target recognition. We combine the collateral effect of Cas13a with isothermal amplification to establish a CRISPR-based diagnostic (CRISPR-Dx), providing rapid DNA or RNA detection with attomolar sensitivity and single-base mismatch specificity. We use this Cas13a-based molecular detection platform, termed Specific High-Sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK), to detect specific strains of Zika and Dengue virus, distinguish pathogenic bacteria, genotype human DNA, and identify mutations in cell-free tumor DNA. Furthermore, SHERLOCK reaction reagents can be lyophilized for cold-chain independence and long-term storage and be readily reconstituted on paper for field applications.United States. Air Force Office of Scientific Research (Grant FA9550-14-1-0060)Defense Threat Reduction Agency (DTRA) (Grant HDTRA1-14-1-0006)National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706)National Institutes of Health (U.S.) (Grant 1R01-MH110049

Cell-free synthetic biology for affordable, on-demand diagnostics

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 104-116).Detection of biomarkers, such as nucleic acids, performs critical roles in managing infectious disease outbreaks, point-of-care testing, and public health monitoring. However, many diseases and public health problems suffer from a lack of affordable, portable tests that can be used to sensitively detect nucleic acids and respond in a rapid manner. Current methods to nucleic acid testing are too expensive, slow, and complex to be routinely used outside of specialized lab settings. New diagnostic tools are needed that can work in resource-limited settings to help guide prompt treatment decisions, prevent spread of infectious diseases, and inform public health decisions. Cell-free synthetic biology has shown promise as a portable, affordable technology to detect biomolecules like nucleic acids. In this thesis, I present several advancements to cell-free synthetic biology diagnostics that enable new application areas. First, I present a paper-based cell-free synthetic biology platform using RNA toehold switch sensors to detect RNAs from human gut microbiome. We showed that this method could quantify bacterial and human RNA transcripts comparably to gold standard methods while reducing time and cost. Next, I used similar cell-free detection technology to create a set of fruit DNA-sensing demonstrations that can be used in high school biology classrooms. I then sought to engineer biomolecular circuits that can process multiple sensor inputs to reduce cost, improve specificity, and build classifier circuits. Finally, I present work to develop and use clustered regularly interspaced short palindromic repeats (CRISPR) enzyme-based diagnostics to achieve attomolar sensitivity and single-nucleotide mismatch specificity. Together, these projects demonstrate a set of advancements in cell-free synthetic biology diagnostics toward filling the gap of nucleic acid detection technologies that are low-cost, portable, sensitive, and easy to use.by Aaron J. Dy.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Biological Engineerin

Control theory meets synthetic biology

The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology.United States. Air Force. Office of Scientific Research (grant no. FA9550-14-1- 0060)United States. Office of Naval Research (grant no. N000141310074)National Science Foundation (U.S.). Graduate Research Fellowship Progra

Automated Curling, Stamping and Counting machine (ACSC machine)

From the paint to food industry, tin cans have shown its worth. An essential part of the can which seals and covers the products is the can lid. Genstar Manufacturing Corp. is a manufacturer of industrial tin cans supplying to the packaging needs of top paint industries in the Philippines. Currently, the company is using two manually operated machines (curling and stamping machine) in producing can lids with two manual processes (stacking and counting). There are operators for each machine and another worker for transporting, counting and stocking the finished product. This setup is not only costly due to the manpower used but also requires a labor intensive production. The set-up paved the way for the design of an Automated Curling, Stamping and Counting Machine. The objective of this project is to design and fabricate an Automated Curling, Stamping and Counting (ACSC) machine that maximize the production at lower cost. Studying the existing processes and machines provided some ideas for the group\u27s design of the prototype. AutoCAD software was used for the design of the prototype. Conveyors were used for transporting the can lid from one process to another. Programmable Logic Controller (PLC), Pneumatics and Sensors were utilized for the movement of the different parts of the prototype. The group conducted time and motion study in the old process to identify the significance of automation and use the results to prove effectiveness and productivity of the automated machine. The prototype was able to curl, stamp, count and stack can lids reliably through the use of experiments. The prototype was able to produce an average output capacity of 19 can lids per minute, which is three times higher, compared to the current output of can lids per minute

Engineering Models to Scale

Main Text The physicist Richard Feynman famously wrote, “What I cannot create, I do not understand,” at the top of his final blackboard. This philosophy has inspired many in the emerging field of synthetic biology, which harnesses the power of biology to rationally engineer biomolecular systems for a variety of purposes, such as whole-cell biosensing and in vivo diagnostics (Slomovic et al., 2015). The “build-to-understand” approach (Elowitz and Lim, 2010) is complementary to top-down systems biology approaches and borrows concepts and techniques from engineering and computer science. By creating biological systems with desired architectures and functions, it aims to test design principles in relative isolation by exploring how biology’s building blocks, such as DNA-encoded genes, can be rearranged and altered to produce different phenotypes. In this issue, Cao et al. use this approach to tackle the question of how self-organizing systems maintain a constant ratio of physical pattern features with changing size, a property known as scale invariance (Cao et al., 2016)

A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers

There is a need for large-scale, longitudinal studies to determine the mechanisms by which the gut microbiome and its interactions with the host affect human health and disease. Current methods for profiling the microbiome typically utilize next-generation sequencing applications that are expensive, slow, and complex. Here, we present a synthetic biology platform for affordable, on-demand, and simple analysis of microbiome samples using RNA toehold switch sensors in paper-based, cell-free reactions. We demonstrate species-specific detection of mRNAs from 10 different bacteria that affect human health and four clinically relevant host biomarkers. We develop a method to quantify mRNA using our toehold sensors and validate our platform on clinical stool samples by comparison to RT-qPCR. We further highlight the potential clinical utility of the platform by showing that it can be used to rapidly and inexpensively detect toxin mRNA in the diagnosis of Clostridium difficile infections.National Institutes of Health (U.S.) (Grant T32-DK007191