Kaemika app, Integrating protocols and chemical simulation

Kaemika is an app available on the four major app stores. It provides deterministic and stochastic simulation, supporting natural chemical notation enhanced with recursive and conditional generation of chemical reaction networks. It has a liquid-handling protocol sublanguage compiled to a virtual digital microfluidic device. Chemical and microfluidic simulations can be interleaved for full experimental-cycle modeling. A novel and unambiguous representation of directed multigraphs is used to lay out chemical reaction networks in graphical form

Common principles and best practices for engineering microbiomes

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue hat structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries on the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyze microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy, and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture, and enabling the bioeconomy

A comprehensive survey of recent advancements in molecular communication

With much advancement in the field of nanotechnology, bioengineering and synthetic biology over the past decade, microscales and nanoscales devices are becoming a reality. Yet the problem of engineering a reliable communication system between tiny devices is still an open problem. At the same time, despite the prevalence of radio communication, there are still areas where traditional electromagnetic waves find it difficult or expensive to reach. Points of interest in industry, cities, and medical applications often lie in embedded and entrenched areas, accessible only by ventricles at scales too small for conventional radio waves and microwaves, or they are located in such a way that directional high frequency systems are ineffective. Inspired by nature, one solution to these problems is molecular communication (MC), where chemical signals are used to transfer information. Although biologists have studied MC for decades, it has only been researched for roughly 10 year from a communication engineering lens. Significant number of papers have been published to date, but owing to the need for interdisciplinary work, much of the results are preliminary. In this paper, the recent advancements in the field of MC engineering are highlighted. First, the biological, chemical, and physical processes used by an MC system are discussed. This includes different components of the MC transmitter and receiver, as well as the propagation and transport mechanisms. Then, a comprehensive survey of some of the recent works on MC through a communication engineering lens is provided. The paper ends with a technology readiness analysis of MC and future research directions

Design and Optimizing of On-Chip Kinesin Substrates for Molecular Communication

Lab-on-chip devices and point-of-care diagnostic chip devices are composed of many different components such as nanosensors that must be able to communicate with other components within the device. Molecular communication is a promising solution for on-chip communication. In particular, kinesin driven microtubule (MT) motility is an effective means of transferring information particles from one component to another. However, finding an optimal shape for these channels can be challenging. In this paper we derive a mathematical optimization model that can be used to find the optimal channel shape and dimensions for any transmission period. We derive three specific models for the rectangular channels, regular polygonal channels, and regular polygonal ring channels. We show that the optimal channel shapes are the square-shaped channel for the rectangular channel, and circular-shaped channel for the other classes of shapes. Finally, we show that among all 2 dimensional shapes the optimal design choice that maximizes information rate is the circular-shaped channel.Comment: accepted for publication in IEEE Transactions on Nanotechnolog

Test analysis & fault simulation of microfluidic systems

This work presents a design, simulation and test methodology for microfluidic systems, with particular focus on simulation for test. A Microfluidic Fault Simulator (MFS) has been created based around COMSOL which allows a fault-free system model to undergo fault injection and provide test measurements. A post MFS test analysis procedure is also described.A range of fault-free system simulations have been cross-validated to experimental work to gauge the accuracy of the fundamental simulation approach prior to further investigation and development of the simulation and test procedure.A generic mechanism, termed a fault block, has been developed to provide fault injection and a method of describing a low abstraction behavioural fault model within the system. This technique has allowed the creation of a fault library containing a range of different microfluidic fault conditions. Each of the fault models has been cross-validated to experimental conditions or published results to determine their accuracy.Two test methods, namely, impedance spectroscopy and Levich electro-chemical sensors have been investigated as general methods of microfluidic test, each of which has been shown to be sensitive to a multitude of fault. Each method has successfully been implemented within the simulation environment and each cross-validated by first-hand experimentation or published work.A test analysis procedure based around the Neyman-Pearson criterion has been developed to allow a probabilistic metric for each test applied for a given fault condition, providing a quantitive assessment of each test. These metrics are used to analyse the sensitivity of each test method, useful when determining which tests to employ in the final system. Furthermore, these probabilistic metrics may be combined to provide a fault coverage metric for the complete system.The complete MFS method has been applied to two system cases studies; a hydrodynamic “Y” channel and a flow cytometry system for prognosing head and neck cancer.Decision trees are trained based on the test measurement data and fault conditions as a means of classifying the systems fault condition state. The classification rules created by the decision trees may be displayed graphically or as a set of rules which can be loaded into test instrumentation. During the course of this research a high voltage power supply instrument has been developed to aid electro-osmotic experimentation and an impedance spectrometer to provide embedded test

Exploring Microfluidic Design Automation: Thin-wall Membrane Regulator

Microfluidics and lab-on-a-chip are a growing technology, influential in many areas of engineering. This project focuses on the necessity for better computer aided design tools for this area. Specifically, it focuses on the automated synthesis of T-junction components with thin-walled membranes for stability. A T-junction is a passive droplet generation component, common in microfluidics which suffers from behavior instability in highly integrated circuits with many components. One way of improving stability is using flexible membranes to mitigate pressure perturbations. This thesis describes the design process of such membranes so that a model can be used to synthesize stable T-junctions. The thesis also discusses Manifold, a software framework for automated synthesis of microfluidic circuits. This is the framework where the design process fits in. To compare the result of the software framework with the analytic model described, physical circuits were fabricated to validate the accuracy of the analytic model and the software. Besides the T-junction, another microfluidics component that was investigated was a “Capillary Electrophoresis Channel”. This component was also investigated with respect to automated synthesis and verification using the Manifold framework, and the details are discussed

REVERSE INSULATOR DIELECTROPHORESIS: UTILIZING DROPLET MICROENVIRONMENTS FOR DISCERNING MOLECULAR EXPRESSIONS ON CELL SURFACES

Lab-on-a-chip (LOC) technologies enable the development of portable analysis devices that use small sample and reagent volumes, allow for multiple unit operations, and couple with detectors to achieve high resolution and sensitivity, while having small footprints, low cost, short analysis times, and portability. Droplet microfluidics is a subset of LOCs with the unique benefit of enabling parallel analysis since each droplet can be utilized as an isolated microenvironment. This work explored adaptation of droplet microfluidics into a unique, previously unexplored application where the water/oil interface was harnessed to bend electric field lines within individual droplets for insulator dielectrophoretic (iDEP) characterizations. iDEP polarizes particles/cells within non-uniform electric fields shaped by insulating geometries. We termed this unique combination of droplet microfluidics and iDEP reverse insulator dielectrophoresis (riDEP). This riDEP approach has the potential to protect cell samples from unwanted sample-electrode interactions and decrease the number of required experiments for dielectrophoretic characterization by ~80% by harnessing the parallelization power of droplet microfluidics. Future research opportunities are discussed that could improve this reduction further to 93%. A microfluidic device was designed where aqueous-in-oil droplets were generated in a microchannel T-junction and packed into a microchamber. Reproducible droplets were achieved at the T-junction and were stable over long time periods in the microchamber using Krytox FSH 157 surfactant in the continuous oil FC-40 phase and isotonic salts and dextrose solutions as the dispersed aqueous phase. Surfactant, salts, and dextrose interact at the droplet interface influencing interfacial tension and droplet stability. Results provide foundational knowledge for engineering stable bio- and electro-compatible droplet microfluidic platforms. Electrodes were added to the microdevice to apply an electric field across the droplet packed chamber and explore riDEP responses. Operating windows for droplet stability were shown to depend on surfactant concentration in the oil phase and aqueous phase conductivity, where different voltage/frequency combinations resulted in either stable droplets or electrocoalescence. Experimental results provided a stability map for strategical applied electric field selection to avoid adverse droplet morphological changes while inducing riDEP. Within the microdevice, both polystyrene beads and red blood cells demonstrated weak dielectrophoretic responses, as evidenced by pearl-chain formation, confirming the preliminary feasibility of riDEP as a potential characterization technique. Two additional side projects included an alternative approach to isolate electrode surface reactions from the cell suspension via a hafnium oxide film over the electrodes. In addition, a commercially prevalent water-based polymer emulsion was found to adequately duplicate microchannel and microchamber features such that it could be used for microdevice replication

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML