Biomolecular implementation of linear I/O systems

Linear I/O systems are a fundamental tool in systems theory, and have been used to design complex circuits and control systems in a variety of settings. Here we present a principled design method for implementing arbitrary linear I/O systems with biochemical reactions. This method relies on two levels of abstraction: first, an implementation of linear I/O systems using idealised chemical reactions, and second, an approximate implementation of the ideal chemical reactions with enzyme-free, entropy-driven DNA reactions. The ideal linear dynamics are shown to be closely approximated by the chemical reactions model and the DNA implementation. We illustrate the approach with integration, gain and summation as well as with the ubiquitous robust proportional-integral controller.     

Design of a biochemical circuit motif for learning linear functions

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.     

Modelling and analysis of the sugar cataract development process using stochastic hybrid systems

Modelling and analysis of biochemical systems such as sugar cataract development (SCD) are critical because they can provide new insights into systems, which cannot be easily tested with experiments; however, they are challenging problems due to the highly coupled chemical reactions that are involved. The authors present a stochastic hybrid system (SHS) framework for modelling biochemical systems and demonstrate the approach for the SCD process. A novel feature of the framework is that it allows modelling the effect of drug treatment on the system dynamics. The authors validate the three sugar cataract models by comparing trajectories computed by two simulation algorithms. Further, the authors present a probabilistic verification method for computing the probability of sugar cataract formation for different chemical concentrations using safety and reachability analysis methods for SHSs. The verification method employs dynamic programming based on a discretisation of the state space and therefore suffers from the curse of dimensionality. To analyse the SCD process, a parallel dynamic programming implementation that can handle large, realistic systems was developed. Although scalability is a limiting factor, this work demonstrates that the proposed method is feasible for realistic biochemical systems.     

Examining the architecture of cellular computing through a comparative study with a computer.

The computer and the cell both use information embedded in simple coding, the binary software code and the quadruple genomic code, respectively, to support system operations. A comparative examination of their system architecture as well as their information storage and utilization schemes is performed. On top of the code, both systems display a modular, multi-layered architecture, which, in the case of a computer, arises from human engineering efforts through a combination of hardware implementation and software abstraction. Using the computer as a reference system, a simplistic mapping of the architectural components between the two is easily detected. This comparison also reveals that a cell abolishes the software-hardware barrier through genomic encoding for the constituents of the biochemical network, a cell's "hardware" equivalent to the computer central processing unit (CPU). The information loading (gene expression) process acts as a major determinant of the encoded constituent's abundance, which, in turn, often determines the "bandwidth" of a biochemical pathway. Cellular processes are implemented in biochemical pathways in parallel manners. In a computer, on the other hand, the software provides only instructions and data for the CPU. A process represents just sequentially ordered actions by the CPU and only virtual parallelism can be implemented through CPU time-sharing. Whereas process management in a computer may simply mean job scheduling, coordinating pathway bandwidth through the gene expression machinery represents a major process management scheme in a cell. In summary, a cell can be viewed as a super-parallel computer, which computes through controlled hardware composition. While we have, at best, a very fragmented understanding of cellular operation, we have a thorough understanding of the computer throughout the engineering process. The potential utilization of this knowledge to the benefit of systems biology is discussed.     

Solution of linear systems in arterial fluid mechanics computations with boundary layer mesh refinement

Computation of incompressible flows in arterial fluid mechanics, especially because it involves fluid–structure interaction, poses significant numerical challenges. Iterative solution of the fluid mechanics part of the equation systems involved is one of those challenges, and we address that in this paper, with the added complication of having boundary layer mesh refinement with thin layers of elements near the arterial wall. As test case, we use matrix data from stabilized finite element computation of a bifurcating middle cerebral artery segment with aneurysm. It is well known that solving linear systems that arise in incompressible flow computations consume most of the time required by such simulations. For solving these large sparse nonsymmetric systems, we present effective preconditioning techniques appropriate for different stages of the computation over a cardiac cycle.     

Propagation of fluctuations in biochemical systems, II: Nonlinear chains

We consider biochemical reaction chains and investigate how random external fluctuations, as characterised by variance and coefficient of variation, propagate down the chains. We perform such a study under the assumption that the number of molecules is high enough so that the behaviour of the concentrations of the system is well approximated by differential equations. We conclude that the variances and coefficients of variation of the fluxes will decrease as one moves down the chain and, through an example, show that there is no corresponding result for the variances of the concentrations of the chemical species. We also prove that the fluctuations of the fluxes as characterised by their time averages decrease down reaction chains. The results presented give insight into how biochemical reaction systems are buffered against external perturbations solely by their underlying graphical structure and point out the benefits of studying the out-of-equilibrium dynamics of systems.     

Advances in nanomaterials for brain microscopy

Microscopic imaging of the brain continues to reveal details of its structure, connectivity, and function. To further improve our understanding of the emergent properties and functions of neural circuits, we need to directly visualize the relationship between brain structure, neuron activity, and neurochemistry. Advances in the chemical and optical properties of nanomaterials, and developments in deep-tissue microscopy, may help to overcome the current challenges of in-vivo brain imaging, particularly when imaging the brain through optically-dense brain tissue, skull, and scalp. Developments in nanomaterials may enable the implementation of tunable chemical functionality for neurochemical targeting and sensing, and fluorescence stability for long-term imaging. In this review, we summarize the current methods used for brain microscopy and describe the diverse classes of nanomaterials recently offered as contrast agents and functional probes for microscopic optical imaging of the brain.     

Engineering materials for artificial cells

The grand challenge of engineering a minimal artificial cell provides a controllable framework for studying the biochemical principles of life. Artificial cells contribute to an increased understanding of complex synthetic systems with life-like properties and provide opportunities to create autonomous cell-like materials. Recent efforts to develop life-like artificial cells by bottom-up approaches involve mimicking the behavior of lipid membranes to recapitulate fundamental cellular processes. This review describes the recent progress in engineering biomimetic artificial minimal cells and recently developed chemical strategies to drive de novo membrane formation from simple synthetic precursors. In the end, we briefly point out the challenges and possible future directions in the development of artificial cells.     

Sensing at the Surface of Graphene Field‐Effect Transistors

Recent research trends now offer new opportunities for developing the next generations of label‐free biochemical sensors using graphene and other two‐dimensional materials. While the physics of graphene transistors operated in electrolyte is well grounded, important chemical challenges still remain to be addressed, namely the impact of the chemical functionalizations of graphene on the key electrical parameters and the sensing performances. In fact, graphene – at least ideal graphene – is highly chemically inert. The functionalizations and chemical alterations of the graphene surface – both covalently and non‐covalently – are crucial steps that define the sensitivity of graphene. The presence, reactivity, adsorption of gas and ions, proteins, DNA, cells and tissues on graphene have been successfully monitored with graphene. This review aims to unify most of the work done so far on biochemical sensing at the surface of a (chemically functionalized) graphene field‐effect transistor and the challenges that lie ahead. The authors are convinced that graphene biochemical sensors hold great promise to meet the ever‐increasing demand for sensitivity, especially looking at the recent progresses suggesting that the obstacle of Debye screening can be overcome.     

Building Biomedical Materials using Photochemical Bond Cleavage

Light can be used as an external trigger to precisely determine where and when a process is initiated as well as how much of the process is being consumed. Phototriggers are a type of photoresponsive functional group that undergo an irreversible photolysis reaction by selectively breaking a chemical bond, enabling three fundamental functions: the photoactivation of fluorescent and bioactive molecules; the photocleavable degradation of macromolecular materials; and the photorelease of drugs, active groups, or surface charges from carriers and interfaces. With the expanded applications of light‐controlled technology, particularly in living systems, new challenges and improvements of phototriggers are required to fulfill the demands for better sensitivity, faster kinetics, and more‐demanding biomedical applications. Here, improvements to several conventional phototriggers are highlighted, and their notable, representative biomedical applications and their challenges are discussed.     

Robust Parameter Estimation in Dynamic Systems

In this paper we present a practical method for robust parameter estimation in dynamic systems. In our study we follow the very successful approach for solving optimization problems in dynamic systems, namely the boundary value problem (BVP) approach. The suggested method combines multiple shooting for parameterizing dynamics, a flexible realization of the BVP principle, with a fast Gauss-Newton algorithm for solving the resulting constrained l ₁ problem. We give an overview of the theoretical background as well as the details of a numerical implementation. We discuss why the Gauss-Newton algorithm, which is known to perform well mainly on well-conditioned problems, is appropriate for parameter estimation problems, while quasi-Newton methods have only limited use for parameter estimation. The method is implemented on the basis of the direct multiple shooting method as implemented in PARFIT, thus inheriting all basic properties of PARFIT such as numerical stability, reliability and efficiency. The new code has been successfully applied to real-life parameter estimation problems in enzyme and chemical kinetics.     

Acoustic Trapping: An Emerging Tool for Microfabrication Technology

The high throughput deposition of microscale objects with precise spatial arrangement represents a key step in microfabrication technology. This can be done by creating physical boundaries to guide the deposition process or using printing technologies; in both approaches, these microscale objects cannot be further modified after they are formed. The utilization of dynamic acoustic fields offers a novel approach to facilitate real-time reconfigurable miniaturized systems in a contactless manner, which can potentially be used in physics, chemistry, biology, as well as materials science. Here, the physical interactions of microscale objects in an acoustic pressure field are discussed and how to fabricate different acoustic trapping devices and how to tune the spatial arrangement of the microscale objects are explained. Moreover, different approaches that can dynamically modulate microscale objects in acoustic fields are presented, and the potential applications of the microarrays in biomedical engineering, chemical/biochemical sensing, and materials science are highlighted alongside a discussion of future research challenges.     

Novel strategies for the buccal delivery of macromolecules

For years now, the delivery of small molecules through the buccal mucosal route has been described in the literature, but it has only been over the past decade that investigations into macromolecule delivery via the buccal route have sharply increased. The administration of macromolecules such as proteins and peptides, antibodies, or nucleic acids by buccal administration would be greatly enhanced due to the avoidance of the gastrointestinal conditions, rapid uptake into systemic circulation, as well as the potential for controlled drug delivery. Since macromolecules are faced with a number of specific challenges related to permeation through the epithelium, several strategies have been employed historically to improve their buccal absorption and subsequent bioavailability. Several conventional strategies to improve macromolecule penetration include the use of chemical permeation enhancers, enzyme inhibitors and the use of mucoadhesive materials acting as carriers. More recent approaches include the incorporation of the macromolecule as part of nanostructured delivery systems to further enhance targeting and delivery. This review focuses on the different permeation enhancing strategies as well as formulation design that are tailored to meet the challenges of active macromolecule delivery using the buccal mucosal route of administration.     

On-chip surface-based detection with nanohole arrays

A microfluidic device with integrated surface plasmon resonance (SPR) chemical and biological sensors based on arrays of nanoholes in gold films is demonstrated. Widespread use of SPR for surface analysis in laboratories has not translated to microfluidic analytical chip platforms, in part due to challenges associated with scaling down the optics and the surface area required for common reflection mode operation. The resonant enhancement of light transmission through subwavelength apertures in a metallic film suggests the use of nanohole arrays as miniaturized SPR-based sensing elements. The device presented here takes advantage of the unique properties of nanohole arrays: surface-based sensitivity; transmission mode operation; a relatively small footprint; and repeatability. Proof-of-concept measurements performed on-chip indicated a response to small changes in refractive index at the array surfaces. A sensitivity of 333 nm per refractive index unit was demonstrated with the integrated device. The device was also applied to detect spatial microfluidic concentration gradients and to monitor a biochemical affinity process involving the biotin-streptavidin system. Results indicate the efficacy of nanohole arrays as surface plasmon-based sensing elements in a microfluidic platform, adding unique surface-sensitive diagnostic capabilities to the existing suite of microfluidic-based analytical tools.     

Domain framework for implementation of open IoT ecosystems

The current Internet-of-things (IoT) hype, pushed by the unprecedented rate of the technological enablers’ innovation, is threatening to leave behind some major, not so obvious, unresolved issues. IoT platforms will extend existing enterprise information systems (EIS) infrastructures to encompass cross-domain sensing and actuating capabilities, thus introducing additional complexity and major risks to the implementation. Furthermore, IoT platforms are typically driven by models of the trivial complexity; they support very simple data structures and almost no business logic implementation. Finally, IoT systems are today managed centrally, which often means less openness, less flexibility and greater change management costs. In this article, we provide the overview of the scientific disciplines which could contribute to the resolution of the IoT implementation problem, namely requirements engineering, change management/continuous improvement, model-based systems engineering, system architecture design, interoperability and policy and regulatory aspects. Then, we identify the challenges of these contributions in the context of IoT and finally make an attempt to identify research directions which could have a significant impact. The discussion of the challenges and opportunities is illustrated by the proposed domain framework for implementation of open IoT ecosystems.     

Tailoring properties and functionalities of metal nanoparticles through crystallinity engineering

Metal nanoparticles (NPs) with size comparable to their electron mean free path possess unusual properties and functionalities, serving as model systems to explore quantum and classical coupling interactions as well as building blocks of practical applications. Although advances in strategies for synthesizing metal NPs have enabled control of size, composition and shape, the requirement that defects are simultaneously controlled, to ensure essential perfect nanocrystallinity for physics modelling as well as device optimization, is a potentially more significant issue, but has posed substantial technological challenges. Here we report that crystallinity of monodisperse silver NPs can be well controlled by judicious choice of functional groups of molecular precursors, thus facilitating investigation of their scope for versatile applications. We demonstrate how nanoscale chemical transformation, electron-phonon interactions and nanomechanical properties are modified by nanocrystallinity. Lastly, we find that performance of NP-based molecular sensing devices can be optimized with significant improvement of figure of merit if perfect single-crystalline NPs are applied. Our approach represents a versatile synthetic route for other metal nanomaterials with unprecedented control of their structure, creating a rational pathway for understanding and manipulating nanoscale chemical and physical processes as well as technological applications of metal NPs.     

Control of a biomolecular motor-powered nanodevice with an engineered chemical switch

The biophysical and biochemical properties of motor proteins have been well-studied, but these motors also show promise as mechanical components in hybrid nano-engineered systems. The cytoplasmic F(1) fragment of the adenosine triphosphate synthase (F1-ATPase) can function as an ATP-fuelled rotary motor and has been integrated into self-assembled nanomechanical systems as a mechanical actuator. Here we present the rational design, construction and analysis of a mutant F1-ATPase motor containing a metal-binding site that functions as a zinc-dependent, reversible on/off switch. Repeated cycles of zinc addition and removal by chelation result in inhibition and restoration, respectively, of both ATP hydrolysis and motor rotation of the mutant, but not of the wild-type F1 fragment. These results demonstrate the ability to engineer chemical regulation into a biomolecular motor and represent a critical step towards controlling integrated nanomechanical devices at the single-molecule level.     

A pipelined noise shaping coder for fractional-N frequencysynthesis

In this paper, we present the design considerations and implementation aspects of a pipelined all-digital fourth-order multi-stage-noise-shaping (MASH) delta-sigma (▵Σ) modulator suitable for fractional-N (F-N) phase-locked loop (PLL) frequency synthesis applications. In an effort to reduce the hardware complexity and power consumption, the alignment registers, which are normally required in pipelined adders, are eliminated by taking advantage of static modulator input. The MASH modulator has successfully been targeted to an Altera^TM field-programmable-gate-array (FPGA) device. The functional operation of the modulator has been verified through structural bit-level simulations as well as experimental results on the actual FPGA implementation     

Thermochemistry of silicon carbide growth by chemical transport reactions

Chemical Vapour Transport is a well known process widely used for the growth of monocrystals. This paper is a thermodynamic overview of different heterogeneous chemical systems, promising for the growth of silicon carbide by means of chemical transport reactions. The systems are Si-C-Y where Y is oxygen or a chalcogen (S, Se) and Si-C-H-X where X is an halogen (Cl, Br, I). We studied in a first step the gas phase composition obtained from SiC etching with the transporting agent as a function of temperature. In a second step, we report the conditions for the formation of silicon carbide from such a vapour at a different temperature. Finally we discuss optimal conditions of temperatures and thermal gradients required for SiC transport with each systems.     

Microfluidic arrays of fluid-fluid diffusional contacts as detection elements and combinatorial tools.

This paper describes microfluidic systems that can be used to investigate multiple chemical or biochemical interactions in a parallel format. These three-dimensional systems are generated by crossing two sets of microfluidic channels, fabricated in two different layers, at right angles. Solutions of the reagents are placed in the channels; in different modes of operation, these solutions can be either flowing or stationary-the latter is important when one set of channels is filled with viscous gels with immobilized reagents. At every crossing, the channels are separated either by a single membrane or by a composite separator comprising a membrane, a microwell, and a second membrane. These components allow diffusive mass transport and minimize convective transport through the crossing. Polycarbonate membranes with 0.1-1-microm vertical pores were used to fabricate the devices. Each crossing of parallel channels serves as an element in which chemical or biochemical interactions can take place; interactions can be detected by monitoring changes in fluorescence and absorbance. These all-organic systems are straightforward to fabricate and to operate and may find applications as portable microanalytical systems and as tools in combinatorial research.