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1502 results in Biotechnology

Page 10 of 76

  • 5 - Ion-Channel Switch (ICS) Biosensor

  • from Part II - Building Engineered Membranes, Devices, and Experimental Results
  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp 83-102

  • Summary

    Introduction

    In this chapter we describe the ion-channel switch (ICS) biosensor. By a biosensor, we mean a sensor built out of synthetic biological material – in our case, built out of an artificial membrane, ion channels, spacers, and tethers. The purpose of the biosensor is to detect the presence of analyte molecules.

    The ICS biosensor provides an example of how devices built out of tethered engineered membranes can be used for high-resolution detection of specific target (analyte) molecules. When it senses the presence of analyte molecules, the ICS biosensor responds by changing its electrical impedance significantly. The amount the impedance changes by depends on the concentration of the analyte molecules. Therefore, by examining the current flowing through the biosensor, one can detect both the presence of the analyte and its concentration. A key feature of the ICS biosensor is that the detection is performed by measuring the time-dependent conduction of the engineered membrane, which is dependent on the ensemble of aqueous pores and a single pair of conducting gramicidin (gA) dimers present. A gramicidin dimer [83, 84, 466] is an example of a conducting ion channel which allows ions to pass through the tethered membrane. Using specific molecular components and excitation potentials, the ICS can be designed to detect specific analyte molecules of interest.

    The ICS biosensor provides an interesting example of engineering at the nanoscale. It is remarkable that the functionality of the device depends on approximately 100 lipids, and a pair of ion channels modulating the flow of billions of ions in a typical sensing event of approximately 5 min. Since the gramicidin monomers (each with a conducting pore of diameter 0.4 nm and length 2.8 nm) move randomly in the outer lipid leaflet of the membrane (1.4 nm thick), we can view the biosensor as a fully functioning nanomachine with moving parts. Indeed, each individual gramicidin monomer diffuses randomly over an area of order 1 μm2. Furthermore, the 4-nm-thick lipid bilayer is tethered 2–3 nm away from the gold surface by hydrophilic spacers, thereby allowing ions to diffuse between the membrane and gold. This permits a flux in excess of 106 ions per second to traverse each gramicidin channel comprised of two gA monomers.

  • 2 - Biochemistry for Engineers: A Short Primer

  • from Part I - Introduction and Background
  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp 9-29

  • Summary

    Biochemistry deals with chemical processes occurring in living organisms. An elementary understanding of the biochemistry of cell membranes is important for understanding the key ideas behind engineered artificial membranes and biosensing. This chapter gives a brief description of the key chemical components in cell membranes, namely, lipids, proteins, and peptides. Lipids are the primary component of all cell membranes; and peptides and proteins are the primary component of signaling in living organisms. Additionally, we discuss tethers and spacers, which provide the basis for connecting engineered membranes to a bioelectronic interface; this bioelectronic interface is connected to electrical measurement equipment that allow signals to be acquired from the device.

    With the availability of powerful computer-based visualization tools (discussed at the end of this chapter), it is strongly recommended that the reader use these tools to get a better feel for the various types of biochemical molecules described in this chapter. These software tools are also a first step in more sophisticated molecular dynamics simulations that are discussed in Part III of the book.

    Bonded and Nonbonded Molecular Interactions

    A chemical bond is an attraction between two or more atoms that facilitates the formation of multiatom chemical compounds. There are two main types of chemical bonds: ionic bonds and covalent bonds. In addition there are also van der Waals forces, a type of nonbonded interaction between atoms, which include attraction or repulsion between atoms that do not arise from covalent or ionic bonds. Specifically, van der Waals forces are a consequence of quantum dynamics in which repulsive or attractive forces between atoms result from correlations in the fluctuating polarizations of atoms. In this section we define the covalent and ionic bonds as well as the van der Waals interaction between atoms and their importance in the dynamics of cell membranes. Additionally, we describe their importance when constructing molecular dynamics simulations.

    Covalent bond. A covalent bond is a chemical bond formed by sharing electrons between atoms. Covalent bonds are key to the structure of nearly all chemical compounds such as water, lipids, peptides, and proteins. The covalent bond is one of the strongest bonds compared with ionic bonds and van der Waals interactions. Typically the covalent bond has a bond energy in the range of 120 to 1000 kJ/mol (120– 500 kJ/mol for single bonds, and up to 1000 kJ/mol for triple bonds).

  • Preface

  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp xv-xvii

  • Summary

    Biological membranes such as the cell membrane play an essential and ubiquitous role in cellular communication, energy storage, structural support, and protecting the contents of living cells. Biological membranes are dynamic structures with remarkable properties: they have moving parts, they act as electrical circuits with long-range memory, they contain protein macromolecules (ion channels) that selectively open and close to admit ions, thereby regulating the electrical activities of cells, and they spontaneously form pores (electroporation) which is crucial for the operation of antimicrobial drugs.

    This book focuses on building and mathematically modeling artificial membranes that mimic biological membranes. Recent advances in bioengineering and biochemistry allow us to build artificial membranes precisely to mimic the remarkable properties of biological membranes and also to build synthetic biological devices out of these membranes. Also advances in computational molecular biology allow us to construct largescale computer simulation models at the atomic-spatial scale to gain deep insight into the dynamic properties and structure-function relationship of such artificial membranes and devices. This dual approach of synthesis (building membranes and novel devices) and analysis (mathematical modeling) is vital for the future development of sensing devices, drug delivery mechanisms, and synthetic biological devices.

    Summary. This book provides a comprehensive description of artificial membranes aimed at advanced undergraduate and graduate students, and researchers in electrical engineering, biological and chemical engineering, biophysics, and applied mathematics. Construction of artificial membranes and devices is intimately linked with careful mathematical modeling so as to predict the performance and improve the design. As a result this book is organized into three interrelated parts:

    Part I gives an overview of the book along with an elementary primer on biochemistry for engineers and applied mathematicians.

    Part II deals precisely with engineering and building artificial membranes, building novel synthetic biological devices out of these artificial membranes, and evaluating how such devices perform in experimental and clinical studies. The devices studied include a super-resolution biosensor (which can be viewed as a fully operational nanomachine), an electroporation platform (for studying how membranes spontaneously form waterfilled pores), and an electrophysiological platform (for noninvasive measurements of cells).

  • 8 - Reaction-Rate-Constrained Models for Engineered Membranes

  • from Part III - Dynamic Models for Artificial Membranes: From Atoms to Device
  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp 139-158

  • Summary

    Introduction

    Part II gave a detailed description of fabricating synthetic biological devices using engineered membranes and also discussed methods to perform experimental measurements. Constructing mathematical models that accurately predict the behavior of engineered membranes is important, both for the design of devices built out of the membrane and for estimating parameters such as analyte concentration and membrane conductance. This chapter focuses on constructing reaction-rate models for engineered membranes. Such models comprise a system of nonlinear ordinary differential equations and can be viewed as a black-box phenomenological model for the underlying synthetic biological device. Reaction-rate models account for the dynamics of tethered membranes that are excited by voltages less than 50 mV; therefore, the process of electroporation is not present. Specifically these models in combination with experimental measurements can be used to estimate the structural integrity of the tethered membrane and the disulfide bonds that anchor the tethers to the gold bioelectronic interface.

    When an electrical potential is established across a tethered membrane, the macroscopic reaction-rate model for the current flowing through the membrane is equivalent to a lumped electrical circuit composed of resistors and capacitors as shown in Figure 8.1. In other words, the simple resistor–capacitor (RC) circuit of Figure 8.1 models the electrical response of the engineered membrane at a macroscopic level. The resulting current voltage response is a second-order linear system of ordinary differential equations. Though widely used to study the dynamics of tethered membranes, this reaction-rate model cannot account for the restricted diffusion dynamics that occur at the electrode surface. Modeling these effects requires introducing fractional-order operators. The fractional-order macroscopic model of the tethered membrane accounts for both the charging dynamics of the membrane and the restricted diffusion dynamics at the electrode surface. This chapter also presents a fractional-order macroscopic model for tethered membranes that contain sterol molecules (e.g., cholesterol). All the reaction-rate models in this chapter are validated using real-world experimental data from engineered membranes with different tether densities, lipid compositions, and analyte solutions.

    Remark: Digital data acquisition, i.e., how the measured current is time sampled and digitized for processing on a computer, is not described in this book. Excellent resources for the design of analog-to-digital converters, time discretization, and antialiasing filters are [52, 102].

  • Appendix C - Experimental Setup and Numerical Methods

  • from Appendices
  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp 412-420

  • Summary

    This section details the precise ambient conditions and hardware specifications under which all experiments reported in this book were conducted. §C.1 provides the parameters for the ion-channel switch (ICS) biosensor experiments. §C.2 provides the experimental parameters estimated for the pore formation measurement platform (PFMP) for the dynamics of peptidyl-glycine-leucine-carboxyamide (PGLa) pore formation. §C.3 provides the experimental parameters of the electroporation measurement platform (EMP) model. Finally, §C.4 through §C.5.1 discuss coarse-grained molecular dynamics (CGMD) and molecular dynamics (MD) simulation parameters.

    Channel Switch Biosensor

    All experimental measurements, unless otherwise stated, were conducted at 27◦C in a phosphate-buffered solution with a pH of 7.2, and a 0.15Msaline solution composed of Na+, K+, and Cl−. At this temperature the tethered membrane is in the liquid phase. A pH of 7.2 was selected to match that typically found in the cellular cytosol of real cells. The forward and reverse reaction rates in Table C.1 are obtained from [220, 221, 283]. In Table C.1, notice that the computed diffusion coefficient for ferritin is 80 μm2/s and that for hCG is 250 μm2/s; this is an expected result because the ferritin has a molecular weight of 450 kDa and hCG a molecular weight of 25.7 kDa.

    The governing equations (10.10), (10.12), (10.13), and (10.15) with the boundary conditions (10.11) are solved numerically with the commercially available finiteelement solver COMSOL 4.3a (Comsol Multiphysics, Burlington, MA). To solve the advection-diffusion equations (10.10) and (10.15) the COMSOL module Transport of Diluted Species is used. The surface reaction diffusion equation (10.13) is solved using the Weak Form Boundary PDE module. The simulation domain is meshed with approximately 46,000 triangular elements constructed using an advancing-front meshing algorithm. The generalized Poisson–Nernst–Planck and Poisson–Nernst–Planck are numerically solved using the multifrontal massively parallel sparse direct solver [14] with a variable-order variable-step-size backward-differential formula [53]. The Levenberg– Marquardt algorithm (10.17) is implemented using the MATLAB function lsqnonlin with (10.16) computed from the results of the COMSOL simulations.

  • 10 - Diffusion-Constrained Continuum Models of Engineered Membranes

  • from Part III - Dynamic Models for Artificial Membranes: From Atoms to Device
  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp 174-211

  • Summary

    Introduction

    In this chapter (and the following three chapters) we study mesoscopic mathematical models for the dynamics of engineered membranes. This chapter constructs mesoscopic models for the following two synthetic biological devices built out of artificial membranes:

  • (i) the ion-channel switch (ICS) biosensor and

  • (ii) the pore formation measurement platform (PFMP).

    • Mesoscopic models deal with physical phenomena at the micrometer length scale and millisecond time scale – their level of abstraction lies between the macroscopic reactionrate models and the microscopic atomistic models. To put this chapter into perspective, recall that Chapter 4 dealt with the construction of engineered membrane devices. Also, Chapters 8 and 9 constructed macroscopic reaction-rate models of these devices which comprised ordinary and fractional-order differential equations. At the end of this chapter we illustrate how approximations of the continuum models can be used to construct the parameters of the macroscopic models in Chapters 8 and 9.

    As discussed in Chapter 9 the ICS biosensor operates in either the mass-transportinfluenced kinetics or the reaction-rate-limited kinetics regime. A schematic of the ICS biosensor and PFMP in the flow chamber is provided in Figure 10.1. If the analyte concentration and/or fluid flow velocity is sufficiently large, the advection-diffusion dynamics of the analyte can be neglected and only the macroscopic surface reaction dynamics govern the impedance response of the ICS and PFMP. These reaction-rate-limited kinetics models were the focus of Chapter 9. In this chapter we consider the masstransport- influenced kinetics regime, where the advection-diffusion dynamics cannot be neglected in the fluid flow chamber. Therefore, the dynamic mesoscopic models involve diffusion-type partial differential equations (PDEs) with boundary conditions determined by reaction-rate-type ordinary differential equations (ODEs).

    Figure 10.1 illustrates the mesoscopic model which couples the electrolyte dynamics with the surface reactions. The electrolyte dynamics involve the advection-diffusion of molecules and ions, and may also include electrodiffusion effects if the molecules and ions are charged. The study of the electrodiffusion of charged particles such as molecules and ions in the presence of an applied external electric field is of great importance in a number of disciplines. For example, in semiconductors these mesoscopic models describe the dynamics of electrons and holes and are used for the design of modern electronic components such as transistors, diodes, and infrared lasers [80].

  • 9 - Reaction-Rate-Constrained Models for the ICS Biosensor

  • from Part III - Dynamic Models for Artificial Membranes: From Atoms to Device
  • William Hoiles, University of British Columbia, Vancouver, Vikram Krishnamurthy, Cornell University, New York, Bruce Cornell, University of Technology Sydney
  • Book:
    Dynamics of Engineered Artificial Membranes and Biosensors
    Published online:
    25 May 2018
    Print publication:
    03 May 2018, pp 159-173

  • Summary

    Introduction

    Recall that the ion-channel switch (ICS) biosensor is a synthetic biological device built out of an artificial membrane, ion channels, spacers, and tethers. Its purpose is to detect the presence of analyte molecules in fluid. When it senses the presence of analyte molecules, the biosensor responds by changing its electrical impedance significantly. The amount the impedance changes by depends on the concentration of the analyte molecules in the fluid. Therefore, by examining the current flowing through the biosensor, one can detect both the presence of analyte and its concentration.

    This chapter studies how the current flowing through the ICS biosensor can be modeled using a two–time scale nonlinear system of ordinary differential equations (for chemical reactions) coupled with the fractional-order macroscopic model of Chapter 8 (for electrical dynamics). The fractional-order macroscopic model of the tethered membrane accounts for both the electrical dynamics of the membrane and the restricted diffusion dynamics at the electrode surface (as detailed in Chapter 8), while the two–time scale nonlinear system of differential equations models the chemical reaction dynamics that occur on the membrane surface of the ICS bisensor. We call the resulting system a reaction-rate-constrained model. Such reaction-rate models are useful for modeling the dynamics of tethered membranes that are excited by potentials less than 50 mV (so that electroporation does not occur).

    After developing reaction-rate-constrained models for the ICS biosensor (with μm2 electrode size), we consider biosensors with very small electrodes (area on the order of 1 × 10−12 m2 = μm2).We construct a stochastic model for the current response of such microelectrode ICS (mICS) biosensors as a noisy finite-state Markov chain observed in noise, i.e., a hidden Markov model (HMM), where each state is associated with the number of conducting gramicidin A (gA) dimers.When the electrode size is very small, individual dimer events are recorded – which is a finite-state process – as opposed to the ensemble average of millions of such events in a large electrode, which gives an ordinary differential equation.

    The typical current response of the ICS biosensor and mICS biosensor is illustrated schematically in Figure 9.1. All the models in this chapter are validated using realworld experimental data from engineered membranes with different tether densities, lipid compositions, and analyte solutions.

Page 10 of 76