Biomedical Engineering Reference
In-Depth Information
1.1.1.3 Signal Transduction Mechanisms and Biosensor Output
A number of general physicochemical classes of signal transduction mechanisms have
been used to create biosensors. The most commonly used classes are optical, electrochem-
ical, and piezoelectric. Thermometric and magnetic mechanisms have also been used.
Microelectromechanical mechanisms and integrated systems for liquid handling leading
to preprocessing of samples as well as their subsequent analysis represent a burgeoning
area of investigation for microscale analytical chemistry as well as biosensor development.
Each of these signal transduction mechanism classes have different advantages, disad-
vantages, and limiting sensitivities, some aspects of which we describe in the sections that
follow. At the Center for Intelligent Biomaterials, we have utilized the three most common
signal transduction mechanisms in the development of biosensors and we select some of
them for discussion in this review. In the generic biosensor, once an analyte at a specific
concentration has been detected by an array of the biological elements, the physicochem-
ical signal is transduced by the platform's mechanism to create an output for the end user.
However, in principle, more complex biosensors can be designed involving nonlinear
systems where inputs can be obtained from multiple channels. In these cases, there is the
need for further 'intelligent' analysis involving statistical or algorithmic processing of the
biosensor input to make accurate interpretations and provide clear quantitative output to
the end user of what is being sensed. Therefore, we present a discussion of the importance
of informatics and data mining approaches later in this review. Our purpose is twofold.
First, we highlight the importance of this capability for smart biosensor data processing in
some cases prior to signal output. Second, we indicate that these approaches can aid in the
original biosensor design process. This can be carried out through the analysis of large
complex data sets to understand and possibly predict the intelligent properties of biolog-
ical elements for potential integration into the biosensor.
1.1.2
Intelligent Properties of Biological Macromolecules and Systems
Systems comprising biological macromolecules, assemblies of these molecules, living
cells, and certainly entire organisms possess some or all of what can be termed intelligent
properties. In fact, individual cells and certainly whole organisms represent paradigms of
systems possessing intelligent properties. These properties include
template-based self-
assembly, self-multiplication, self-repair, self-degradation (selective), redundancy, self-diagnosis,
learning, and prediction/notification.
While single biological macromolecules or small
assemblies of them might contain limited subsets of these properties, living cells, and
entire organisms possess various manifestations of all of them. Intelligent properties incor-
porated into smart biosensors ideally would enable the biosensors to be responsive in real
time to their environment and be capable of integrating multiple functions such as recog-
nition/discrimination, feedback, standby, appropriate response, to name a few.
That we focus on living cells and their constituent macromolecules in the Center for
Intelligent Biomaterials is due to the following important fact. Evolution, acting over
nearly 1.5 billion years time, has achieved highly sophisticated levels of hierarchical
organization and complex integrated function in the biochemical subassemblies found in
cells, comprising complexes of DNA, RNA, proteins, carbohydrates, and lipid membranes
(5). Despite decades of design attempts by synthetic chemists and materials scientists to
duplicate these systems' properties using biomimetic approaches, the level of organization
and complex integrated functions found in cells remains unsurpassed. These facts have
helped define our research direction, which has been to identify important and appropri-
ate biological macromolecular systems for study, modification, and design into biosensors
and biomaterials. In the following section, we describe the experiments our Center has
