Biomedical Engineering Reference
In-Depth Information
out that modeling often leads to deeper understanding. However, there was no lack of
effort in mathematical modeling regarding the fast photoelectric effect. Rather, the prob-
lem was a plethora of ad hoc models generated by means of what is known as exponen-
tial analysis, that is, using computer-based curve fitting to generate a mathematical
formula to account for the kinetics of measured electrical responses. As was discussed in
detail elsewhere (e.g., Section 20 of [29]), exponential analysis alone imposes no constraint
on the mathematical model so derived, and is, therefore, not experimentally
falsifiable
. As
explained lucidly by science philosopher Popper [119], a scientific theory or model must
be experimentally falsifiable. This puts models or theories generated by means of expo-
nential analysis in the same league with the “theory of intelligent design,” which was pro-
posed by its proponents to be a rival theory of evolution for the consumption of public
school children in the United States. The work of Yao et al. [50] is a case in point.
Nevertheless, in a subsequent publication, Yao et al. [51] did make a scientific statement:
differential responsivity is not an intrinsic property of bR, but rather the consequence of
AC-coupling of the measuring circuit. Evidence to be invoked to falsify the latter state-
ment abounds: No AC-coupling was involved in the measurement of Figure 15.12B,
Figure 15.15A, Figure 15.15B, and Figure 15.21. Additional evidence can also be found in
[120]. From the point of view of photoelectric sensor designs, exponential analysis offers
no design aid because it cannot predict data other than the very set, on which the analysis
has been conducted. In Gauch's words [121,122], models based on the exponential analy-
sis offer only
postdictions
but no
predictions
.
We have placed a great deal of emphasis on the materials science aspect of biosensor
technology. However, the search for suitable biomaterials for sensor construction is not the
only reason to launch a biological approach. I believe reverse-engineering Nature is a
viable approach. In spite of misgivings about reverse engineering, raised by investigators
from time to time in the past, imitating Nature at the level of fundamental principles,
rather than superficial and “verbatim” imitation, could still yield handsome dividends
[123]. There are obvious advantages in utilizing biomaterials for sensing purposes. The
mere incorporation of biomaterials in the sensor construction vastly increases the variety
and the specificity of sensors, since we have the resource of a vast repertoire of biomolec-
ular recognition at our command—the myriad of signal transduction processes involves
molecular recognition. However, advances made in digital computer technology have
already ushered in an unprecedented trend of information explosion. Merely increasing
the variety and the degree of specificity of sensors would probably not make human users
more informed. Rather, these improvements may further exacerbate information explo-
sion. Human users have already become overwhelmed with a huge amount of undigested
raw information, as the U.S. intelligence community had learned the hard way in the after-
math of the 9/11 attacks. A popular technique of electronic eavesdropping depends
largely on matching of keywords. Contrary to popular belief, keyword matching, in which
digital computers excel, does not always lead to understanding of what raw information
can provide, as astute Internet users know how to paraphrase common keywords so as to
evade cyber-surveillance. Even the so-called semantic web was of limited help [124]. A
reverse-engineering approach to understand how the human brain comprehends raw
information is therefore desirable. This brings us to the field of biocomputing research (for
reviews, see [125-128]).
A digital computer is often compared with the human brain, yet there are significant
differences in terms of information processing [129]. Computer scientist Conrad [125]
previously pointed out that the predominant feature of biological information processing is
“shape-based” computing. In contrast, digital computing is switch-based, since the latter
recognizes only “0” and “1.” Conrad further surmised that the “computer” architecture of
the human brain is hierarchical, and it involves two levels of information processing: a
