Biotechnology, Life Sciences and
Control Engineering – Some
Synthesis Issues
Aidan O’Dwyer
School of Control Systems and Electrical Engineering,
Dublin Institute of Technology, Ireland
aidan.odwyer@dit.ie
Colloquium on Control in Systems Biology, Sheffield, March 2007
1
Structure of Presentation
Part A: An outline survey of the synergistic work
between biotechnology, life sciences and control
engineering.
Part B: Examples of control engineering concepts
applied to biological applications, in two sections.
Section 1 outlines how a model of the pupil reflex of the
human eye to light may be estimated from an appropriate
number of data points.
Section 2 reports on an innovative undergraduate experiment,
developed by the author, which estimates a model of a
persons’ eye-brain-hand motor response.
2
Part A: An outline survey of the synergistic work between
biotechnology, life sciences and control engineering.
• There is an increasing interest in life science related
areas in the electrical and control engineering
community.
• One manifestation of this interest is the development
of interdisciplinary programmes, such as the B.Sc. in
Medical Physics and Bioengineering, offered by the
Dublin Institute of Technology from September 2005.
• A significant amount of research work in control
engineering has focused on aspects of human
physiological system, for example blood glucose
regulation, cardiovascular modelling and control, and
limb control.
3
• Engineering approaches in the study of biological sciences
are thus well explored.
• However, “we are in the midst of revolutionary
developments in biological sciences”, with “new systemslevel knowledge being urgently required” (Sontag, E.D.
(2004). Syst. Biol., 1(1), 9-18). In one example, an
influential U.S. “future trends” report has signalled, for
example, the importance of the role of control theory in
understanding the working of cell networks (in molecular
biology).
• In recognition of this, the Institute of
Electrical Engineers (IEE) has starting
publishing, from June 2004, a new journal
entitled IEE Proceedings on Systems
Biology, which aims to study intra- and
inter-cellular dynamics, using systems and
4
signals oriented approaches.
Systems biology: other strands of activity
• Interestingly, examples have been reported over thirty
years ago e.g. control of enzyme activity (As reported in
Fleming, D.G. and Feinberg, B.N. (1976). “CRC Handbook of
Engineering in Medicine and Biology”, CRC Press, pages 2730).
• Recent conferences on the topic have included the
IFAC Conference on the Foundations of Systems
Biology in Engineering (www.fosbe.org), California,
August 2005; to be held again in Germany, September
2007.
• More widely, the control of biotechnological processes
was one of the major themes of the 2005 IFAC World
Congress in Automatic Control.
5
National Institute of Bioprocessing
Research and Training (Ireland)
• http://www.nibrt.ie
• NIBRT is a recently established institute providing training
and research for the Bioprocessing Industry in Ireland.
• NIBRT supports fundamental, applied and industrial research
in
Cell Biology & Product Expression Systems.
Protein Stimulation & Biopharmaceutical Design.
Bioreaction Engineering.
Downstream Processing.
Process & Product Analytics - Automation.
6
Part B: Control engineering concepts
applied to biological applications.
Section 1: Model of the pupil reflex of the
human eye to light.
[From: http://www.physics.brocku.ca/www/faculty/sternin/120/images/f26030.jpg]
7
Pupil size in darkness
Dazzled
Full vision: pupil contracted
Images: http://www.physics.brocku.ca/www/faculty/sternin/120/images/f26030.jpg,
http://www.opt.pacificu.edu/journal/Articles/citek_dre/CITEKDRE.HTM,
http://www.meddean.luc.edu/lumen/MedEd/medicine/pulmonar/phydx/s10a.jpg
8
The pupil reflex to light may be described as a
closed loop control system
Pupil
neuromuscular
apparatus
How can a transfer function model be obtained for the
pupil neuromuscular apparatus?
1. Step testing in open loop:
….. difficult to perform experimentally
2. Frequency domain testing in open loop:
Reference: Stark, L. and Sherman, P. (1957). “A
servoanalytic study of consensual pupil reflex to light”,
Journal of Neurophysiology, 20, 17-26.
9
Pupil
neuromuscular
apparatus
• The disturbance inputs were sine wave variations in light
intensity onto the retina, at a variety of frequencies.
• The pupil area (also a sine wave variation) was measured
continuously by reflecting an infra-red light from the iris to
the photocell.
• The magnitude and phase of pupil area relative to light
intensity was determined.
• Open loop frequency response data determined:
f
0.14
0.7
0.9
1.3
1.34
2.1
2.3
2.4
2.7
3.0
3.4
M
0.16
0.15
0.12
0.13
0.11
0.06
0.07
0.05
0.05
0.02
0.02
P
-60
-90
-140 -190 -190 -320 -380 -420 -470 -510 -530
10
Results
• Four models can be developed (using graphical
methods:
0.16e0.42s
G m (s) 
1  0.2s
0.18e 0.38s
G m (s) 
(1  0.133s) 2
0.16e 0.18s
G m (s) 
(1  0.1s) 3
0.16e 0.38s
G m (s) 
(1  0.1s)1  0.18s 
Stark and Sherman, 1957
“Analytical” model developed:
0.16e 0.39 s
Gm 
1  0.17s
“Gradient” model developed:
0.13e 0.38s
Gm 
1  0.13s
11
Model validation
Bode plot of process and models
Process
Graphical method
Gradient model ( )
Analytical model (*)
Stark and Sherman
12
Other interesting data sets
• Human motor response of patients with Parkinson’s syndrome
13
• Hand control system
Reference: Stark, L. (1968). “Neurological control systems: studies in
bioengineering”, Plenum Press, New York.
14
Section 2: Eye-brain-hand motor response
This section reports on the development of an innovative
undergraduate experiment which estimates a model of a
persons’ eye-brain-hand motor response.
In the experiment the person is successively asked to
track, with a mouse, ten sine wave signals at different
frequencies on a computer screen.
Based on the average of the data recorded, the persons
eye-brain-hand motor response in the frequency domain
is recorded (and may be summarized on a Bode plot).
Subsequently, the parameters of a single-input, singleoutput (SISO) process model may be determined.
15
A typical example of one sine wave input signal,
and a person’s tracking attempt, is shown.
16
Procedure
A computer with a data acquisition card and suitable
software is used to record the input sine wave
(generated in SIMULINK) and the persons’ tracking
attempt. Implementation:
17
The peak-to-peak amplitude and phase shift of
the output was recorded for each frequency.
 tp
c
ac
ay
ys
Time
Amplitude ratio,
AR 
ay
ac
Phase,

 t p
2
 360º
18
Based on an average of the amplitudes and phase differences
recorded, a Bode plot is drawn from the data. The figure
shows the Bode plot of all the data gathered.
Bode plot of all non-leading data gathered from eye-brain-hand experiment
Magnitude (dB)
4
2
0
-2
-4
0
Phase (deg)
-45
blue = right hand data, AOD, May 99
green = left hand data, AOD, May 99
black = right hand data, AOD, Jan 06
red = left hand data, AOD, Jan 06
cyan = data, students, May 99 onwards
magenta = data, older group, May 99
black line = average of all the data
-90
-135
-180
-225
-1
10
0
10
Frequency (rad/sec)
1
10
19
Model determination
• A model may be determined directly from the Bode
plot, or using an analytical technique.
• It is clear from the figure that subjects tend to be able
to follow the amplitude of the sine wave accurately.
Therefore, on average, the gain, K m , of the model is
approximately 1.0.
• The phase varies with frequency in an approximately
linear manner. A time delay (reaction time),  m , may
be fitted to this data. The average eye-brain-hand
motor response reaction time is 0.25 seconds.
20
Some experimental results
Bode plot of authors’ response (age 36): right hand
21
Model identified: K m  1.08 , m  0.24 sec s
Validation of model and original data:
Experiment
Model
22
Youth versus Middle Age …
Bode plot - dominant and non-dominant hand data (exc. leading) - older, younger
Magnitude (dB)
4
2
0
-2
-4
0
Phase (deg)
-45
-90
blue = dominant hand, older (and avg)
green = dominant hand, younger (and avg.)
magenta = non-dominant hand, older (and avg.)
red = non-dominant hand, younger (and avg.)
-135
-180
-225
-1
10
0
10
Frequency (rad/sec)
1
10
23
Summary of results obtained
Condition
Delay determined
Average of all data gathered
0.25 s
Average, dominant hand data, students, May 99 - Jan 06
0.14 s
Average, dominant hand data, 22 year old male, May 99
0.16 s
Average, dominant hand data, 36 year old male, May 99
0.20 s
Average, dominant hand data, 42 year old male, Jan 06
0.32 s
Average, non-dominant hand data, students, May 99 - Jan 06
0.20 s
Average, non-dominant hand data, 22 year old male, May 99
0.19 s
Average, non-dominant hand data, 42 year old male, Jan 06
0.41 s
24
Pedagogical Issues - 1
• Since its development in 1999, this experiment has been
carried out by students taking a control engineering
option in the programmes in electrical/electronic
engineering at DIT.
• The experiment does not require strong mathematical
foundations.
• The author has found that students are enthusiastic about
the experiment and frequently spend over the allocated
time on aspects of it.
25
Pedagogical Issues - 2
Formal Student Feedback 2005-6
Please answer the following questions. To answer each question, please write a
number between 1 and 5, with 5 - strongly agree, 4 – agree, 3 – unsure, 2 –
disagree 1 – strongly disagree
1. The work was a beneficial learning experience (compared to other exercises)
– Average score: 4.3
2. The work is user-friendly – Average score: 4.2
3. The work complements and enhances my understanding of lecture material
– Average score: 4.5
4. The work is fun and sustained my interest – Average score: 4.5
5. I became more interested in the material because of this work
– Average score: 4.0
6. There is enough time to perform the work – Average score: 3.0
7. I would recommend this work to others – Average score: 4.3
26
Pedagogical Issues - 3
Student feedback is very positive; the reasons for
this, in my opinion, are:
• The experiment provides direct feedback to the user on the
PC screen.
• The experiment is not excessively time-consuming; a
typical experiment time to gather one set of data, at 10
frequencies, is 10 minutes.
• A competitive edge among (typically, male) students is
frequently observed, with a desire to have the shortest
reaction time.
• A motivational aspect for some students is the application
of the idea in biomedical engineering, possibly in the
diagnosis of some motor response disorders.
27
Conclusions
• The contribution briefly outlines the synergistic work that is
occurring between biotechnology, life sciences and control
engineering.
• Control engineering concepts may be applied readily to biological
applications. Two examples of transfer function modeling of such
applications are described.
• The second application described has been used by the author in
the undergraduate control engineering laboratory for a number of
years. The experiment does not require strong mathematical
foundations. Student feedback is positive.
• In the wider perspective (in Ireland), the recent establishment of
the (Irish) National Institute of Bioprocessing Research and
Training (http://www.nibrt.ie/), whose mission includes training
and research for the biotechnology industry, is facilitating further
synergies.
28