- Email: [email protected]
Use of spreadsheets in biotechnology
TIBTECH - NOVEMBER 1987 [Vol. 5]
smaller local industries. This sort of approach requires organization and it requires incentives. In Britain, there has already been some discussion of compiling a register of companies which would be willing to aid school research in biotechnology and of providing a mechanism by which interested schools could make appropriate industrial contacts. Irena Olejnicowa of the Polytechnic of the South Bank in London has said that she would be willing to head such a project if funds became available. []
[]
[]
Perhaps organization at a local level might be most appropriate. Until the advantages of schoolbased biotechnology research become apparent, financial incentives will be necessary. Several years ago, the UK government made considerable sums available to enable schools to purchase microcomputers for classroom, use. Similar sums might serve as suitable incentives for the initiation of industry-school collaborative ventures. Offers of the use of certain facilities or the donation of small amounts Of equip-
[]
[]
[]
[]
[]
[]
[]
[]
Use of spreadsheets in biotechnology tion of differentia] equations. Spreadsheets reduce the heavy work involved in recording measurements, calculating parameters using complex formulae, and writing reports. Spreadsheets succinctly handle any types of equations, allow the data to be directly introduced in them and produce graphs and printouts at the touch of a few keys. In biotechnological engineering, not only can spreadsheets be used to fit kinetic models to data, but also, can help the researcher to assess the --Fig.
]. Mata-Alvarez, P. Llabres-Luengo and P. Clapes are at the Departament d'Enginyeria Quimica i Metal]urgia, Universitat de Barcelona, C/Marti i Franques 1, 6t). 08028 Barcelona, Spain. © 1987, Elsevier Publications, Cambridge
0166- 9430/87/$02.00
[]
[]
What is a spreadsheet?
A spreadsheet is a table, arranged in rows and columns whose cells can contain numbers, labels, or formulas. Columns are usually denoted by letters, and rows by numbers: the 45th cell in the third column is denoted C45. Numerical data typed into cells are plainly visible and can be used in a formula by referring to the cell number. Cell content can be reviewed or changed at any time and, most importantly, after a change the entire table can be recalculated.
1 A
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
[]
relative significance of the different parameters appearing in any mathematical relationship. Models for chemical or biological operations, physical-chemical phenomena, and many others are equally appropriate for this treatment.
J. Mata-Alvarez, P. Llabres-Luengo and P. Clapes Spreadsheet programs, such as Lotus 1-2-3, are becoming increasingly popular. They have been traditionally used for business applications but with the wider availability of personal computers, their application has undoubtedly broadened. Spreadsheets provide a convenient way of archiving data, organizing medium size databases, and performing mathematical operations. They do not require any special programming expertise and, indeed, are very easy to use. Neat reports containing text, tables and graphs can be produced and, importantly, spreadsheets make quick work of mathematical model building and testing. The most advanced of them can perform iterated calculations and, through the macro facilities, can perform tasks which previously required programming in FORTRAN or BASIC. Applications of spreadsheet software ranges from the collection of data through the kinetic analysis of data by fitting to models the integra-
ment w o u l d be as welcome as cash. In my experience, academia is already willing to provide assistance; industry less so. If this all seems a little idealistic, please consider that youth is so. It is also keen, receptive and openminded. The source of tomorrow's ideas will benefit from early exposure to tomorrow's technology. Perhaps guided tours of breweries and fact sheets will become things of the past. Schools are ready now to do research. Is industry ready to do business?
B C D E F REGRESSION ON M I C H A E L I S MENTEN KINETIC EQUATION ................................................
Given
G
H
the
Michaelis-Menten equation, k Ce Ca ............ K + Ca This program finds the constants k a n d K, g i v e n a s e r i e s of s u b s t r a t e c o n c e n t r a t i o n Ca a s a f u n c t i o n of t i m e , and the initial enzyme concentration Ce. A linear regression is p e r f o r m e d on a modified f o r m of the Michaelis-Nenten integrated equation. T h e p r o g r a m d i s p l a y s a p l o t of the r e g r e s s i o n llne together with the experimental p o i n t s to g r a p h i c a l l y a s s e s s the g o o d n e s s of t h e fit. A S t u d e n t test is a l s o p e r f o r m e d at t h e r e q u i r e d p r o b a bility level. r=
To
begin
the
program
introducing
the
data,
press
ALT
D
Example of a spreadsheet program to estimate the Michaelis-Menten kinetic constants. Introductory screen.
TIBTECH - NOVEMBER 1987 [Vol. 5] --Fig. 2 R
Q
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
S DATA
Number O f p o i n t s . . P r o b a b i l i t y level Student test . . . . . . Initial substrate concentration .... Initial enzyme
T INTRODUCTION
U
4 95
Point number %
to
time
1 2 3 4 5 6 7 8 9 i0 ii 12 13 14 15
1
Press A L T R regression
W
Substr. concentr.
...........................
500
concentration....
V
perform
the
1 2 4 5
320 180 25 10
equation gives:
(Cao CA)
K In
[
To find the values and K through a this equation must give the following (C&-
--Fig. 3
In H
J
I
**********
K
L
REGRESSION
H RESULTS
11 12
error error error
of K . . . . . . . . . . . . . . . . . of k x C e . . . . . . . . . . . . of Y e s t i m a t e s . . . . . . .
16 17
T o d i s p l a y the r e g r e s s i o n line the experimental points, press
18 19
To
produce
a
results
printout
13.13 17.13 10.75 0.9978 21.0577 2.92
together
with
FIO
press
ALT
P
20
Regression analysis screen.
The spreadsheet also allows one to type labels and explanations where needed. Inputs and results can be arranged in any desired order, and the whole spreadsheet or any part of it can be easily printed. Graphs, also, can be generated and printed. In many instances, therefore, spreadsheets have definite advantages over programming. An application in bioengineering An example of the use of the spreadsheet is a regression program to fit the constants of a simple Michaelis-Menten model (Fig. 1):
t
(CAolCA)
- K + k CEo In
(3)
220.74 276.48 276.48
Correlation coeflcient r ............ t associated to r . . . . . . . . . . . . . . . . . . . S t u d e n t t, at . . . . . 95 % .......
13 14 15
CA)
(CAo/CA)-
*************
7
Standard Standard Standard
of the constants k linear regression, be manipulated to useful form:
N
Constant K .......................... Product k x Ce ...................... Constant k .........................
8 9 10
(CA°-- CA) = kCEo t (2)
Data introduction screen.
1 2 3 4 5 6
+
plug flow fermenter. For this system, integration of the Michaelis-Menten
Linear regression now allows the estimation of both k and K. Most spreadsheet programs have subroutine commands that enable the user to perform regressions on numerical data. Therefore, if the data are manipulated via the spreadsheet in a suitable format (for instance, that of Eqn 3), the regression results can be obtained easily and quickly. Additionally, the program we have used performs a Student test on the regression results. Once introduced the t-table, a function called HLOOKUP does the rest: that is, at the given probability level and taking into account the degrees of freedom of the data, it locates the t-value. This
--Fig. 4
EQUATION
LINEARIZED M - M
REGRESSION RESULTS 4-20 400
+
380 360 340 32O
\
8
300
v 9. \
280 260
r~
r
k CA CG -
K+CA
(1)
where CA is the substrate concentration at a given time, CEo is the initial enzyme concentration, and k and K are constants. After a single key stroke, the display shown in Fig. 2 appears, enabling data entry. The data on the relationship between substrate concentration (CA) and time (t), can be taken from a batch or a
I
240
8
220 200 180 160 140 120
+ I 1.2
, 1.4
I
,
,
1.6 t /
I 1.8
In ( C a o -
I
l 2
~
, 2.2
Co)
Graphical output of the spreadsheet, showing the regression line and the
experimental points.
TIBTECH
value can be compared with that from the regression to decide upon the adequacy of the model (Fig. 3). Using the Graph commands, a plot is generated showing the regression line and the experimental points. Thus the goodness of fit can be assessed by pressing a single key. If desired, a hardcopy of the graph can also be obtained (Fig. 4). The program performs all these steps automatically on request, and also clears the data ranges used. The automation is achieved through the use of macro commands, i.e. strings
of individual commands which can be executed using pairs of keystrokes (ALT plus a selected letter).
Conclusion In the last few years, spreadsheet applications have broadened appreciably because of the growth of the personal computer market. In biotechnology, spreadsheets can play an important role because of their power and flexibility. They provide a quick response to 'what if' questions, do not require any programming expertise and the commands are very easy to
Proteins and surface effects in fermentation: foam antifoam and mass transfer A. Prins and K. van't Riet. Foaming can be a serious problem in fermentation, particularly in large scale, highly loaded fermentations. Overflow and dangerous or inefficient use of the reactor are the main operating problems. Foaming is largely a result of the stabilization of the liquid foam films by proteins. Foaming can be reduced but this can also reduce mass transfer. Therefore, sensitive foam control methods are necessary. In this article, the m e c h a n i s m s behind foam and bubble stability will be examined. Foaming in fermentation has received little attention in the scientific literature compared with other engineering problems like mass transfer and mixing. This is surprising because foaming is one of the main operating problems of commercial fermentations 1-3. Foam can be defined as the dispersion of gas bubbles in a liquid. Usually a distinction is drawn between foam and gas hold up: in foam, A. Prins and K. van't Riet are at the Department of Food Science, Agricultural University; Biotechnion, De Dreijen 12, 6703 BC Wageningen, The Netherlands. © 1987, Elsevier Publications, Cambridge
the bubbles are polyhedral, forming a honeycomb structure and the gas/ liquid volume ratio is larger than one, and usually larger than 3: in hold-up, the gas/liquid volume ratio is much smaller and the bubbles are more spherical. Usually, the distinction is even simpler; gas hold-up is the bubbles in the broth and foam is the bubbles on top. Foaming is greatly influenced by proteins adsorbing to the gas liquid interface. A monolayer of proteins contains about i mg protein m - 2 . In a fermentation broth, the gas/liquid surface area is of the order of magnitude of 1000 m 2 m - 3 . Since
0166- 9430/87/$02.00
-
NOVEMBER 1987 [Vol. 5]
learn. The program we have used to illustrate their utility is merely an example of what can be done. The user of a spreadsheet can easily build up a library of multiple applications which, without doubt, will help him in organizing, interpreting, and filing his data. More complex applications will be handled in the near future, with newly available microprocessors such as the Intel 80386. This, and other advances, will facilitate the extension of this useful tool.
proteins adsorb at interfaces at very low concentrations, protein concentrations of as little as 1 mg 1-1 can influence foaming. In fermentations where extracellular proteins are the product of interest, protein concentration will obviously greatly exceed l m g 1 - 1 . But other fermentation broths, too, contain proteins: extracellular enzymes, medium components and cell lytic products. In principle, therefore, foaming can occur in every fermentatfon. Foaming has a number of consequences. A foaming broth can have better mass transfer characteristics (by a factor of two or more) than a similar broth containing an antifoam agent. Since this reduces the considerable power consumption for mass transfer, this effect is advantageous. However, foaming causes very serious operating problems. Consider, for instance, a large scale commercial fermenter working at a gas superficial velocity (see glossary) of 3 cm s -1. When the foam contains 30% by volume of liquid, without any destruction of foam, the foam level will rise at 3.9 cm s -1. For a large fermenter with a 0.5 m high headspace, there will only be 13 seconds before the foam overflows. Clearly, conditions producing fully stable foam are unacceptable in commercial fermentations. Mechanical foam destruction is an unsatisfactory option: the energy consumption is too large and mechanical systems are not wholly reliable. A chemical antifoam back up system is always needed. In practice, foam control should be subtle, allowing a certain amount of
smaller local industries. This sort of approach requires organization and it requires incentives. In Britain, there has already been some discussion of compiling a register of companies which would be willing to aid school research in biotechnology and of providing a mechanism by which interested schools could make appropriate industrial contacts. Irena Olejnicowa of the Polytechnic of the South Bank in London has said that she would be willing to head such a project if funds became available. []
[]
[]
Perhaps organization at a local level might be most appropriate. Until the advantages of schoolbased biotechnology research become apparent, financial incentives will be necessary. Several years ago, the UK government made considerable sums available to enable schools to purchase microcomputers for classroom, use. Similar sums might serve as suitable incentives for the initiation of industry-school collaborative ventures. Offers of the use of certain facilities or the donation of small amounts Of equip-
[]
[]
[]
[]
[]
[]
[]
[]
Use of spreadsheets in biotechnology tion of differentia] equations. Spreadsheets reduce the heavy work involved in recording measurements, calculating parameters using complex formulae, and writing reports. Spreadsheets succinctly handle any types of equations, allow the data to be directly introduced in them and produce graphs and printouts at the touch of a few keys. In biotechnological engineering, not only can spreadsheets be used to fit kinetic models to data, but also, can help the researcher to assess the --Fig.
]. Mata-Alvarez, P. Llabres-Luengo and P. Clapes are at the Departament d'Enginyeria Quimica i Metal]urgia, Universitat de Barcelona, C/Marti i Franques 1, 6t). 08028 Barcelona, Spain. © 1987, Elsevier Publications, Cambridge
0166- 9430/87/$02.00
[]
[]
What is a spreadsheet?
A spreadsheet is a table, arranged in rows and columns whose cells can contain numbers, labels, or formulas. Columns are usually denoted by letters, and rows by numbers: the 45th cell in the third column is denoted C45. Numerical data typed into cells are plainly visible and can be used in a formula by referring to the cell number. Cell content can be reviewed or changed at any time and, most importantly, after a change the entire table can be recalculated.
1 A
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
[]
relative significance of the different parameters appearing in any mathematical relationship. Models for chemical or biological operations, physical-chemical phenomena, and many others are equally appropriate for this treatment.
J. Mata-Alvarez, P. Llabres-Luengo and P. Clapes Spreadsheet programs, such as Lotus 1-2-3, are becoming increasingly popular. They have been traditionally used for business applications but with the wider availability of personal computers, their application has undoubtedly broadened. Spreadsheets provide a convenient way of archiving data, organizing medium size databases, and performing mathematical operations. They do not require any special programming expertise and, indeed, are very easy to use. Neat reports containing text, tables and graphs can be produced and, importantly, spreadsheets make quick work of mathematical model building and testing. The most advanced of them can perform iterated calculations and, through the macro facilities, can perform tasks which previously required programming in FORTRAN or BASIC. Applications of spreadsheet software ranges from the collection of data through the kinetic analysis of data by fitting to models the integra-
ment w o u l d be as welcome as cash. In my experience, academia is already willing to provide assistance; industry less so. If this all seems a little idealistic, please consider that youth is so. It is also keen, receptive and openminded. The source of tomorrow's ideas will benefit from early exposure to tomorrow's technology. Perhaps guided tours of breweries and fact sheets will become things of the past. Schools are ready now to do research. Is industry ready to do business?
B C D E F REGRESSION ON M I C H A E L I S MENTEN KINETIC EQUATION ................................................
Given
G
H
the
Michaelis-Menten equation, k Ce Ca ............ K + Ca This program finds the constants k a n d K, g i v e n a s e r i e s of s u b s t r a t e c o n c e n t r a t i o n Ca a s a f u n c t i o n of t i m e , and the initial enzyme concentration Ce. A linear regression is p e r f o r m e d on a modified f o r m of the Michaelis-Nenten integrated equation. T h e p r o g r a m d i s p l a y s a p l o t of the r e g r e s s i o n llne together with the experimental p o i n t s to g r a p h i c a l l y a s s e s s the g o o d n e s s of t h e fit. A S t u d e n t test is a l s o p e r f o r m e d at t h e r e q u i r e d p r o b a bility level. r=
To
begin
the
program
introducing
the
data,
press
ALT
D
Example of a spreadsheet program to estimate the Michaelis-Menten kinetic constants. Introductory screen.
TIBTECH - NOVEMBER 1987 [Vol. 5] --Fig. 2 R
Q
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
S DATA
Number O f p o i n t s . . P r o b a b i l i t y level Student test . . . . . . Initial substrate concentration .... Initial enzyme
T INTRODUCTION
U
4 95
Point number %
to
time
1 2 3 4 5 6 7 8 9 i0 ii 12 13 14 15
1
Press A L T R regression
W
Substr. concentr.
...........................
500
concentration....
V
perform
the
1 2 4 5
320 180 25 10
equation gives:
(Cao CA)
K In
[
To find the values and K through a this equation must give the following (C&-
--Fig. 3
In H
J
I
**********
K
L
REGRESSION
H RESULTS
11 12
error error error
of K . . . . . . . . . . . . . . . . . of k x C e . . . . . . . . . . . . of Y e s t i m a t e s . . . . . . .
16 17
T o d i s p l a y the r e g r e s s i o n line the experimental points, press
18 19
To
produce
a
results
printout
13.13 17.13 10.75 0.9978 21.0577 2.92
together
with
FIO
press
ALT
P
20
Regression analysis screen.
The spreadsheet also allows one to type labels and explanations where needed. Inputs and results can be arranged in any desired order, and the whole spreadsheet or any part of it can be easily printed. Graphs, also, can be generated and printed. In many instances, therefore, spreadsheets have definite advantages over programming. An application in bioengineering An example of the use of the spreadsheet is a regression program to fit the constants of a simple Michaelis-Menten model (Fig. 1):
t
(CAolCA)
- K + k CEo In
(3)
220.74 276.48 276.48
Correlation coeflcient r ............ t associated to r . . . . . . . . . . . . . . . . . . . S t u d e n t t, at . . . . . 95 % .......
13 14 15
CA)
(CAo/CA)-
*************
7
Standard Standard Standard
of the constants k linear regression, be manipulated to useful form:
N
Constant K .......................... Product k x Ce ...................... Constant k .........................
8 9 10
(CA°-- CA) = kCEo t (2)
Data introduction screen.
1 2 3 4 5 6
+
plug flow fermenter. For this system, integration of the Michaelis-Menten
Linear regression now allows the estimation of both k and K. Most spreadsheet programs have subroutine commands that enable the user to perform regressions on numerical data. Therefore, if the data are manipulated via the spreadsheet in a suitable format (for instance, that of Eqn 3), the regression results can be obtained easily and quickly. Additionally, the program we have used performs a Student test on the regression results. Once introduced the t-table, a function called HLOOKUP does the rest: that is, at the given probability level and taking into account the degrees of freedom of the data, it locates the t-value. This
--Fig. 4
EQUATION
LINEARIZED M - M
REGRESSION RESULTS 4-20 400
+
380 360 340 32O
\
8
300
v 9. \
280 260
r~
r
k CA CG -
K+CA
(1)
where CA is the substrate concentration at a given time, CEo is the initial enzyme concentration, and k and K are constants. After a single key stroke, the display shown in Fig. 2 appears, enabling data entry. The data on the relationship between substrate concentration (CA) and time (t), can be taken from a batch or a
I
240
8
220 200 180 160 140 120
+ I 1.2
, 1.4
I
,
,
1.6 t /
I 1.8
In ( C a o -
I
l 2
~
, 2.2
Co)
Graphical output of the spreadsheet, showing the regression line and the
experimental points.
TIBTECH
value can be compared with that from the regression to decide upon the adequacy of the model (Fig. 3). Using the Graph commands, a plot is generated showing the regression line and the experimental points. Thus the goodness of fit can be assessed by pressing a single key. If desired, a hardcopy of the graph can also be obtained (Fig. 4). The program performs all these steps automatically on request, and also clears the data ranges used. The automation is achieved through the use of macro commands, i.e. strings
of individual commands which can be executed using pairs of keystrokes (ALT plus a selected letter).
Conclusion In the last few years, spreadsheet applications have broadened appreciably because of the growth of the personal computer market. In biotechnology, spreadsheets can play an important role because of their power and flexibility. They provide a quick response to 'what if' questions, do not require any programming expertise and the commands are very easy to
Proteins and surface effects in fermentation: foam antifoam and mass transfer A. Prins and K. van't Riet. Foaming can be a serious problem in fermentation, particularly in large scale, highly loaded fermentations. Overflow and dangerous or inefficient use of the reactor are the main operating problems. Foaming is largely a result of the stabilization of the liquid foam films by proteins. Foaming can be reduced but this can also reduce mass transfer. Therefore, sensitive foam control methods are necessary. In this article, the m e c h a n i s m s behind foam and bubble stability will be examined. Foaming in fermentation has received little attention in the scientific literature compared with other engineering problems like mass transfer and mixing. This is surprising because foaming is one of the main operating problems of commercial fermentations 1-3. Foam can be defined as the dispersion of gas bubbles in a liquid. Usually a distinction is drawn between foam and gas hold up: in foam, A. Prins and K. van't Riet are at the Department of Food Science, Agricultural University; Biotechnion, De Dreijen 12, 6703 BC Wageningen, The Netherlands. © 1987, Elsevier Publications, Cambridge
the bubbles are polyhedral, forming a honeycomb structure and the gas/ liquid volume ratio is larger than one, and usually larger than 3: in hold-up, the gas/liquid volume ratio is much smaller and the bubbles are more spherical. Usually, the distinction is even simpler; gas hold-up is the bubbles in the broth and foam is the bubbles on top. Foaming is greatly influenced by proteins adsorbing to the gas liquid interface. A monolayer of proteins contains about i mg protein m - 2 . In a fermentation broth, the gas/liquid surface area is of the order of magnitude of 1000 m 2 m - 3 . Since
0166- 9430/87/$02.00
-
NOVEMBER 1987 [Vol. 5]
learn. The program we have used to illustrate their utility is merely an example of what can be done. The user of a spreadsheet can easily build up a library of multiple applications which, without doubt, will help him in organizing, interpreting, and filing his data. More complex applications will be handled in the near future, with newly available microprocessors such as the Intel 80386. This, and other advances, will facilitate the extension of this useful tool.
proteins adsorb at interfaces at very low concentrations, protein concentrations of as little as 1 mg 1-1 can influence foaming. In fermentations where extracellular proteins are the product of interest, protein concentration will obviously greatly exceed l m g 1 - 1 . But other fermentation broths, too, contain proteins: extracellular enzymes, medium components and cell lytic products. In principle, therefore, foaming can occur in every fermentatfon. Foaming has a number of consequences. A foaming broth can have better mass transfer characteristics (by a factor of two or more) than a similar broth containing an antifoam agent. Since this reduces the considerable power consumption for mass transfer, this effect is advantageous. However, foaming causes very serious operating problems. Consider, for instance, a large scale commercial fermenter working at a gas superficial velocity (see glossary) of 3 cm s -1. When the foam contains 30% by volume of liquid, without any destruction of foam, the foam level will rise at 3.9 cm s -1. For a large fermenter with a 0.5 m high headspace, there will only be 13 seconds before the foam overflows. Clearly, conditions producing fully stable foam are unacceptable in commercial fermentations. Mechanical foam destruction is an unsatisfactory option: the energy consumption is too large and mechanical systems are not wholly reliable. A chemical antifoam back up system is always needed. In practice, foam control should be subtle, allowing a certain amount of