The document summarizes research on determining the initial population and growth constant of multiple non-interacting microorganism groups from total population time series data. It describes using the logistic growth equation to model microorganism population over time based on parameters like initial population, growth rate and maximum population. Methods are presented to calculate the growth rate and initial population for a single group from population data over time. However, the document notes it is difficult to determine parameters for multiple interacting populations uniquely due to non-unique total population solutions.
Determining Growth Rates of Microorganism Populations
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Determining Initial Population and Growth
Constant of Multi-group Non-interacting
Microorganism from Total Population Time
Series: Inversion of Artificial Data
Sparisoma Viridi1
, Souvia Rahimah2
1
FMIPA, Institut Teknologi Bandung, Bandung 40132, Indonesia
2
FTP, Universitas Padjadjaran, Sumedang 45363, Indonesia
1
dudung@fi.itb.ac.id, 2
souvia@unpad.ac.id
20190627_3
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Outline
• Introduction
• (Numerical) solution
• System
• Results and discussion
• Summaries
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Introduction
4. Logistic equation
• General form of equation
,
where Nmax is maximum population number,
k is growth rate, and α, β, γ > 0
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γβ
α
−=
max
1
N
N
kN
dt
dN
A. Tsoularis, J. Wallace, "Analysis of logistic growth models", Mathematical Biosciences [Math. Biosci.], vol. 179, no. 1,
pp. 21-55, Jul–Aug 2002, url https://doi.org/10.1016/S0025-5564(02)00096-2
5. Logistic eqn.: Well-known form
• With α, β, γ = 1, previous equation becomes
,
which is more well-known
• It is characterized by k, N0, and Nmax
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−=
max
1
N
N
kN
dt
dN
.
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(Numerical) solution
7. (Numerical) solution
• Using finite difference method with forward
scheme, the solution is
• t = 0 → N(0) = N0
• t = ∞ → N(∞) = Nmax
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( ) ( ) ( ) ( ) t
N
tN
tkNtNttN ∆
−+=∆+
max
1
8. Influence of k
• N0 = 20, Nmax = 100, k = 2, 1, 0, -1
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.
0
20
40
60
80
100
120
140
0 1 2 3 4 5 6 7 8 9 10
N
t
S1 S2 S3 S4
9. Influence of Nmax
• N0 = 1, k = 1, Nmax = 100, 80, 60, 40
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.
0
20
40
60
80
100
120
140
0 1 2 3 4 5 6 7 8 9 10
N
t
S1 S2 S3 S4
10. Influence of N0
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.
• Nmax = 100, k = 1, N0 = 140, 100, 400, 1
0
20
40
60
80
100
120
140
0 1 2 3 4 5 6 7 8 9 10
N
t
S1 S2 S3 S4
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System
12. System
• Population of microorganism in an isolated
environment
• Initial population number N0 is known
• Growth rate k is not known
• Population number Nn is known at certain time
t = tn
• There are might more than one kind of
microorganism
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13. Growth range
• Ranges: Exponential, linear, saturated
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N = 34.70t- 67.98
R² = 0.999
N = 5.183e0.687t
R² = 0.998
N = 180 - 6454.e-0.82t
R² = 0.998
0
40
80
120
160
200
0 1 2 3 4 5 6 7 8 9 10
N
t
14. Relation with logistic equation
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−=
max
1
N
N
kN
dt
dN
tkA
eNN 0=
tk
C
c
eNNN −
−= max
tkNN BB +=
Exponential
Linear
Saturated
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Results and discussion
16. Use in getting parameters
• Growth range must be determined carefully
• -> Range A: at least two observation points
• -> Range B: at least two observation points
• -> Range C: at least three observation points
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17. Range A
• At initial observation time t = 0, N = N0
• At a certain time t1,
• It can be found that the growth rate is
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1
01
tkA
eNN =
=
0
1
1
ln
1
N
N
t
kA
18. Range B
• At initial observation time t = t1, N1= NB + kBt1
• At a certain time t2,
• It can be found that the growth rate is
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22 tkNN BB +=
12
12
tt
NN
kB
−
−
=
19. Range C
• At initial observation time t = t2,
• At a certain time t3,
• Then
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23 32 tk
C
tk CC
e
N
NN
e −−
−
−
=
2
max2
tk
C
C
eNNN −
−=
3
max3
tk
C
C
eNNN −
−=
20. Range C (cont.)
• By setting t2 = 0 and t3 = t3 – t2 (*)
* This can used also in previous range
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−−
=
C
C
C
NNN
N
t
k
323
ln
1
21. Initial population
• Using calculated growth k in each range, initial
population in each range N0 can be calculated
using appropriate equation (slide 14)
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22. Problem
• What if there are more than one type of
popu-lation with different initial population
number and also growth rate? Can this
problem be solved with this method?
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23. Not-so-unique
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0
200
400
600
800
1000
1200
0 2 4 6 8 10 12 14 16 18 20
N
t
S1 S2 S3 S4 SS
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Summaries
25. Summaries
• Growth rate of microorganism population can
be calculated using two observations points in
exponential, linear, and saturated ranges
• Calculation is simpler when at each range the
time is translate to 0
• There is still no fine solution to get the para-
meters for multi-group microorganism popu-
lation, due to not-so-unique total population
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26. Acknowledgements
• This research is supported by a research grant
in 2019
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https://osf.io/nse6q/
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Thank you