lab note
7,14,2014
Lab member
Yoshikawa,Nakashima
Contents
Making Plate Culture
Ampicillin *10
Chloramphenicol*6
7,15,2014
Lab member
Yoshikawa,Nakashima
Contents
Making DW 100mL
TF
4-17F(A-1), 4-4E(A-2), 3-19O(A-3), 3-4G(A-4), 4-1N(A-6), 3-3F(A-8), 4-13L(B-10)
7,16,2014
Lab member
Yoshikawa,Nakashima
Contents
A-2:No colony
TF
A-2(recovery)
preculture
A-1, A-3, A-4, A-6, A-8, B-10,Escherichia coli JM109(for competent cell)
7,17,2014
Lab member
Yoshikawa,Nakashima
Contents
A-2:There are something like colonies.
Making glycerol stock/miniprep
A-1, A-3, A-4, A-6, A-8
All samples:consentration is low.
B-10:disposed
making reagent
LB: 500mL
50mM CaCl2: 400mL
50mM CaCl2/ 20% glycerol: 200mL
preculture
A-1 #1
A-2 #1 #2
A-3 #1
A-4 #1
A-6 #1
A-8 #1
B-10 #1 #2
E. coli JM109(for competent cell)
#:colony number
7,18,2014
Lab member
Yoshikawa,Nakashima
Contents
miniprep
A-1,A-3,A-6:from culture 2.5ml OK
A-4,A-8:from culture 1.5ml OK
A-2#1:made glycerol stock. Low consentration.
B-10#1:Low consentration.
A-2#2,B-10#2:did not grow
Making competent cell
100uL*120
making reagent
(2000×)Ampicillin 3mL (100mg/mL)
7,22,2014
Lab member
Yoshikawa,Nakashima
Contents
Cut check
A-4 (81ng/μL):ES Cut,XP cut.Checked in 1.5h, 2h, 2.5h, 3h:OK
Left:1kbp Ladder/A-4(Control)/ES 1.5h/ES 2h/ES 2.5h/ES 3h
Right:1kbp Ladder/A-4(Control)/XP 1.5h/XP 2h/XP 2.5h/XP 3h
(ここに写真20140722_1を貼付)
TF
We measured cfu of competent cell we made.
We used Efficiency Kit (RFP Construct on pSB1C3)in distribution Kit.
TF
: 50pg, 20pg, 10pg, 5pg
competent cell:25uL
Sprinkling:100uL
preculture
B-10 #3, A-4
7,23,2014
Lab member
Yoshikawa,Nakashima
Contents
plate check:Did not grow.
miniprep
A-4, B-10 #3(made glycerol stock):OK
Making plate
200ml,CP*10
7,28,2014
Lab member
Nakashima
Contents
digestion,gel extraction
A-6 SP, B-10 XP:disposed
100bp Ladder, A-6 SP, B-10 XP, NC
(ここに20140728_1を貼付)
(wrote at 0731
A-6
band near 2kbp:SP cut or single cut
band near 1.5kbp:Plasmid)
colony PCR
A-6 #1, B-10 #1
1kbp Ladder, A-6, B-10, NC
(ここに20140728_2を貼付)
A-6:OK,B-10:wrong
Measuring cfu
competent cell:50uL
7,29,2014
Lab member
Yoshikawa,Nakashima
Contents
Plate check:All Plates did not grow.
TF
B-10 by electroporation.
Measuring cfu
A-1:13,65,130pg
Electrophoresis check
1kbp Ladder, A-6(Plasmid), NC
(ここに20140729_1を貼付)
There is band in 1500kbp,so 1500kbp in 0728 may be rest of cutting.
No contamination in Dye.
7,30,2014
Lab member
Yoshikawa,Nakashima
Contents
colony check
cfu:Did not grow
B-10:One colony
Cut check
We checked whether A-6 band in 0728 was rest of cutting.
A-1 SP cut:checked on different times.
colony PCR
B-10(from Plate made in 0729)
1kbp Ladder, 1.5h, 2h, 2.5h, B-10(colony PCR), NC
(ここに20140730_1を貼付)
band near 800bp:RFP between Spe1 site and Pst1 site of A-1.
band near 2kbp:Backbone cut by SP cut
band near 3kbp:rest of cutting
1.5h:incompletely cut
2h,2.5h:OK
We decided 2h for digestion.
B-10:OK
preculture
B-10
7,31,2014
Lab member
Yoshikawa,Nakashima
Contents
miniprep
B-10 #1(from 140729 Plate)(made glycerol stock)
digestion,gel extraction
A-6 SP, B-10 XP
1kbp Ladder, A-6, B-10
(ここに20140731_1を貼付)
B-10:incomplete cut
others:OK
ligation
C-7 (A-6 SP + B-10 XP)
8,1,2014
Lab member
Yoshikawa,Nakashima
Contents
TF
C-7(Chemical & EP)
ligation Check
PCR reaction primed with Universal Primer, using ligation products as template.
(ここに20140801_1を貼付)
OK!
8,4,2014
Lab member
Yoshikawa,Nakashima
Contents
Plate check
No colony
digestion
A-6 SP, B-10 XP
Electrophoresis
1kbp Ladder, A-6, B-10
(ここに20140804_1を貼付)
Incomplete cut.Disposed.
preculture
B-10
8,5,2014
Lab member
Yoshikawa,Nakashima
Contents
miniprep
B-10 →Sample Lost! Bye bye, GFP!
Cut check
SP(A-1), XP(A-1), EX(A-4), ES(A-4)
2h, 2.5h, 3h
Elrctrophoresis
1kbp Ladder, A-1, A-1 SP(2h, 2.5h, 3h), A-1 XP(2h,2.5h, 3h), A-4,
A-4 EX(2h, 2.5h 3h), A-4 ES(2h, 2.5h, 3h), None, 1kbp Ladder
(ここに20140805_1を貼付)
Incomplete cut.
preculture
A-1, A-4, B-10
8,6,2014
Lab member
Yoshikawa
Contents
miniprep
A-1, A-4, B-10
digestion, gel extraction
A-6 SP, B-10 XP
1kbp Ladder, A-6, B-10
(ここに20140806_1を貼付)
A-6:OK
B-10:Incomplete cut
(ここに20140806_2を貼付)
We chenged restriction enzyme.
B-10 XP
(ここに20140806_3を貼付)
OK!
(ここに20140806_4を貼付)
making gel
200ml
ligation
C-7(A-6 SP + B-10 XP)
TF
4-11L, C-7
8,7,2014
Lab member
Yoshikawa,Nakashima,Tara,Itoh,Tsukada
Contents
colony PCR
C-7
(ここに20140807_1を貼付)
(ここに20140807_2を貼付)
All bands are self ligation of backbone.
digestion, gel extraction
A-6 SP, B-10 XP
1kbp Ladder, B-10, A-6
(ここに20140807_3を貼付)
ligation
C-7 (A-6 SP + B-10 XP)
Cut check
(O/N)
(enzyme-buffer)
S-B, S-H*, S-M*, P*-FD, E-FD
*:Takara's product
8,8,2014
Lab member
Nakashima,Nakamura,Yamanaka,Yoshikawa(Fresh),Yoshikawa
Contents
Cut check sample Electrophoresis
1kbp Ladder, S-B, S-H*, S-M*, NC, E-FD, P*-FD
(ここに20140808_1を貼付)
FD buffer is appropriate for SP cut.
digestion, gel extraction
A-6 SP, B-10 XP
(ここに20140808_2を貼付)
(ここに20140808_3を貼付)
OK!
ligation
C-7 (A-6 SP + B-10 XP)
TF
C-7,4-11L(culture in test tube)
overlap extension PCR →PCR clean up
1+2
anealing 57℃,extension 10 sec, 30 cycle.
100bp Ladder, Pecf11, Pecf20, crRBS, taRNA
(ここに20140808_4を貼付)
OK!(low concentration)
making gel
200ml
8,9,2014
Lab member
Yoshikawa
Contents
Plate and test tube check
4-11L:OK!mixed with glycerol, and conserved in -80℃.
C-7:OK! conserved in refrigerator.
8,11,2014
Lab member
Yoshikawa,Nakashima,Yamanaka,Nakamura
Contents
colony PCR
C-7 #1~11
#1~4, NC
(ここに20140811_1を貼付)
#5~11,NC
(ここに20140811_2を貼付)
#10 is OK!
overlap extension PCR →PCR clean up
Pecf11, Pecf20, crRBS, taRNA
(1+2)+3,PCR product
1kbp Ladder, Pecf11(1+2+3), Pecf20(1+2+3), crRBS(1+2+3)
taRNA(1+2+3), Pecf11(all), Pecf20(all), crRBS(all), taRNA(all)
(ここに20140811_3を貼付)
Making Plate
Ampicillin*20
preculture
A-6, B-10, C-7 #10
8,12,2014
Lab member
Nakashima,Nakamura,Yamanaka,Yoshikawa(Fresh),Yoshikawa
Contents
miniprep
A-6, B-10, C-6 #10 (made glycerol stock)
digestion →gel extraction
A-8 EX, C-6 ES, pSB1A2(A-1) XP *2 sample, A-9 linear XP
A-10 linear XP, A-7 linear XP, B-1 linear XP
1kbp Ladder, A-1 XP, A-1 XP, A-8 EX, C-6 ES
(ここに20140812_1を貼付)
C-6:wrong
A-1:OK!
colony PCR
4-11L
(ここに20140812_2を貼付)
OK!
ligation →TF
A-9, A-10, A-7, B-1
Making reagent
LB 800ml
8,13,2014
Lab member
Yoshikawa,Nakashima,Tara,Nakamura,Takemura,Tsukada
Contents
miniprep
4-11L(made glycerol stock)
digestion→ gel extrction
A-8 EX, C-6 ES, A-6 SP, 4-11L XP
1kbp Ladder, A-6 SP, A-8 EX, C-6 ES, 4-11L XP
(ここに20140813_1貼付)
(ここに20140813_2貼付)
C-6:Incomplete cut
Backbone of 4-11L said to be pSB1A2, but ,actually,may be pSB1AK3.
ligation→TF
D-7 (4-11L XP + A-6 SP), D-7(C-6 ES + A-8 EX)
colony PCR
A-7 #1~2, A-9 #1~4, A-10 #1~4, B-1 #1~2 ,100bp Ladder
(ここに20140813_3を貼付)
A-7 #1, A-10 #4:OK!
preculture
A-8, C-6, A-7 #1, A-10 #4
8,14,2014
Lab member
Yoshikawa,Nakashima,Tara,Takemura,Nakamura
Contents
miniprep
A-7 #1(made glycerol stock), A-10 #4(made glycerol stock), A-8, C-6
overlap extension PCR
Pecf 11, Pecf 20, crRBS, taRNA
(35 cycles, annealing in 59℃)
digestion → gel extranction
A-7 SP, 4-11L XP, pSB1A2 (B-10), A-7 linear XP
A-9 linear XP, A-10 linear XP, B-1 linear XP
1kbp Ladder, A-7 SP, 4-11L XP
(ここに20140814_1を貼付)
(ここに20140814_2を貼付)
OK!
A-10 XP, 100bp Ladder
(ここに20140814_3を貼付)
OK!(low concentration)
100bp Ladder, pSB1A2(B-10), pSB1A2(B-10), A-7, A-9, B-1
(ここに20140814_4を貼付)
(ここに20140814_5を貼付)
Incomplete cut of pSB1A2 is weigh on our mind.
gel making
2% 25mL
1% 200mL
colony PCR
D-7(Ampicillin), D-7(Chloramphenicol)
1kbp Ladder, D-7(Amp) #1~4, D-7(CP) #1~4
(ここに20140814_4を貼付)
ligation → TF
D-6 (A-7 SP + 4-11L XP), A-7 (linear XP + pSB1A2 XP)
A-9, A-10, B-1 (linear XP + pSB1A2 XP)
8,15,2014
Lab member
Yoshikawa,Nakashima,Tara,Takemura,Nakamura
Contents
miniprep
D-7 #3 (made glycerol stock)
digestion → gel extraction
A-10 SP, D-7 XP
(ここに20140815_1を貼付)
(ここに20140815_2を貼付)
colony PCR
A-7 #1~4, A-9 #1~4, A-10 #1~4, B-1 #1~4, D-6 #1~4
(ここに20140815_3を貼付)
A-7 #3,4, A-10 #1,2,4, B-1 #1,2,4:OK!
D-6
(ここに20140815_4を貼付)
OK!
ligation → TF
E-18 (A-10 SP + D-7 XP)
preculture
A-7 #3,4, A-10 #1,2,4, B-1 #1,2,4, D-6 #1
8,16,2014
Lab member
Yoshikawa
Contents
miniprep
A-7 #3,4, A-10 #1,2,4, B-1 #1,2,4, D-6 #1
(made glycerol stock)
8,18,2014
Lab member
Yoshikawa,Hiura
Contents
digestion → gel extraction
A-1 SP, A-2 SP, A-3 SP, D-6 XP *2, A-7 #3,4, B-10
(ここに20140818_1を貼付)
sequence
A-7, A-7 #3,4, A-10, A-10 #1,2,4, B-1 #1,2,4
C-6 F, C-6 R, D-7 F, D-7 R, D-6 F, D-6 R
colony PCR
E-18 #1~3
(ここに20140818_2を貼付)
OK!
ligation → TF
E-14 (A-3 SP + D-6 XP)
E-15 (A-1 SP + D-6 XP)
E-16 (A-2 SP + D-6 XP)
preculture
E-18 #1
8,19,2014
Lab member
Yoshikawa,Nakamura,Yamanaka,Yoshikawa(Fresh)
Contents
miniprep
E-18 (made glycerol stock)
digestion
A-8 XP, B-1 #1,2,4 SP
→Today, sequences of these dna proved to be wrong, so we disposed them.
making gel
2% gel:25ml
colony PCR
C-5, E-14, E-15,E-16
sequence
A-7:RBS
A-7 #3:none
A-7 #4:none
A-10:OK
B-1: all none
8,20,2014
Lab member
Yoshikawa,Nakamura,Tsukada,Yamanaka,Itoh
Contents
PCR
sigma11, sigma20, anti11, anti20
TF
1-21P
8,21,2014
Lab member
Yoshikawa,Nakashima
Contents
digestion → gel extraction
pSB1C3(A-4), A-6, B-3(We did not extract.), A-6'(NEB buffer)
(ここに20140821_1を貼付)
colony PCR
A-2, A-3, A-4, 1-21P, NC
(ここに20140821_2を貼付)
ligation → TF
B-3, C-2, C-2'
preculture
1-21P
8,22,2014
Lab member
Yoshikawa,Nakamura,Nakashima
Contents
miniprep
1-21P(made glycerol stock)
colony PCR
B-3, C-2, C-2
(ここに10140822_1を貼付)
digestion → gel extraction
1-21P SP, D-7 XP, C-6 E, C-6 P, C-6(NC)
(ここに20140822_2を貼付)
(ここに20140822_3を貼付)
ligation → TF
K-1(1-21P SP + D-7 XP)
preculture
B-3 #1,4, D-7, C-2 #1,3, C-2' #3,4
8,23,2014
Lab member
Yoshikawa
Contents
miniprep
D-7, B-3 #1,4, C-2 #1,3, C-2' #3,4
8,25,2014
Lab member
Nakashima,Nakamura,Yamanaka,Hiura
Contents
digestion → gel extraction
A-8 EX *2, B-3 #1 ES, B-3 #4 ES, C-2 #1 ES, C-2 #3 ES
(ここに20140825_1を貼付)
(ここに20140825_2を貼付)
Plate making
Ampicillin*10, Chloramphenicol*20
colony PCR
K-1
(ここに20140825_3を貼付)
#1,4:OK!
sequence
B-3 #1,4, C-2 #1,3, C-2' #3,4, E-18, A-2, A-3
ligation → TF
D-3 1 (A-8 EX + C-2 #1 ES), D-3 2(A-8 EX + C-2 #3 ES)
C-10 1(A-8 EX + B-3 #1 ES), C-10 2(A-8 EX + B-3 #4 ES)
preculture
A-2, A-8, K-1 #1, C-2 #1,3, B-3 #1
8,26,2014
Lab member
Yoshikawa,Nakashima,Yamanaka,Takemura,Tsukada
Contents
miniprep
K-1(made glycerol stock), A-2, A-8, B-3 #1, C-2 #1, C-2 #3
colony PCR
D-3 (1), D-3 (2), C-10 (1), C-10 (2)
(ここに20140826_1を貼付)
OK!(C-10(1)#4 may be a little shorter.)
digestion → gel extraction
K-2 linear EP, K-3 linear EP, K-4 limear EP, K-5 linear EP, pSB1C3 (A-4) EP
(ここに20140826_2を貼付)
We forgot to take the photo before we sliced the band.
ligation → TF
K-2, K-3, K-4, K-5
preculture
D-3 #1, C-10 #1
sequence
A-2:E-X-S-S-Pconst(weak)-S-P wrong sequence
A-3:OK
B-3 #1:OK
B-3 #4:OK
C-2 #1:OK
C-2 #3:OK
C-2' #3:point mutation *2
C-2' #4:OK
E-18:OK
8,27,2014
Lab member
Yoshikawa,Nakashima,Yoshikawa(fresh),Itoh,Tara,Nakamura
Contents
miniprep
D-3, C-10(made glycerol stock)
digestion → gel extraction
A-1 SP, A-3 SP, A-10 SP, D-3 XP *2
(ここに20140827_1を貼付)
(ここに20140827_2を貼付)
D-3 is a little strange.Contamination?
colony PCR
K-2~5 #1~4
*100bp Ladder
(ここに20140827_3を貼付)
500bp band:self ligation of backbone(A-4 200bop)
300bp band:If this band is blank vector, this band(EP cut) must be shorter than 300bp.
So, this band is OK!.
→K-2 #4, K-3 #1, K-4 #1,2, K-5 #2,3,4:OK!
ligation → TF
E-5(A-3 SP + D-3 XP), E-6(A-1 SP + D-3 XP), E-20(A-10 SP + D-3 XP)
preculture
E-15
D-3, C-10(Recovery)
K-2 #4, K-3 #1, K-5 #2
Remarks
(ここに20140827_4を貼付)
We observed that pCMV expressed in Escherichia.coli.
Left:pCMV-GFP
Right:pConst(strong)-GFP
We may be able to carry out the characterization of pCMV and meet the gold medal requirement(parts implovement).
8,28,2014
Lab member
Yoshikawa,Nakashima,Itoh,Tara,Tsukada,Yamanaka
Contents
miniprep
K-2~5, E-23(made glycerol stock)
C-10, D-3
digestion →gel extraction
K-2 EX, K-3 EX, K-4 EX, K-5 EX, E-23 EP, C-6 ES *2, pSB1C3(A-4) EP, pSB1C3(A-4) XP
(ここに20140828_1を貼付)
(ここに20140828_2を貼付)
→pSB1C3 XP:keep in freezer
overlap extension PCR
A-7, A-9, B-1, B-2, D-8, D-9
check & colony PCR
check:E-5, E-6
colony PCR:(E-5 #1~4, E-6 #1~4, NC), A-7, A-9, D-8, B-1, B-2, D-9
(ここに20140828_3を貼付)
A-7, A-9, B-1, D-8:OK!
B-2, D-9:Needs retry in higher concentration.
E-5 #2~4, E-6:OK!
ligation → TF
E-23'(E-23 EP + pSB1C3 EP), L-1~4(C-6 ES + K-2~5 EX)
8,29,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Tara,Tsukada,Yamanaka
Contents
miniprep
E-5(made glycerol stock)
A-4, C-6
E-6:No colony
digestion →gel extraction
A-7 linear XP, A-9 linear XP, A-10 SP, B-1 linear XP, D-3 XP, D-8 linear XP, E-5 SP, E-18 XP
(ここに20140829_1を貼付)
(ここに20140829_2を貼付)
PCR
B-2, D-9(retry)
check & colony PCR
E-23' #1~4, B-2, D-9
(ここに20140829_3を貼付)
L-1~4
(ここに20140829_4を貼付)
E-23 #1, L-1 #2,4, L-2 #4, L-3 #1~3, L-4 #2,4:OK!
PCR:failed
sequence
order
E-23, K-1, K-2~5
ligation → TF
A-7, A-9, B-1, D-8( linear XP + pSB1C3 XP)
F-2 (E-18 XP + E-5 SP), E-20(A-10 SP + D-3 XP)
preculture
A-10, E-6, E-18, E-23', K-3, L-1~4
8,30,2014
Lab member
Yoshikawa
Contents
miniprep
A-10, E-18, K-3
L-1~4, E-23', E-6, E-18(made glycerol stock)
PCR
B-2, D-9
9,1,2014
Lab member
Nakashima,Itoh,Tara,Tsukada,Takemura
Contents
PCR clean up
B-2, D-9
(ここに20140901_1を貼付)
failed
digestion → gel extraction
L-4 ES, E-6 ES, E-18 EX, L-1~3 ES, K-2~5 EX
(ここに20140901_2を貼付)
(ここに20140901_3を貼付)
Making plate
CP *30
colony PCR
A-7 #1~4, A-9 #1
(ここに20140901_4を貼付)
OK!
D-8 #1~4, E-20 #1~4, F-2 #1~4 (100bp Ladder)
(ここに20140901_5を貼付)
D-8 #1~4, E-20 #1~4,F-2#1,4:OK!
B-1 #1~4
(ここに20140901_6を貼付)
OK!
Ligation
G-5 (E-6 ES + E-18 EX), M-1~4(K-2~5 EX + L-1~4 ES)
preculture
A-7 #1~4, A-9 #1, B-1 #1~4, D-8 #1~4, F-2, E-20
9,2,2014
Lab member
Yoshikawa,Nakashima,Hiura,Tara,Tsukada,Takemura
Contents
miniprep
A-7 #1~4, A-9, B-1 #1~4, D-8 #1~4, F-2, E-2(made glycerol stock)
digestion → gel extraction
A-4 SP, A-7 SP #1, A-7 SP #2, A-9 SP
(ここに20140902_1を貼付)
(ここに20140902_2を貼付)
A-8XP
(ここに20140902_3を貼付)
(ここに20140902_4を貼付)
B-1 #1, B-1 #2, B-3 XP, B-10 XP, D-7 XP, D-8 XP, E-20 XP, F-2 SP
(ここに20140902_5を貼付)
(ここに20140902_6を貼付)
D-8:???
sequence
A-7 #1~4, B-1 #1~4
D-8 #1~4
A-9, A-4, E-5, E-6, E-20
PCR
B-2, D-9
check & colony PCR
M-1~3 #1~4
(ここに20140902_7を貼付)
OK!
M-4 #1~4, B-2, D-9, PC(VF2→B-10←VR )
NC, G-5
(ここに20140902_8を貼付)
M-4 #1~4,PC(VF2→B-10←VR ):OK!
(M-4 #2~4 is a little longer?)
ligation → TF
C-4(A-7 SP + B-3 XP), C-5(A-7 SP + B-10 XP), E-17(A-9 SP + D-7 XP)
D-1(B-1 SP + D-8 XP), E-21(A-4 SP + D-8 XP), J-4 (F-2 SP + E-20 XP)
preculture
M-1~4 #1, G-5 #1, E-20
We got a result!!
simpler version of assay 1.
Left:Pσ11→GFP generator
Right:Pconst(strong)→σ11 generator & Pσ11→GFP generator
(ここに20140902_9を貼付)
9,3,2014
Lab member
Yoshikawa,Nakashima,Hiura,Tara,Yamanaka,Takemura
Contents
M-1~4, G-5(made glycerol stock of M-3,4)
sequence
A-4: OK
A-7: all OK
A-9: OK
B-1:all OK
D-8#1: NG (2 skip)
#2; OK
#3: NG (1 mut.)
#4; NG (2 mut. 2 skip)
E-5: OK
E-6: OK
E-20:OK
digestion → gel extraction
A-7 #1, 2 SP, C-9 XP, C-10 XP, K-2~4 EX, M-1~4 ES
(ここに20140903_1を貼付)
(ここに20140903_2を貼付)
colony PCR
C-4 a, C-5 a, D-1 a, E-17
(ここに20140903_3を貼付)
C-4 a#2,3, C-5 a, D-1 a, E-17:OK!
E-21 b, J-4
(ここに20140903_4を貼付)
J-4:wrong
E-21:a little shorter
PCR
B-2, D-9, B-7, B-2(Taq), D-9(Taq)
Taq:positive control
anealing temperature:51℃~61℃(gradient)
ligation → TF
D-5 (A-7 SP + C-10 XP), D-6 (A-7 SP + C-9 XP), N-1~4 (M-1~4 ES + K-2~5 EX)
N-5 (M-1 ES K-3 EX), N-6 (M-3 ES + K-5 EX)
9,4,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Tara,Yamanaka,Tsukada
Contents
miniprep
E-20, D-1(made glycerol stock)
digestion → gel extraction
pSB1C3 (A-4) XS, pSB1C3 (A-4) XP, A-3 SP, D-1 XP
(ここに20140904_1を貼付)
(ここに20140904_2を貼付)
PCR check & clean up
D-9 #1,3,5,7,9
(ここに20140904_3を貼付)
There are bands in #5,7,9
B-2 1,3,5,7,9,11, B-7.
(ここに20140904_4を貼付)
There are bands in B-2 #5,7, B-7.
B-2 4,6, D-9 10,11,12
(ここに20140904_5を貼付)
OK!
We decided to clean up B-2 #5, D-9 #11, B-7.
Making Plate
CP *30
digestion
B-2 linear SP, D-9 linear XP, B-7 linear XP
colony PCR
D-5, D-6, N-1, N-2 #1~4
(ここに20140904_6を貼付)
D-5, D-6, N-1, N-2 #1,2:OK!
N-3~6 #1~4
(ここに20140904_7を貼付)
N-3,N-4#1,3,4,N-5#1,4,N-6:OK!
ligation → TF
E-1 (A-3 SP + D-1 XP), B-2 (B-2 linear XP + pSB1C3 XP), D-9 (D-9 linear XP + pSB1C3 XP)
B-7 (B-7 linear XP + pSB1C3 XP), Emp. (pSB1C3 XS)
miniprep
M-1, M-2, C-4, C-5, E-17, E-21, J-4 (made glycerol stock)
K-2, K-5, C-9
preculture
F-2, D-5, D-6, N-1~6
9,5,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Takemura,Yamanaka
Contents
miniprep
F-2
N-1~6, D-5, D-6
→N-1, 2, 4, 5. 6 :failed
digestion → gel extraction
A-1 SP, A-3 SP, D-5 XP, D-6 SP, E-20 XP, K-4 EX, N-3 ES, F-2 SP
J-4 E(check)
(ここに20140905_1を貼付)
(ここに20140905_2を貼付)
ligation → TF
E-11 (A-3 SP + D-5 XP), E-12 (A-1 SP + D-5 XP), E-14 (A-3 SP + D-6 SP)
E-15 (A-1 SP + D-6 XP), O-3 (K-4 EX + N-3 ES)
PCR
B-2, D-9, B-7
colony PCR
B-2, B-7, D-9, E-1 #1~4
(ここに20140905_3を貼付)
B-7 #2,3, D-9 #1, E-1 #3,4:OK!
Z-1 #2~4
(ここに20140905_3を貼付)
preculture
B-7 #2,3, D-9 #1, E-1 #3, Z-1 #2, K-3, N-1,2,4~6
9,6,2014
Lab member
Yoshikawa,Nakashima
Contents
miniprep
B-7 #2,3, D-9 #1, E-1 #3, Z-1 #2 (made glycerol stock)
N-1,2,4~6, K-3
PCR
B-2, D-9, B-7
check & colony PCR
B-2, D-9, B-7, B-2#5~8, B-7 #2,3,5,6, D-9 #5,6.7,8
(ここに20140906_1を貼付)
9,8,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Takemura,Yamanaka,Tara
Contents
digestion → gel extaction
B-2 linear XP (did not extracted)
N-1,2,4~6 ES
(ここに20140908_1を貼付)
(ここに20140908_2を貼付)
A-4 SP, pSB1C3 (A-4) XP, A-6 SP, B-7 #2 XP, B-7 #3 XP, D-9 #1 XP, K-2,3,5 EX
(ここに20140908_3を貼付)
B-7 #2, D-9 #1:disposed(incomplete cut)
B-7 #3:disposed(a little longer)(This band proved to be correct.9/9 wrote)
(ここに20140908_4を貼付)
colony PCR
E-11,12,14,15 #1~4
(ここに20140908_5を貼付)
OK!
O-3 #1,2
(ここに20140908_6を貼付)
#2:OK!
PCR
B-2
(ここに20140908_7を貼付)
B-7,D-9
(ここに20140908_8を貼付)
The band of the longest DNA is OK!
ligation → TF
O-1 (N-1 ES + K-2 EX), O-2 (N-2 ES + K-3 EX), O-4 (N-4 ES + K-5 EX), O-5 (N-5 ES + K-3 EX)
O-6 (N-6 ES + K-5 EX), B-2 (B-2 linear XP (9/8 PCR
) + pSB1C3 XP)
B-2' (B-2 linear XP + pSB1C3 XP), D-9, B-7
preculture
D-5, D-6, N-3, E-21, A-3, K-4
E-11 #1, E-12 #1, E-14 #1, E-15 #1, O-3 #2, B-7 #6, D-9 #5,6
9,9,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Takemura,Yamanaka
Contents
miniprep
E-11,12,14,15, O-3, B-7 #6, D-9 #5,6 (made glycerol stock)
D-5, N-3, K-4, A-3
D-6:did not grow
D-5:diposed(doubt of cross contamination)
digestion → gel extraction
B-2 linear XP (did not extracted)
pSB1C3 (A-4) XP, A-6 SP, A-8 EX, B-7 #6 XP, D-9 linear XP, B-7 linear , E-1 EX, E-14 ES, E-15 ES, O-3 ES
(ここに20140909_1を貼付)
(ここに20140909_2を貼付)
colony PCR
B-2 #1~3, B-2' #1~4, B-7 #1~3, D-9 #1,2, O-1 #1~4
(ここに20140909_3を貼付)
B-2' #1, B-7 #1,2, O-1:OK!
O-2,4,5,6 #1~4
(ここに20140909_4を貼付)
O-2,3,4,6,O-5#2,3,4
sequence
B-7 #2: NG
B-7 #3: OK
D-9 #1: NG
E-1: OK
C-4: OK
C-5: OK
D-5: OK
D-6: OK
E-17: OK
E-21: OK
F-2: ?
J-4: OK
N-1: OK
N-2: M-2
N-3: OK
N-4: M-4
N-5: OK
N-6: OK
ligation → TF
D-9 (D-9 linear XP + pSB1C3 XP) *2, B-2 (B-2 linear XP + pSB1C3 XP)*2
P-3 (O-3 ES + A-8 EX), I-1 (E-14 ES + E-1 EX), I-2 (E-15 ES + E-1 EX)
preculture
F-2, D-5,6, D-9 #5,6, E-12, K-4
O-1,2,4,6 #1, O-5 #2, B-2' #1
9,10,2014
Lab member
Nakashima,Hiura,Takemura,Yamanaka,Yoshikawa(Fresh)
Contents
miniprep
O-1,5,6, N-2,4, F-2, B-2' #1 (made glycerol stock)
E-12, K-4, D-5
D-6:did not grow
digestion → gel extraction
A-6 SP, A-7 SP, A-8 EX, B-2' XP, B-7 XP
(ここに20140910_1を貼付)
(ここに20140910_2を貼付)
K-3 EX, K-5 EX, O-1 ES, N-2 ES, N-4 ES
(ここに20140910_3を貼付)
(ここに20140910_4を貼付)
O-5 ES, O-6 ES
(ここに20140910_5を貼付)
(ここに20140910_6を貼付)
colony PCR
I-1 #1~4
(ここに20140910_7を貼付)
B-2(140909) #1~8, D-9 #1~8
(ここに20140910_8を貼付)
B-2 #2, D-9 all, I-1 #2~4, I-2 #1~4:OK!
ligation → TF
O-2 (N-2 ES + K-3 EX), O-4 (N-4 ES + K-5 EX), P-1 (O-1 ES + A-8 EX), P-5 (O-5 ES + A-8 EX)
P-6 (O-6 ES + A-8 EX), C-1' (B-2' XP + A-6 SP), C-3' (B-2 ' XP + A-7 SP), C-8 (B-7 XP + A-6 SP)
preculture
D-9 #5,6, D-9(140909) #1~4, B-2 (140909) #2, I-1 #2, I-2 #1, D-6 #1
9,11,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Hiura,Itoh,Tsukada
Contents
miniprep
D-9 #1~4, B-2 #2, I-1, I-2, D-6 (made glycerol stock)
digestion → gel extraction
A-1 EP, pSB1C3(A-3) EP *2, A-4 SP, A-6 SP, A-7 SP, A-8 EX, A-10 EX, B-2 #2 XP, E-6 EP, E-12 EP
(ここに20140911_1を貼付)
(ここに20140911_2を貼付)
E-15 EP, E-18 EP, E-20 EP, D-9 #1 XP, O-3 ES
(ここに20140911_3を貼付)
(ここに20140911_4を貼付)
sequence
B-2' #1, B-2 #2, F-2, E-11, E-12, E-14, E-15, O-1, N-2, O-3, N-4, O-5, O-6, D-9 #1~4
Plate Making
CP *30
colony PCR
C-1', C-3', C-8, O-2, O-4 #1~3
(ここに20140911_5を貼付)
C-1', C-3', C-8, O-2, O-4 #1,2:OK!
P-1, P-5, P-6 #1~3
(ここに20140911_6を貼付)
→P-1 #1~3, P-5 #2, P-6 #1~3:OK!
ligation → TF
C-1 (A-6 SP + B-2 #2 XP), C-3 (A-7 SP + B-2 #2 XP)
E-22a (A-4 SP + D-9 #1 XP), E-22b (A-4 SP + D-9 #2 XP), P-3 (O-3 ES + A-8 EX)
A-10 CP, A-1 CP, E-6 CP, E-12 CP, E-20 CP, E-15 CP (replace backbone to pSB1C3)
preculture
C-1' #1, C-3' #1, C-8 #1, O-2 #1, O-4 #1, P-1 #1, P-5 #2, P-6 #1, E-12,14,15,18, A-1, O-1,3,5
9,12,2014
Lab member
Yoshikawa,Nakashima,Hiura,Itoh,Tsukada,Tara
Contents
miniprep
C-1', C-3', O-2, O-4, P-1, P-5, P-6 (made glycerol stock)
E-12, E-14, E-15, O-1, O-3, O-5, E-18, A-1
digestion → gel extraction
A-6 SP, A-8 EX *2, B-2 XP, C-1' ES, C-3' ES, C-8 ES, K-6 SP, O-2 ES, O-3 ES, O-4 ES
(ここに20140912_1を貼付)
(ここに20140912_2を貼付)
C-1,3:did not extracted
P-1 XP, P-5 XP, P-6 XP
(ここに20140912_3を貼付)
(ここに20140912_4を貼付)
colony PCR
A-1 CP, A-10 CP, C-3, E-6 CP #1~3
(ここに20140912_5を貼付)
E-12 CP, E-15 CP, E-20 CP, E-22a, E-22b
(ここに20140912_6を貼付)
except E-20#1,2:OK!
sequence
B-2' #1: NG
B-2 #2: OK
D-9 #1: NG
#2: NG
#3: NG
#4: OK
O-1: OK
O-3: OK
O-5: OK
O-6: OK
N-2: OK
N-4: OK
E-11: Ok
E-12: OK
E-14: OK
E-15: OK
F-2: OK
ligation → TF
C-1 (A-6 SP + B-2 XP), P-2~4 (O-2~4 ES + A-8 EX), Q-1,5,6 (P-1,5,6 XP + K-6 SP)
ligation
P-3 (O-3 ES + A-8 EX)
preculture
A-4, A-6, A-8, C-3 #1, A-1 CP #1, E-12 CP #1, E-15 CP #1, E-20 CP #3, E-6 CP #1
9,13,2014
Lab member
Yoshikawa
Contents
miniprep
A-4, A-6, A-8
C-3, E-6 CP, E-12 CP, E-15 CP, E-20 CP, A-1 CP(made glycerol stock)
9,15,2014
Lab member
Yoshikawa,Nakashima,Takemura,Nakamura,Yamanaka,Tara
Contents
digestion → gel extraction
A-8 EX, C-3 ES
(ここに20140915_1を貼付)
(ここに20140915_2を貼付)
colony PCR
C-1 (σ20F, VR)
(ここに20140915_3を貼付)
OK!
ligation → TF
D-4 (C-3 ES + A-8 EX) *2 (new and old competent cell)
preculture
C-1 #1
9,16,2014
Lab member
Yoshikawa,Nakashima,Hiura,Itoh,Nakamura,Yamanaka
Contents
miniprep
C-1 (made glycerol stock)
colony PCR
D-4, P-2~4
(ここに20140916_1を貼付)
A-10
(ここに20140916_2を貼付)
Q-1,5,6:confirmed by fluorescence
digestion → gel extraction
A-4 SP, A-8 EX, C-1 ES, D-9 XP, E-18 EP, pSB1C3 (A-4) EP
(ここに20140916_3を貼付)
ligation → TF
D-2 (A-8 EX + C-1 ES), E-18 CP (E-18 EP + pSB1C3 EP) *2, E-22 (A-4 SP + D-9 XP)
preculture
D-4, P-2~4, A-10 CP, Q-1,5,6
assay
(ここに20140916_4を貼付)
PC (E-23': Pconst (strong)-GFP-d.term)
Experiment (K-1: pCMV-GFP-d.term)
NC (Z-1: pSB1C3)
absorbance:600nm and 395nm
Absorbance of 395nm proved not to be able to measure.
We need fluorospectro-photometer.
9,17,2014
Lab member
Yoshikawa,Nakashima,Hiura,Takemura,Yoshikawa(Fresh),Yamanaka
Contents
miniprep
D-4, P-2~4, Q-1,5,6, A-10 CP (made glycerol stock)
digestion → gel extraction
A-1 CP SP, A-3 SP, D-4 XP, K-6 SP, P-2 XP, P-3 XP, P-4 XP
(ここに20140917_1を貼付)
(ここに20140917_2を貼付)
colony PCR
D-2, E-18 CP, E-22
(ここに20140917_3を貼付)
ligation → TF
E-8 *2 (A-3 SP + D-4 XP), E-9 (A-1 CP SP + D-4 XP), Q-2~4 (K-6 SP + P-2~4 XP)
assay
Photo of plate(n=4)
(ここに20140917_4を貼付)
(ここに20140917_5を貼付)
(ここに20140917_6を貼付)
(ここに20140917_7を貼付)
(ここに20140917_8を貼付)
(ここに20140917_9を貼付)
(ここに20140917_10を貼付)
(ここに20140917_11を貼付)
9,18,2014
Lab member
Yoshikawa,Nakashima,Hiura,Tsukada,Nakamura
Contents
miniprep
D-2, E-18 CP, E-22
(made glycerol stock)
digestion → gel extraction
A-1 CP SP, A-3 SP, A-9 SP, D-2 XP, E-22 SP, G-5 XP
(ここに20140918_1を貼付)
(ここに20140918_2を貼付)
colony PCR
E-8, E-9 #1~4
(ここに20140918_3を貼付)
Q-2~4 :confirmed by fluorescent
ligation → TF
E-2 (A-3 SP + D-2 XP)
E-3 (A-1CP SP + D-2 XP)
E-19 (A-9 SP + D-2 XP)
H-5 (E-22 SP + G-5 XP)
preculture
E-8,9, Q-2~4
9,19,2014
Lab member
Yoshikawa,Nakashima,Hiura,Tara,Yamanaka,Nakamura
Contents
miniprep
E-8,9, Q-2~4
digestion → gel extraction
A-9 SP, D-2 XP, E-22 XP, F-2 SP, G-5 SP
(ここに20140919_1を貼付)
(ここに20140919_2を貼付)
colony PCR
E-2, E-19 #1~4
E-3, H-5 :No colony
(ここに20140919_3を貼付)
ligation → TF
H-5 (G-5 SP + E-22 XP)
H-4 (F-2 SP + E-22 XP)
preculture
E-2 E#1, E-19 #1
9,20,2014
Lab member
Yoshikawa
Contents
miniprep
E-2, E-19
9,22,2014
Lab member
Yoshikawa,Nakashima,Takemura,Nakamura,Tara,Yamanaka
Contents
digestion → gel extraction
A-1 SP, D-2 XP, E-2 ES, E-17 EX, E-22 XP, G-5 SP, J-4 SP
(ここに20140922_1を貼付)
(ここに20140922_2を貼付)
colony PCR
H-4
(ここに20140922_3を貼付)
H-4#1,2,3:OK!
ligation - TF
H-5 (G-5 SP + E-22 XP)
J-6 (J-4 SP + E-22 XP)
E-3 (A-1 SP + D-2 XP)
F-1 (E-2 ES + E-17 EX)
9,23,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Tara,Yamanaka
Contents
digestion, gel extraction
A-1CP SP, D-2 XP, E-22 XP, G-5 SP
J-4 SP(stopped)
(IMGP0241)
(IMGP0242)
miniprep
H-4(made glycerol stock)
colony PCR
J-6
(IMGP0243)
OK!
ligation,TF
H-5(G-5 SP+E22 XP)
E-3(A-1 SP+D-2 XP)
9,24,2014
Lab member
Nakashima,Yamanaka,Takemura
Contents
miniprep
F-1,J-6(made glycerol stock)
E-22,D-6
G-6 did not grow.
We disposed J-6.(dropped on the floor)
digestion,gel extraction
A-1 SP,E-2 ES,E-5 ES,E-17 EX,E-18CP EX,E-21 XP,G-5 SP,D-2 XP,F-1 SP,E-19 XP,E-22 XP
(IMGP0244)
(IMGP0245)
ligation,TF
J-2(F-1 SP+E-1 XP)
H-1(F-1 SP+E-21 XP)
F-3(E-5 ES+E-17 EX)
F-4(E-2 ES+E-18 EX)
E-3(A-1 SP+D-2 XP)
H-5(G-5 SP+E-22 XP)
9,25,2014
Lab member
Yakashima,Nakamura,Tara,Tsukada,Yamanaka
Contents
miniprep
A-1CP, J-6
digestion,gel extraction
A-1 SP,D-2 XP,E-5 ES,E-17 EX,E-19 XP,E-21 XP,E-22 XP,F-1 SP,G-5 SP
(IMGP0247)
(IMGP0248)
ligation,TF
J-2(F-1 SP+E-19 XP)
H-1(F-1 SP+E-21 XP)
F-3(E-5 ES+E-17 EX)
E-3(A-1 SP+D-2 XP)
H-5(G-5 SP+E-22 XP)
Making competent cell
preculture
G-5,E-5,E-17,E. coli JM109
competent cell(made at 0911) check
Ampicillin,Chloramphenicol,non-antibiotics
culture in LB 3ml
LB midium 3ml, as N.C.
culture for 2h.
no TF
result of check
Ampicilln,non-antibiotics:become clouded
Chroramphenicol,LB:did not grow
So,we confirmed ampicillin resistant plasmid exists in competent cell and we disposed it.
9,26,2014
Lab member
Yoshikawa,Nakashima,Tara,Yoshikawa(Fresh),Tsukada
Contents
miniprep
G-5,E-5,E-17
digestion
A-1CP SP,D-2 XP,G-5 EP(strange band appeared,disposed),pSB1C3(A-4) EP,A-1 SP
(IMGP0249)
(IMGP0250)
colony PCR
F-3,F-4,H-1,H-5,J-2
(IMGP0251)
(IMGP0253)
OK!
ligation,TF
E-3(D-2 XP+A-1SP)
E-3CP(D-2 XP+A-1CP SP)
preculture
F-3,F-4,H-1,H-5,J-2#1,G-5
9,27,2014
Lab member
Yoshikawa,Nakashima
Contents
TF
E-3(chemical)
miniprep
F-3,F-4,H-1,H-5,J-2(made glycerol stock)
G-5
TF
E-3CP(electroporation)
9,29,2014
Lab member
Yoshikawa,Nakashima,Nakamura,Tara,Takemura
Contents
digestion,gel extraction
A-1CP SP,pSB1C3(A-4) EP,D-2 XP,E-21 XP,G-5 EP,H-5 EP,J-2 SP
(IMGP0256(0929))
(IMGP0257)
Gel making
ligation
G-5CP(G-5 EP+pSB1C3 EP)
H-5CP(H-5 EP+pSB1C3 EP)
J-5(J-2 SP+E-21 XP)
E-3(A-1CP SP+P-2 XP)
preculture
E-5,E-11,E-22
K-1,E-23',Z-1(for assay)
PCR
VF2-(ligation product of E-3)-VR
E-3 lig:1uL
VF2:1uL
VR:1uL
Taq:5uL
MilliQ:2uL
Total:10uL
extention:1m12sec
(IMGP0258(0929))
We observed the band which had expected length.
The ligtion must be OK!
9,30,2014
Lab member
Yoshikawa.Nakamura,Tsukada,Tara,Yamanaka
Contents
miniprep
E-2,E-5,E-11
digestion,gel extraction
We could not find D-2 sample(probably accidentally disposed).
So we stopped it.
colony PCR
G-5,H-5,J-5
(IMGP0258(0930))
H-5,J-5#1,2,3,4:OK!
Making M9 medium
1M MgSO4 50ml
1M CaCl2 10ml
20% glucose 50ml
digestion(cutcheck)
G-5 E,G-5 P,G-5 non-cut
(IMGP0260)
Strange bands appeared.
We decided to read a sequence of this part.
10,1,2014
Lab member
Nakashima,Tara,Nakamura,Takemura
Contens
miniprep
H-5CP,G-5CP,J-5(made glycerol stock)
sequence order
E-2,E-8,E-9,E-19,F-1,F-3,F-4,G-5,H-1,H-4
H-5(VF-VR,Psigma2F-anti2R),J-2,J-4,J-5,J-6
10,2,2014
Lab member
Nakashima,Nakamura,Tara
Contents
assay
completely failed
In M9 medium, Escherichia.coli JM109 did not grow well.(doubling time:1h)
The glycerol stock needed to be put a lot.
culture
F-2,F-3,F-4(for M9 check)
PCR
G-1(F,R),G-4(F,R),G-5(F,R),H-1(F,R),H-4(F,R),H-5(F,R)
Template:1ng/uL,1uL
Making M9 medium
5*M9 24mL
20% Glu 1.2mL
MgSO4 240uL
CaCl2 12uL
Amino acid 10mL
mess up to 1L
PCR check
(IMPG0261)
OK!(except G-5)
PCR
G-1,G-4,H-1,H-4
extension time:6m30sec
EpCAM nested
95C 3min-(-95C 30sec-48C 30sec-72C 3min-)*30-72C 5min-4C
preculture
E-17,E-18,F-1,F-2,F-3,F-4,Z-1(M9)
E-23'(LB)
10,3,2014
Lab member
Nakashima,Tara,Nakamura
Contents
PCR clean up and check
(IMGP0262)
G-1,G-4,H-1,H-4:Band in correct position and unexpected position.
EpCAM:No band
digestion,gel extraction
(IMGP0263)
(IMGP0264)
G-1deg linear EP,G-4deglinear EP,H-1deg linear EP,H-4deg linear EP(A-4)
(deg:degradation tag)
PCR
EpCAM nested
94C 3min-(-94C 30sec-48C 30sec-72C 3min-)*30-72C 5min-4C
(IMGP0265)
No band
gel making
ligation,TF
H-1'(H-1'linear EP+pSB1C3 EP)
H-4'(H-4'linear EP+pSB1C3 EP)
G-1'(G-1'linear EP+pSB1C3 EP)
G-4'(G-4'linear EP+pSB1C3 EP)
PCR
EpCAM nested
95C 3min-(-95C 30sec-46C 30sec-72C 3min-)*30-72C 5min-4C
(IMGP0267)
No band
10,4,2014
Lab member
Yoshikawa
assay1
F-2 in 20mL culture
When OD600=0.516,we put 10% arabinose 20uL.
F-1 in 20mL culture
When OD600=0.514,we put 10% arabinose 20uL.
Z-1 in 20mL culture
When OD600=0.544,we put 10% arabinose 20uL.
O/N culture
Culture in 3mL:disposed(OD600 value did not agrees with each other.)
colony PCR
H-1deg,H-4deg,G-1eg,G-4deg
We confirmed by fluorescence.
preculture
H-1deg,H-4deg#1,G-1eg,G-4deg
10,5,2014
Lab member
Yoshikawa
Contents
miniprep
H-1deg,H-4deg#1,G-1eg,G-4deg
preculture
(M9)
E-15CP,E-17,E-18CP,I-2,F-1,2,3,4,Z-1
10,6,2014
Lab member
Yoshikawa,Nakashima,Tara
Contents
assay1,3
E-15,I-2,F-1,F-2,F-3,F-4,F-17,E-18,Z-1
Measured the OD 600 value.
Except I-2,F-2,the value is more than 1.0.
F-1,F-3,E-17,Z-1(20 fold dilution):Culture in 3mL.
The composition of M9 proved to be wrong,so disposed.
Making M9 medium
5*M9 200mL
20% Glu 10mL
amino acids 10mL
1M MgSO4 2mL
1M CaCl2 100uL
MilliQ 778mL
Total 1000mL
result of OD600
F-3(non-Chloramphenicol)
1h:0.094
2h:0.086
F-3(Chloramphenicol)
1h:0.074
2h:0.073
assay1
(tentative)
We measured the fluorescence of F-1,F-2,Z-1(culture in 20mL).
Ex:501nm
F-2:We observed a peak in 511nm.
F-1,Z-1:No peak
preculture
F-1,F-2,F-3,F-4,E-15CP,I-2,Z-1,E-17,E-18CP(M9 medium)
E-23'(LB medium)
10,7,2014
Lab member
Yoshikawa,Nakashima,Tara,Nakamura
Contents
assay
F-1,F-13,E-17,Z-1,E-15,I-2
culture in M9
except E-15,OD600 is more than 1.0.
E-15:over 0.85
F-2:only 0.3
E-23':over 2.0,culture in LB medium
All samples was cultured in 3 mL.(n=5)
F-1,Z-1:cultured in 20mL(flask)(n=1)
OD600 before subculture
E-15:1.113
E-11:0.933
E-18:1.190
E-23':forgot to measure
F-1:0.927
F-2:0.374
F-3:0.878
F-4:0.992
I-2:0.888
Z-1:1.084
PCR
J-5(F,R),J-6(F,R)
J-5:OK!
extension time
F:1800+200=2000bp,4min
R:1850+200=2050bp,4min
PCR
J-6(F,R)
J-5R
extension time:1min
10,8,2014
Lab member
Yoshikawa,Nakashima,Tara
Contents
making gel
subculture
Escherichia.coliMG1655
100 fold dilution
20mL,flask
PCR clean up and check
(IMGP0268)
(IMGP0269)
J-6F:low concentration
J-6R:shorter band is OK!
We decided that we made J-5,J-6
as H5deg-positive feedback circuit, and H6deg-positive feedback circuit.
sequence order
G-1deg,G-4deg,H-1deg,H-4deg
TF
(electroporation)
F-1,F-2,F-3,F-4,E-17,E-18CP,Z-1,I-2,E-15
G-1deg,G-4deg,H-1deg,h-4deg,J-4,J-4
also,2mL culture as recovery.
10,9,2014
Lab member
Yoshiakwa,Nakashima,Tara
digestion,gel extraction
E-19 XP,E-20 XP,H-1deg SP,H-4deg SP
making gel
making plate
Chloramphenicol*10
ligation
J-5deg(E-19XP+H-1degSP)
J-6deg(E-20XP+H-4degSP)
We did not have competent cell of E.coli MG1655, so put it in freezer.
preculture
F-1,E-17,E-15,Z-1,I-2:both in LB medium and M9 medium.
MG1655:LB medium
10,10,2014
assay1,3
I-2,E-15,Z-1,F-1,E-17(made glycerol stock,subculture in 20 fold dilution)
MG1655 did not grow, because we accidentally put antibiotics.
OD600(O/N culture)
E-15:1.795
E-17:1.781
F-1:1.937
I-2:1.886
Z-1:1.780
making M9 medium
Modeling is an attempt to describe, in a precise way, an understanding of the elements of a system of interest, their states, and their interactions with other elements.
The purpose of our modeling team is to peel back the layer of appearance of the device to reveal it's underlying nature. We tried to improve the device, cooperating with the experiment team. To achieve our goal, we have developed three fundamental themes. These three themes divide the modeling part into three parts. At the beginning, we con�rmed whether our circuit realizes a reaction:this for part 1. Next, we adjusted the parts and the conditions, for the device to reproduce a satisfactory value suitable for naming the device as a counter:this for part 2. Finally, we discussed what would be appropriate modeling, frequent issue to attack, in order to �nd the best strategy of modeling and wrote how we constructed our model:this for part 3.
In Part1(Deterministic Model,Stochastic Model), we approached the problem in two ways.
・Deteministic model:In this model,chemical reactions are discribed as differential equations and concentration of reaction product can be calu- culated by those of reactants. This model is intutive, simple and hence popular to estimate the result of experiment.
・Stochastic model:The most common formulation of stochastic models for biochemical networks is the chemical master equation (CME). We used Gillepie Algorithm to solve CME.
In Part2(Result), changing measured values of gene copy numbers, strength of pConst, sequence of taRNA and etc. in silico, we estimated in which combination of values the counter outputs a sufficient amount of data.
In Part3(Guide for Modeling), what is modeling, aims of modeling and differernt stochastic approaches and their interrelationShips
First of all, we constructed the deterministic model to estimate the behavior of the counter. In this model, chemical reactions are discribed as differential equations and concentration of reaction product can be calu- culated by those of reactants. This model is intutive, simple and hence popular to estimate the result of experiment. We could therefore get some parameters for modeling of the counter.[→ parameter]
We had simpli�ed the counstruction of mathematical model before described time evolution in which concentrations of mRNAs and proteins change as differential equations. First, we regarded that the reaction between taRNA(transactivating RNA) and crRNA(cis-repressor RNA) in riboregulator is much faster than that of transcription or translation and equilibrium reaction. This diminution of parameters enable us to use the equilibrium constant as a parameter and prevent us from over �tting when we adapt this model to raw data.
We decided to describe mRNAs and the coupling of taRNA and crRNA as stated above. Subscript mean coding sequence of its mRNA. We regarded that affinities of two riboregulators which the counter had is equal. The dissociation constant of equilibrium reaction was therefore shown as following.
Using dissociation constant, concentrations of reaction products such as [mcrcr-σ] could be discribed as function of those of taRNA and mRNA of σ and GFP. We put X, A and B as the total quantity of taRNA, σ and GFP.
Using these equations((3)-(7)) and equilibrium constant, concentrations of binding taRNA or not mRNA coding σ and GFP were discribed as following. These are all of simpli�cations.
Finally, we built up differential equations about concentrations of reaction products including mRNA of σ which has no riboregulator. (It makes positive feedback loop.) We hypothesized that the amount of transcriptional product increasing per unit time is in proportion to the number of promotor if the promotor expressed constitutively and determined by Hill equation when the inducer controled its promotor. We also hypothesized propotional connection between decomposition amount of mRNA and protein and concentration of that. Some of used parameters were cited from references.[1]~[6]
We aimed to determine parameters about $\sigma$ through experiment and used provisonal parameter deter- mined in reference to other promotor.
The amount of sigma mRNA transcribed in positive feedback loop and that of anti sigma mRNA transcribed by IPTG induction to reset the counterwere described as following.
In our project, IPTG induction was aimed at enough production of anti-sigma to reset the counter and the sensitivity of lac promoter was not our main interest. Therefore, we used simple equation,(15) to describe how lac promoter behave. $ P_{lac} $ depend on the concentration of IPTG but we regarded it as a fixed number in this modeling.
Taking into account that translation coincide with transcription in prokaryotes, we hypothesized linear relationship between transcriptional product and the amount of translational product increasing per unit time and that this relationship does not depend on the kind of translational product. We also hypothesized that anti σ combine with σ and form inert matter and reaction velocity of that is proportional to product of these.
Using above-mentioned differential equations, we simulated behavior of the counter by Euler's method.
We explained the parameters of the deterministic model. PoPS (promoter per second) is 0.03\cite{promoter}, so its promoter activity is $0.03/6.0*10^{23}\cdot 1.0\cdot 10^{-15}$[M], 0.051[nM/sec]. The switch point and hill coefficients of pBAD is writen in \cite{pBAD1}. RPU (relative promoter unit) is $\frac{5}{60}\cdot1.7$[nM]. We set the RPU of pLac as 2 when induced. We don't consider the leak expression from pLac.
The average half life of mRNA is 2-5 min\cite{Uri}, so we ser the degradation rate of mRNA as 0.020[/sec]. The half life of GFP is $\infty$\cite{GFP}, so we set the degradation of GFP as 0.0[sec]. The degradation rate of sigma factor[2] is fast. So we set as 0.0001[/sec]. The equilibrium constant of the equations (1)(2) is 80.0[nM]\cite{taRNA}. The number of plasmids copied is 100$\sim$300\cite{plasmid1}\cite{plasmid2} , so we set as 200. The number of ribosomes on a mRNA is about 20 and the time for a ribosome to translate is about 2 minute, so we set the translational rate as 1.43[/sec].
The summary of the parameters of this model is given in Table 1.
The unit of vertical axis is [nM], and that of the horizontal axis is [sec]. We can see that only after the second induction GFP was expressed.
By conducting sensitivity analysis, we can know what parameters have the most influential to the system.
horizontal axis : the pulse length of the arabinose induction
vertical axis:$\displaystyle \frac{\mathrm{fluorescense~after~the~first~induction}}{\mathrm{fluorescense~after~the~second~induction}}$
If there are a lot of molecules, modeling usually uses ordinary differntial equations, but some in vivo reactions involve only a few molecules. For example, transcription involves the cell's genomic DNA which is one copy or plasmids which are about 200 copies \cite{plasmid1}\cite{plasmid2} in a cell of {\em Escherichia coli}. The average size of a cell of {\em E. coli} is about $1.0 \cdot 10^{-15}$[L]\cite{volume}, so the concentration of DNA is about $1.7$[nM] and the concentration of plasmids is about 200 times of it. This is obviously weak. Reactions like this are well affected by fluctuations due to the reactants's limited copy numbers. So, we need to take this fluctuations into our modeling which is derived from stochastic methods. We also introduce delay effect.
First we explain about the Gillespie algorithm which is often used in stochastic simulations. In the Gillespie algorithm, we treated not the concentration of molecules but the number of them. Reactions are also viewed as descrete, essentially instantaneous physical events. What we have to determine when using the Gillespie algorithm is (1) when the next reaction is going to occur and (2) which type of the reaction it will be. Looking more closely at the Gillespie algorithm by the next set of reaction formulas;
Let $n_{1}, n_{2}$, and $n_{3}$ denote the respective copy number of the components $X_{1}, X_{2}$, and $X_{3}$. Notice that they are all integer. First we have to determine how easily each reactions could happen. It depends on the number of components copied. In stochatic simulations, we often determine the paremeter called stochastic rate constant, which is often written as ``$c$''. We assume that each possible combinations of reactant molecules have the same probability $c$ per unit time to react. In other words, $c \cdot\mathrm{dt}$ gives the probability that a particular combination of reactant molecules will react in a short time interval [t,t+dt). We call the stochastic rate constant of a reaction j $c_{j}$. Considering the all combinations of reactant molecules, the probability that the reaction 0 occur in [t,t+dt) is $c_{0}\cdot n_{1} \cdot n_{2}$. We now define the propensity function as the function of which product with dt gives the probability that a particular reaction will occur in the next infinitesimal time dt, which is often written as ``$a$''. Later on, the propensity function of a reaction j is $a_{j}$. Following the equation;
Notice that $c_{j}$ is invariant parameter, but $a_{j}$ changes as the state changes. In the same way, $a_{1} = c_{1} \cdot n_{3}$.
First we answer the question (1) when is the next reaction going to occur? Now, to simplify the situation we assume the situation that only the reaction 0 occurs. Set the time as 0, and define P(t) as the probability that the reaction 0 doesn't occur in [0,t). Then from the definition of $a$,we obtain the equation; P(t+dt) = P(t)$\cdot$(1$-$a$\cdot$dt). (Because the probability that the reaction 0 doesn't occur in [0,t+dt) is the product of the probability that the reaction 0 doesn't occur in [0,t) with the probability that the reaction 0 doesn't occur in [t,t+dt).) Using P(t+dt) = $\displaystyle \mathrm{P(t)} + \frac{d\mathrm{P(t)}}{\mathrm{dt}} \cdot \mathrm{dt}$, we get ;
Because the probability that the reaction0 doesn't occur in a 0 second interval is zero; $P(0)=1$. Solving the above ordinary differential eqaution we get ;
If $r_{1}$ is a uniform number from [0,1], the time of the next reaction should be determined by solving P(t) = $r_{1}$. Using (2), we get t = $\displaystyle -\frac{a_{0}}{\mathrm{log}r_{1}}$.
Now we suppose there is N types of reactions. Let $a_{1},a_{2},\dots,a_{N}$ denote the respective propensity function of reaction 1,2$,\dots,$N. From previous method;
Let dt be so small that we can ignore the term of higher than two orders of dt. The equation(3) becomes;
Solving (4) ($\displaystyle a = \sum_{j=1}^{N}a_{j}$);
Setting $\tau$ as the time of the next reaction, we get;
Second we answer the question (2) what types of the reaction will it be? We determined the time of the next reaction, so what we have left to do is to determine what kind of reaction occured. Some people may feel queer, but in the Gillespie algorithm, first the time of next reaction will be determined, and second the kind of reaction will be determined. It is natural to determine that the probability that the reaction j occurs is $\displaystyle \frac{a_{j}}{a}$. If $r_{2}$ is a uniform number from [0,1], j is the only number that meets below inequations;
In the case $a_{0} \geq a \cdot r_{2}$, the reaction that occured is reaction 0.
Now we can run the Gillespie algorithm by following the next steps.($t_{MAX}$ is the finish time of the simulation.)
1.Initialize the system at $t = 0$ with initial numbers of molecules for each spieces,$n_{0},\cdots ,n_{s}$
2.For each j = 0,1,$\cdots $,r, calculate $a_{j}(n)$ based on the current state n using (21)
3.Calculate the exit rate $\displaystyle a(n) = \sum_{j=0}^{r} a_{j}(n) $.
4.Compute a sample $\tau $ of the time until the next time using (27)
5.Update the time $t = t + \tau$
6.Compute a sample j of the reaction index using (28)
7.Update the state n according to the reaction j.
8.If $t < t_{MAX}$, return to Step 2
Stochastic rate constant can be determined by the parameters we used in the deterministic model (if we modeled the reaction in the determinsitic model) . If there are a lot of reactant molecules, stochastic simulations have to show similar results as those of determinisitic simulations. For this reason, stochastic rate constant, $c$, can be calculated from the chemical reaction rate constant, $k$. See \cite{gillespie1} if you want to know the deriving process. Here we just write the result.
For a unimolecular reaction, $c$ numerically equals to $k$, whereas for a bimolecular reaction, $c$ equals to $\displaystyle \frac{k}{N_{A}V}$ if the species of the reactant molecules are different, or $\displaystyle \frac{2k}{N_{A}V}$ if they are the same. $V$ is the volume of the system and $N_{A}$ is the Avogadro's constant.
However, these results should not be taken to imply that the mathematical forms of the propensity functions are just heuristic extrapolations. The propensity functions are grounded in molecular physics, and the formulas of deterministic chemical kinetics are approximate consequences of the formulas of stochastic chemical kinetics, not the other way around.
The Gillespie algorithm is so clear and useful that it is often used. However, this algorithm is not suitable for describing transcriptions and translations beacuse they are very slow and complex reactions involving many kinds of reactant molecules. If we treat transcription from plasmids as one reaction, assuming the copy number of plasmids as 200, then the propensity function $a$ equals to the stochastic rate constant multiplied by 200 ($200\cdot c$). So it will take about one of a two hundred times of an average transcription time to finish one transcription. Of course, in the time scale of average transcription time it is not a big problem, but this may not be good for simulating, like in our project, the system that uses the time for transcriptions and translations cannot be shortened. We introduce time-delay into the Gillespie algorithm based on \cite{delay1}$\sim$\cite{delay3}. The mathematical rightness of this algorithm is proved in \cite{delay3}. Time-delay means treating reactions as following;
Furthermore, transcriptions and translations are too complex to list up all of the reactions step by step. Therfore it is better to treat them as time-delay than reaction formulas.
Now we begin to model our project, $\sigma$ re-counter. In our model, there are only three reactions: transcription, translation, and an association and disassociation of crRNA and taRNA. We introduce time-delay into only transcription and translation. Then, we explain how we treat these three reactions in general.
First we explain transcription's model\cite{stochastic}. When the RNA polymerase binds to the promoter region, first they take the RNAP/promoter close complex. At this state, the complex can disociate. But with a certain probability, the close complex turn to the open complex which doesn't disociate. After the RNA polymerase and the promoter region take the open complex, a transcription starts. Then the reaction formula of transcription can be described as following's reactions;
combining reaction$3'$ and reaction$3''$, we get;
Second, we refer to the translational model [8]. Similary to the transcrptional model we model as following;
combining reaction2' and reaction2'', we get;
Last, the model of association and disassociation of crRNA and taRNA is a reversible reaction. So we model as following;
We can conclude that reaction formulas of our model are as follows:
In this section we have discussed the improved models of the σ-recounter.
First, we modeled the triple σ-recounter, the expansion of the double counter. Below is the construct of the triple re-counter.
The explanation on this construct is available here. The reaction formulas were established just like as the above-mentioned deterministic model. The result of the modeling of the triple recounter:
The unit of vertical axis is [nM], and that of the horizontal axis is [sec].
Fig 3count result is the result of the modeling of the triple recounter. Although there seems to be a few leak expression, the count is precisely conducted. Here we did not model resetting, because it is obvious from its orthogonality that resetting will be precisely conducted if the pulse length is long enough.
Second, we thought of genetic circuits that would not be affected by the pulse length of the arabinose induction. The current σ re-counter depends much on pulse length; when the pulse length is too long, it would count 2 or more (if there is). (Non-improved version)
induction time: 20000-40000, 60000-80000
If the induction is too long, there will be no difference in the first induction and the second induction; that is, it has no function of counting.
However, by improving this construct a little, our counter would not count more than 1 by a single pulse, as long as the pulse length is long enough (longer than $\tau_{0}$) for it to count. The figure shown below is the improved constructs.
X and Y are substances that bind together to activate pX\&Y promoter.
Before arabinose is induced, pTet and pConst express Y and crRBS-σ. When the arabinose is induced (for longer than time τ0), pBAD becomes activated and TetR and X are expressed. X binds to Y and the transcription of taRNA from pX\&Y occur, which leads to counting. At that time, expression of Y is repressed by TetR and the amount of Y decreases exponentially. Thus, pX\&Y is again repressed, the amount of taRNA decreases, and the counter never counts more than 1. You might be afraid that pX\&Y also begins transcription of taRNA when the induction ends; however, supposed degradation of X is faster than that of TetR, it will not occur. When the induction ends, X first degrades while still a lot of TetR remain and Y is not abundant. Since pTet has a simoidal transcriptional response, the production rate of Y will change little even if the concentration of TetR decrease a little. When TetR degrades so much that it finishes repression of Y, most of X have already decomposed, and pX\&Y will not be activated to begin transcription of taRNA.
We modeled this construct to test if it can be realized. We did not modeled resetting this time, either.
The inductions were modeled to be conducted just the same as non-improved version. Although pulse length is long, counts are precisely done. Thus, theoretically, the counter independent of the pulse length is suggested to be available. Only thing we have to do is to research for the substances that satisfy these conditions!
What is modeling
The model should be sufficiently detailed and precise so that it can in principle be used to simulate the bevavior of the system on a computer.
In the context of molecular cell biology, a model may describe the mechanisms involved intranscription, translation, gene regulation, cellular signaling, DNA damage and repair processes, homeostatic processes, the cell cycle, or apotosis. Indeed any biochemical mechanism of interest can, in principle, be modelled. At a higher level, modeling may be used to describe the functioning of a tissue, organ, or even an entire organism. At still higher levels, models can be used to describe the behavior and time evolution of populations of individual organisms.
The first issue to confront when embarking on a modeling project is to describe on exactly which features to include in the model, and in particular, the level of detail model is intended to capture. Interacting with other cells and/or its environment, the cell realizes four key functions: growth, proliferation, apotosis, and defferentiation. The processes that realize these functions of a cell can be further organized into three processes levels: gene regulation, signaltransduction and metabolism. So, a model of an entire organism is unlikely to describe the detailed functioning of every individual cell, but a model of a cell is likely to include a variety of very detailed description of key cellular processes. Even then, however, a model of a cell is unlikely to contain details of every single gene and protein.
Indeed, really accurate modeling of the process would require a model far more detalied and complex than most biologists would be comfortable with, using molecular dynamic simulations that explicitly manage the position and momentum of every molecule in the system.
The "art" of building a good model is to capture the essential features of the biology without burdening the model with non-essential details. Every model is to some extent a simplification of the biology, but models are valuable because they take ideas that might have been expressed verbally or diagrammatically and make them more explicit, so that they can begin to be undestood in a quantitative rather than purely qualitative way.
Aims of modeling
The features of a model depend very much on the aims of the modeling excercise. We therefore need to consider why people model and what they hope to achieve by so doing. Often the most basic aim is to make clear the current state of knowledge regarding a particular system, by attempting to be precise about the elements involved and the interactons between them. Doing this can be a particularly effective way of highlighting gaps in understanding. In addtion, having a detailed model of a system allows people to test that their understanding of a system is correct, by seeing if the implications of their models are consistent with observed experimental data. In practice, this model validation stage is central to the systems biology approach. However this work will often represent only the initial stage of the modeling process. Once people have a model they are happy with, they often want to use their models predictively, by conducting "virtual experiments" that might be difficult, time-consuming, or impossible to do in the lab. Such experiments may uncover important indirect relationships between the model components that would be hard to predict otherwise. An additional goal of modern biological modeling is to pool a number of samll models of well-understood mechanisms into a large model in order to investigate the effect of interactions between the model components. Models can also be extremely useful for informing the design and analysis of complex biological experiments.
In summary, modeling and computer simulation are becoming increasingly important in post-genomic biology for integrating knowledge and experimental data and making testable predictions about the behavior of complex biological systems.
Stochastic Approaches
The most common formulation of stochastic models for biochemical networks is the chemical master equation (CME). The analytical nature of the early stochastic approaches was highly complicated and, in some cases, intractable so that they received little attention in the biochemical community. Later, the situation changed with the increasing computational power of modern computers. And finally Gillespie presented an ground-breaking algorithm for numerically generating sample trajectories of the abundances of chemical species in chemical reaction networks. The so-called "stochastic simulation algorithm," or "Gillespie algorithm," can easily be implemented in any programming or scripting language that has a pseudorandom number generator. Several software packages implementing the algorithm have been developed. Differernt stochastic approaches and their interrelationchips are depicted in Figure.
For large biochemical systems, with many species and reactions, stochasitc simulations (based on the original Gillespie algorithm) become computationally demanding. Recent years have seen a large interest in improving the efficiency/speed of stochastic simulations by modification/approximation of the original Gillespie algorithm. These improvements include the "next reaction" method of Gibson and Bruck, the "τ-leap" method and its various improvements and generalizations and the "maximal time step method", which combines the next rection and the τ-leap methods.
While stochastic simulations sre a practical way to realize the CME, analytical approxinmations offer more insihgts into the influence of noise on cell function. Formally, the CME is a continuous-time discrete-state Markov process. For gaining intuitive insight and a quick characteriztion of fluctuations in biochemical networks, the CME is usually approximated analytically in different ways, including the frequently used chemical Langevin equation (CLE), the linear noise approximation (LNA), and the two-moment approximation (2MA).
The traditional Langevin approach is based on the assumption that the time-rate of abundance (copy number or concentration) or the flux of a component can be decomposed into a deterministic flux and a Lngevin moise term, which is a Gaussian (white noise) process with zero mean and amplitude determined by the system dynamics. This separation of noise from system dyanmics may be a reasonable assumption for external noise that arises from the interaction of the system with the other systems (such as the environment), but cannot be assumed for the internal noise that arises from within the system. Internal noise is not something that can be isolated from the system, because it results from the descrete nature of the underlying molecular events. Any noise term in the model must be derived from the system dynamics and cannot be presupposed in an ad hoc manner. However, the CLE does not suffer from above criticism because because Gillespie derived it from the CME description. The CLE allows much faster simulations compared to the exact stochastic simulation algorithm (SSA) and its variants. The CLE is a stochastic diiferent equation (dealing directly with random variables rather than moments) and has no direct way of representing the mean and (co)ariantce and the coupling between the two. That does not imply tha CLE, like the LNA, which has the same mean as the solution of deterministic model, ignores the coupling.
Markov processes form the basis of the vast majority of stochastic models of dynamical systems. At the center of a stochastic analysis is the Chapman-Kolmogorov equation (CKE), which describes the evolution of a Markov process over time. From the CKE stem three equations of practical importance: the master equation for jump Markov processes, the Fokker-Planck equation for continuous Markov processes, and the differential Chapman-Kolmogorov equation (dCKE) for processes made up both the continuous and jump parts.
Stochastic Formulation and Markov Process
Since the occurrence of reactions involves discrete and random events at the microscopic level, it is impossible to deterministically predict the progress of recations interms of the macroscopic variables (obsevables) N(t) and Z(t). To acount for this uncertainty, one of the observables N()Z()
Our goal is to determine how the process N(t) of copy numbers evolves in time. Starting at time t=0 from some initial state N(0), every sample path of the process remains in state N(0) for a random amount of time W\_1 until the occurrence of a reaction takes process to a new state N(W\_1); it remains in state N(W\_1) for another random amount of time W\_2 until the occurrence of another reaction takes the process to a new state N(W\_1+W\_2), and so on. In other words, the time-dependent copy number N(t) is a jump process.
The stochasitc process N(t) is characterized by a collection of state probabilities and transition probabilities. The state probability P(n,t)=Pr[N(t)=n] is the probability that the process N(t) is the state n at a time t. The transition probability Pr[N(t\_0+t)=n|N(t\_0)=m] is the conditional probability that process N(t) has moved from state m to state n during the time interval [t\_0,t\_0+t]. The analysis of a stochastic process becomes greatly simplified when the above transition probability depends on (i) the starting state m but not on the states before time t\_0 and (ii) the interval length t but not on the. Property (i) is the well-known Markov process. The process holding property (ii) is said to be homogeneous process.
[1]D.J.Wilkinson.Stochastic Modelling for Systems Biology.Mathematical \& Computational Biology. Chapman \& Hall/CRC, London, Apr. 2006. ISBN 1584885408
[2]Mukhtar Ullah \& Olaf Wolkenhauer Stochastic Approaches for Systems Biology.
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[15] Part:BBa I13453 ( We �nally accessed on 2014/8/20)
[16] iGEM Kyoto 2010
[17] pSB1A2 ( We �nally accessed on 2014/8/20)
[18] pSB1C3 ( We �nally accessed on 2014/8/20)