CG920 Genomics
Lesson 12
Systems Biology Tools
Model organisms, PCR and PCR Primer Design
Jan Hejátko
Functional Genomics and Proteomics of Plants,
Mendel Centre for Plant Genomics and Proteomics,
Central European Institute of Technology (CEITEC), Masaryk University, Brno
hejatko@sci.muni.cz, www.ceitec.muni.cz
 Literature sources for Chapter 12:
 Wilt, F.H., and Hake, S. (2004). Principles of Developmental Biology. (New York ; London: W. W.
Norton)
 Roscoe B. Jackson Memorial Laboratory., and Green, E.L. (1966). Biology of the laboratory
mouse. (New York: Blakiston Division) http://www.informatics.jax.org/greenbook/index.shtml
 Eden, E., Navon, R., Steinfeld, I., Lipson, D., and Yakhini, Z. (2009). GOrilla: a tool for discovery
and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48.
 The Arabidopsis Genome Initiative. (2000). Analysis of the genome sequence of the flowering
plant Arabidopsis thaliana. Nature 408, 796-815.
 Gregory, S.G., Sekhon, M., Schein, J., Zhao, S., Osoegawa, K., Scott, C.E., Evans, R.S.,
Burridge, P.W., Cox, T.V., Fox, C.A., Hutton, R.D., Mullenger, I.R., Phillips, K.J., Smith, J., Stalker,
J., Threadgold, G.J., Birney, E., Wylie, K., Chinwalla, A., Wallis, J., Hillier, L., Carter, J., Gaige, T.,
Jaeger, S., Kremitzki, C., Layman, D., Maas, J., McGrane, R., Mead, K., Walker, R., Jones, S.,
Smith, M., Asano, J., Bosdet, I., Chan, S., Chittaranjan, S., Chiu, R., Fjell, C., Fuhrmann, D., Girn,
N., Gray, C., Guin, R., Hsiao, L., Krzywinski, M., Kutsche, R., Lee, S.S., Mathewson, C., McLeavy,
C., Messervier, S., Ness, S., Pandoh, P., Prabhu, A.L., Saeedi, P., Smailus, D., Spence, L., Stott,
J., Taylor, S., Terpstra, W., Tsai, M., Vardy, J., Wye, N., Yang, G., Shatsman, S., Ayodeji, B., Geer,
K., Tsegaye, G., Shvartsbeyn, A., Gebregeorgis, E., Krol, M., Russell, D., Overton, L., Malek, J.A.,
Holmes, M., Heaney, M., Shetty, J., Feldblyum, T., Nierman, W.C., Catanese, J.J., Hubbard, T.,
Waterston, R.H., Rogers, J., de Jong, P.J., Fraser, C.M., Marra, M., McPherson, J.D., and Bentley,
D.R. (2002). A physical map of the mouse genome. Nature 418, 743-750.
 Benitez, M. and Hejatko, J. Dynamics of cell-fate determination and patterning in the vascular
bundles of Arabidopsis thaliana (submitted)
Literature
2
 Tools of systems biology
 Gene ontology analysis
 Molecular Regulatory Networks Modeling
 Model organisms
 Mus musculus
 Arabidopsis thaliana
 Selected methods of molecular biology
 Preparation of transgenic organisms
 PCR
 Design and preparation of primers (Dr. Hana Konečná)
Outline
3
 Tools of systems biology
 Gene ontology analysis
Outline
4
Results of –omics Studies vs
Biologically Relevant Conclusions
□ Results of –omics studies are representred by huge amount
of data, e.g. differential gene expression. But how to get any
biologically relevant conclusions?
gene locus sample_1 sample_2 status value_1 value_2 log2(fold_change) test_stat p_value q_value significant
AT1G07795 1:2414285-2414967 WT MT OK 0 1,1804 1.79769e+308
1.79769e+
308 6.88885e-05
0,00039180
1 yes
HRS1 1:4556891-4558708 WT MT OK 0 0,696583 1.79769e+308
1.79769e+
308 6.61994e-06
4.67708e-
05 yes
ATMLO14 1:9227472-9232296 WT MT OK 0 0,514609 1.79769e+308
1.79769e+
308 9.74219e-05
0,00053505
5 yes
NRT1.6 1:9400663-9403789 WT MT OK 0 0,877865 1.79769e+308
1.79769e+
308 3.2692e-08
3.50131e-
07 yes
AT1G27570 1:9575425-9582376 WT MT OK 0 2,0829 1.79769e+308
1.79769e+
308 9.76039e-06 6.647e-05 yes
AT1G60095
1:22159735-
22162419 WT MT OK 0 0,688588 1.79769e+308
1.79769e+
308 9.95901e-08
9.84992e-
07 yes
AT1G03020 1:698206-698515 WT MT OK 0 1,78859 1.79769e+308
1.79769e+
308 0,00913915 0,0277958 yes
AT1G13609 1:4662720-4663471 WT MT OK 0 3,55814 1.79769e+308
1.79769e+
308 0,00021683 0,00108079 yes
AT1G21550 1:7553100-7553876 WT MT OK 0 0,562868 1.79769e+308
1.79769e+
308 0,00115582 0,00471497 yes
AT1G22120 1:7806308-7809632 WT MT OK 0 0,617354 1.79769e+308
1.79769e+
308 2.48392e-06
1.91089e-
05 yes
AT1G31370
1:11238297-
11239363 WT MT OK 0 1,46254 1.79769e+308
1.79769e+
308 4.83523e-05
0,00028514
3 yes
APUM10
1:13253397-
13255570 WT MT OK 0 0,581031 1.79769e+308
1.79769e+
308 7.87855e-06
5.46603e-
05 yes
AT1G48700
1:18010728-
18012871 WT MT OK 0 0,556525 1.79769e+308
1.79769e+
308 6.53917e-05
0,00037473
6 yes
AT1G59077
1:21746209-
21833195 WT MT OK 0 138,886 1.79769e+308
1.79769e+
308 0,00122789 0,00496816 yes
AT1G60050
1:22121549-
22123702 WT MT OK 0 0,370087 1.79769e+308
1.79769e+
308 0,00117953 0,0048001 yes
Didi et al., unpublished
AT4G15242 4:8705786-8706997 WT MT OK 0,00930712 17,9056 10,9098 -4,40523 1.05673e-05 7.13983e-05 yes
AT5G33251
5:12499071-
12500433 WT MT OK 0,0498375 52,2837 10,0349 -9,8119 0 0yes
AT4G12520 4:7421055-7421738 WT MT OK 0,0195111 15,8516 9,66612 -3,90043 9.60217e-05 0,000528904yes
AT1G60020
1:22100651-
22105276 WT MT OK 0,0118377 7,18823 9,24611 -7,50382 6.19504e-14 1.4988e-12 yes
AT5G15360 5:4987235-4989182 WT MT OK 0,0988273 56,4834 9,1587 -10,4392 0 0yes
Excample of an output of transcriptional profiling study using Illumina sequencing performed in
our lab. Shown is just a tiny fragment of the complete list, copmprising about 7K genes
revealing differential expression in the studied mutant
5
Molecular Regulatory
Networks Modeling
□ Vascular tissue as a developmental model for GO analysis and MRN
modeling
Lehesranta etal., Trends in Plant Sci (2010)
6
Hormonal Regulation in Plant Biotechnology
WT hormonal mutant
Hormonal Control Over Vascular
Tissue Development
□ Plant Hormones Regulate Lignin Deposition in Plant Cell
Walls and Xylem Water Conductivity
WT mutant
lignified cell walls
Water Conductivity
WT hormonal mutants
7
Hormonal Regulation in Plant Biotechnology
WT hormonal mutant
Hormonal Control Over Vascular
Tissue Development
□ Transcriptional profiling via RNA sequencing
mRNA
Sequencing by Illumina and
number of transcripts determination
mRNA
cDNA cDNA
8
Hormonal Regulation in Plant Biotechnology
Results of –omics Studies vs
Biologically Relevant Conclusions
□ Transcriptional profiling yielded more then 7K differentially
regulated genes…
gene locus sample_1 sample_2 status value_1 value_2 log2(fold_change) test_stat p_value q_value significant
AT1G07795 1:2414285-2414967 WT MT OK 0 1,1804 1.79769e+308
1.79769e+
308 6.88885e-05
0,00039180
1 yes
HRS1 1:4556891-4558708 WT MT OK 0 0,696583 1.79769e+308
1.79769e+
308 6.61994e-06
4.67708e-
05 yes
ATMLO14 1:9227472-9232296 WT MT OK 0 0,514609 1.79769e+308
1.79769e+
308 9.74219e-05
0,00053505
5 yes
NRT1.6 1:9400663-9403789 WT MT OK 0 0,877865 1.79769e+308
1.79769e+
308 3.2692e-08
3.50131e-
07 yes
AT1G27570 1:9575425-9582376 WT MT OK 0 2,0829 1.79769e+308
1.79769e+
308 9.76039e-06 6.647e-05 yes
AT1G60095
1:22159735-
22162419 WT MT OK 0 0,688588 1.79769e+308
1.79769e+
308 9.95901e-08
9.84992e-
07 yes
AT1G03020 1:698206-698515 WT MT OK 0 1,78859 1.79769e+308
1.79769e+
308 0,00913915 0,0277958 yes
AT1G13609 1:4662720-4663471 WT MT OK 0 3,55814 1.79769e+308
1.79769e+
308 0,00021683 0,00108079 yes
AT1G21550 1:7553100-7553876 WT MT OK 0 0,562868 1.79769e+308
1.79769e+
308 0,00115582 0,00471497 yes
AT1G22120 1:7806308-7809632 WT MT OK 0 0,617354 1.79769e+308
1.79769e+
308 2.48392e-06
1.91089e-
05 yes
AT1G31370
1:11238297-
11239363 WT MT OK 0 1,46254 1.79769e+308
1.79769e+
308 4.83523e-05
0,00028514
3 yes
APUM10
1:13253397-
13255570 WT MT OK 0 0,581031 1.79769e+308
1.79769e+
308 7.87855e-06
5.46603e-
05 yes
AT1G48700
1:18010728-
18012871 WT MT OK 0 0,556525 1.79769e+308
1.79769e+
308 6.53917e-05
0,00037473
6 yes
AT1G59077
1:21746209-
21833195 WT MT OK 0 138,886 1.79769e+308
1.79769e+
308 0,00122789 0,00496816 yes
AT1G60050
1:22121549-
22123702 WT MT OK 0 0,370087 1.79769e+308
1.79769e+
308 0,00117953 0,0048001 yes
Ddii et al., unpublished
AT4G15242 4:8705786-8706997 WT MT OK 0,00930712 17,9056 10,9098 -4,40523 1.05673e-05 7.13983e-05 yes
AT5G33251
5:12499071-
12500433 WT MT OK 0,0498375 52,2837 10,0349 -9,8119 0 0yes
AT4G12520 4:7421055-7421738 WT MT OK 0,0195111 15,8516 9,66612 -3,90043 9.60217e-05 0,000528904yes
AT1G60020
1:22100651-
22105276 WT MT OK 0,0118377 7,18823 9,24611 -7,50382 6.19504e-14 1.4988e-12 yes
AT5G15360 5:4987235-4989182 WT MT OK 0,0988273 56,4834 9,1587 -10,4392 0 0yes
9
Gene Ontology Analysis
□ One of the possible approaches is to study gene ontology, i.e.
previously demonstrated association of genes to biological
processes
Ddii et al., unpublished
10
Gene Ontology Analysis
□ Several tools allow statistical evaluation of enrichment for
genes associated with specific processes
Eden et al., BMC Biinformatics (2009)
One of such recent and very useful tools is Gorilla software, freely available at
http://cbl-gorilla.cs.technion.ac.il/.
11
Gene Ontology Analysis
□ Several tools allow statistical evaluation of enrichment for
genes associated with specific processes
12
Gene Ontology Analysis
□ Several tools allow statistical evaluation of enrichment for
genes associated with specific processes
13
Gene Ontology Analysis
□ Several tools allow statistical evaluation of enrichment for
genes associated with specific processes
14
Gene Ontology Analysis
□ Several tools allow statistical evaluation of enrichment for
genes associated with specific processes
15
Gene Ontology Analysis
□ Several tools allow statistical evaluation of enrichment for
genes associated with specific processes
16
 Tools of systems biology
 Gene ontology analysis
 Molecular Regulatory Networks Modeling
Outline
17
□ Vascular tissue as a developmental model for MRN modeling
Benitez and Hejatko, submitted
Molecular Regulatory
Networks Modeling
18
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
□ Literature search for published data and creating small database
Interaction Evidence References
A-ARRs –| CK
signaling
Double and higher order type-A ARR mutants show
increased sensitivity to CK.
Spatial patterns of A-type ARR gene expression and
CK response are consistent with partially redundant
function of these genes in CK signaling.
A-type ARRs decreases B-type ARR6-LUC.
Note: In certain contexts, however, some A-ARRs
appear to have effects antagonistic to other A-ARRs.
[27]
[27]
[13]
[27]
AHP6 –| AHP ahp6 partially recovers the mutant phenotype of the
CK receptor WOL.
Using an in vitro phosphotransfer system, it was
shown that, unlike the AHPs, native AHP6 was
unable to accept a phosphoryl group. Nevertheless,
AHP6 is able to inhibit phosphotransfer from other
AHPs to ARRs.
[9]
[9]
Benitez and Hejatko, submitted
19
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
□ Formulating logical rules defining the model dynamics
Network node Dynamical rule
CK 2 If ipt=1 and ckx=0
1 If ipt=1 and ckx=1
0 else
CKX 1 If barr>0 or arf=2
0 else
AHKs ahk=ck
AHPs 2 If ahk=2 and ahp6=0 and aarr=0
1 If ahk=2 and (ahp6+aarr<2)
1 If ahk=1 and ahp6<1
0 else
B-Type ARRs 1 If ahp>0
0 else
A-Type ARRs 1 If arf<2 and ahp>0
0 else
Benitez and Hejatko, submitted
20
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
□ Specifying mobile elements and their model behaviour
21
According to experimental evidence for the system under study, the hormone IAA, the peptide
TDIF, and the microRNA MIR165/6 are able to move among the cells. In the case of TDIF and
MIR165/6, the mobility is defined as diffusion and is given by the following equation:
g(t+1)T[i]= H(g(t)[i]+ D (g(t)[i+1]+g(t)[i-1] – N(g(t)[i]))-b) (2),
where g(t)T[i] is the total amount of TDIF or MIR165 in cell (i). D is a parameter that
determines the proportion of g that can move from any cell to neighboring ones and is
correlated to the diffusion rate of g. b is a constant corresponding to a degradation term. H is a
step function that converts the continuous values of g into a discrete variable that may attain
values of 0, 1 or 2. N stands for the number of neighbors in each cell. Boundary conditions are
zero-flux. In the case of IAA, the mobility is defined as active transport dependent on the radial
localization of the PIN efflux transporters and is defined by the equation:
iaa(t+1)T[i]=Hiaa(iaa(t)[i]+Diaa(pin(t)[i+1])(iaa(t)[i+1])+Diaa(pin(t)[i-1])(iaa(t)[i-
1])−N(Diaa)(pin(t)[i])(iaa(t)[i])−biaa) (3),
where Diaa is a parameter that determines the proportion of IAA that can be transported
among cells. The transport depends on the presence of IAA and PIN in the cells and biaa
corresponds to a degradation term. As in equation 2, H is a step function that converts the
continuous values to discrete ones and N stands for the number of neighbors in each cell.
Boundary conditions for IAA motion are also zero-flux.
22
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
□ Preparing the first version of the model and its testing
23
The proposed model considers data that we identified and evaluated through an
extensive search (up to January 2012). It takes into account molecular interactions,
hormonal and expression patterns, and cell-to-cell communication processes that
have been reported to affect vascular patterning in the bundles of Arabidopsis. The
model components and interactions are graphically presented in the figure above. In
the network model, nodes stand for molecular elements regulating one another’s
activities. Most of the nodes can take only 1 or 0 values (light gray nodes in the
figure), corresponding to “present” or “not present,” respectively. Since the formation
of gradients of hormones and diffusible elements may have important consequences
in pattern formation, mobile elements TDIF and MIR, as well as members of the CK
and IAA signaling systems, can take 0, 1 or 2 values (dark gray nodes in the figure
above) Benitez and Hejatko, submitted.
24
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
□ Specifying of missing interactions via informed predictions
Interaction Evidence References
A-ARRs –| CK
signaling
Double and higher order type-A ARR mutants show increased
sensitivity to CK.
Spatial patterns of A-type ARR gene expression and CK
response are consistent with partially redundant function of
these genes in CK signaling.
A-type ARRs decreases B-type ARR6-LUC.
Note: In certain contexts, however, some A-ARRs appear to
have effects antagonistic to other A-ARRs.
[27]
[27]
[13]
[27]
AHP6 –| AHP ahp6 partially recovers the mutant phenotype of the CK
receptor WOL.
Using an in vitro phosphotransfer system, it was shown that,
unlike the AHPs, native AHP6 was unable to accept a
phosphoryl group. Nevertheless, AHP6 is able to inhibit
phosphotransfer from other AHPs to ARRs.
[9]
[9]
CK → PIN7 radial
localization
Predicted interaction (could be direct or indirect)
Informed by the following data:
During the specification of root vascular cells in Arabidopsis
thaliana, CK regulates the radial localization of PIN7.
Expression of PIN7:GFP and PIN7::GUS is upregulated by CK
with no significant influence of ethylene.
In the root, CK signaling is required for the CK regulation of
PIN1, PIN3, and PIN7. Their expression is altered in wol, cre1,
ahk3 and ahp6 mutants.
[18]
[18,20]
[19]
CK→ APL Predicted interaction (could be direct or indirect)
Consistent with the fact that APL overexpression prevents or
delays xylem cell differentiation, as does CKs.
Partially supported by microarray data and phloem-specific
expression patterns of CK response factors.
[21]
(TAIR,
ExpressionSet:1
005823559,
[22])
25
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
□ Preparing the next version of the model and its testing
Benitez and Hejatko, PlosONE, 2013
In comparison to the model shown on slide 23,
the final version of the model contains the
predicted interactions (dashed lines).
26
Signaling and Hormonal Regulation of Plant Development
□ Good model should be able to simulate reality
Benitez and Hejatko, PlosONE, 2013
Molecular Regulatory
Networks Modeling
27
Signaling and Hormonal Regulation of Plant Development
□ Formulating equations describing the relationships in the model
Molecular Regulatory
Networks Modeling
Static nodes: gn(t+1)=Fn(gn1(t),gn2(t),..., gnk(t))
Mobile nodes: g(t+1)T [i]= H(g(t) [i]+ D (g(t) [i+1]+g(t) [i-1] – N(g(t) [i]))-b)
state in the time t+1
state in the time tlogical rule function
state in the time t+1
Amount if TDIF or MIR165 in cell i
proportion of movable element
constant corresponding to a degradation term
28
Signaling and Hormonal Regulation of Plant Development
□ Good model should be able to simulate reality
Benitez and Hejatko, submitted
Molecular Regulatory
Networks Modeling
Static nodes: gn(t+1)=Fn(gn1(t),gn2(t),..., gnk(t))
Mobile nodes: g(t+1)T [i]= H(g(t) [i]+ D (g(t) [i+1]+g(t) [i-1] – N(g(t) [i]))-b)
29
The initial conditions specify the initial state of some of the network elements (figure
above) and are the following :
I) In the procambial position (central compartment), CK is initially available and there is an
initial and sustained IAA input or self-upregulation. This condition is supported by several
lines of evidence. Also HB8, a marker of early vascular development that has been found
in preprocambial cells, is assumed to be initially present at this position. These conditions
are not fixed, however. After the initial configuration, all the members of the CK and IAA
signaling pathways, as well as HB8, can change their states according to the logical rules.
II) In the xylem and phloem positions, it is assumed that no element is initially active
except for the CK signaling pathway and TDIF, both in the phloem position.The level of
expression for a given node is represented by a discrete variable g and its value at a time
t+1 depends on the state of other components of the network (g1, g2, ..., gN) at a
previous time unit. The state of every gene g therefore changes according to:
gn(t+1)=Fn(gn1(t),gn2(t),..., gnk(t)) (1).
30
In this equation, gn1, gn2,…, gnk are the regulators of gene gn and Fn is a discrete
function known as a logical rule (logical rules are grounded in available experimental data,
for example see slide 20). Given the logical rules, it is possible to follow the dynamics of
the network for any given initial configuration of the nodes expression state. One of the
most important traits of dynamic models is the existence of steady states in which the
entire network enters into a selfsustained configuration of the nodes state. It is thought that
in developmental systems such self-sustained states correspond to particular cell types.
According to experimental evidence for the system under study, the hormone IAA, the
peptide TDIF, and the microRNA MIR165/6 are able to move among the cells. In the case
of TDIF and MIR165/6, the mobility is defined as diffusion and is given by the following
equation:
g(t+1)T[i]= H(g(t)[i]+ D (g(t)[i+1]+g(t)[i-1] – N(g(t)[i]))-b) (2),
where g(t)T[i] is the total amount of TDIF or MIR165 in cell (i). D is a parameter that
determines the proportion of g that can move from any cell to neighboring ones and is
correlated to the diffusion rate of g. b is a constant corresponding to a degradation term. H
is a step function that converts the continuous values of g into a discrete variable that may
attain values of 0, 1 or 2. N stands for the number of neighbors in each cell.
31
Boundary conditions are zero-flux. In the case of IAA, the mobility is defined as active
transport dependent on the radial localization of the PIN efflux transporters and is defined
by the equation:
iaa(t+1)T[i]=Hiaa(iaa(t)[i]+Diaa(pin(t)[i+1])(iaa(t)[i+1])+Diaa(pin(t)[i-1])(iaa(t)[i-
1])−N(Diaa)(pin(t)[i])(iaa(t)[i])−biaa) (3),
where Diaa is a parameter that determines the proportion of IAA that can be transported
among cells. The transport depends on the presence of IAA and PIN in the cells and biaa
corresponds to a degradation term. As in equation 2, H is a step function that converts the
continuous values to discrete ones and N stands for the number of neighbors in each cell.
Boundary conditions for IAA motion are also zero-flux.
Using the logical rules, equations 1–3, and a broad range of parameter values (not shown
here), it is possible fully to reproduce the results and analyses reported in the following
sections (see the figure above for the simulation time course).
32
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
Benitez and Hejatko, submitted
□ The good model should be able to simulate reality
Another representation of the distinct expression profiles in the individual vascular bundle
compartments (phloem, procambium and xylem).
33
Signaling and Hormonal Regulation of Plant Development
Molecular Regulatory
Networks Modeling
Benitez and Hejatko, submitted
□ Simulation of mutants
34
Signaling and Hormonal Regulation of Plant Development
 Tools of systems biology
 Gene ontology analysis
 Molecular Regulatory Networks Modeling
 Model organisms
 Mus musculus
Outline
35
 Low requirements for area
 Relatively large number of offspring (3-14, 6-8 on
average)
 Genome size is close to the size of human genome
(about 3000 Mbp), the number of genes as well (about
24K)
 20 chromosomes (19+1)
 Suitable for a wide range of physiological experiments
(anatomical and physiological similarity to human)
 Possibility to obtain (quite easily) KO mutants and
transgenic lines
Mus musculus
house mouse
More info about mouse at
http://www.informatics.jax.org/greenbook/index.shtml.
36
 Genome known since 2002
(http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/mouse/)
Mus musculus
house mouse
37
 Tools of systems biology
 Gene ontology analysis
 Molecular Regulatory Networks Modeling
 Model organisms
 Mus musculus
 Arabidopsis thaliana
Outline
38
 Low requirements for cultivation area
Arabidopsis thaliana
mouse-ear cress
 High number of seeds (20.000 per plant and more)
 Small and compact genome, (125 MBp, about
25.000 genes, average size 3 kb)
 5 chromosomes
 Suitable for wide range od physiological experiments
 High natural variability (approximately 750 ecotypes
(Nottingham Arabidopsis Seed Stock Centre))
Columbia 0 Landsberg 0 Wassilewskija 0
http://seeds.nottingham.ac.uk/
39
Arabidopsis thaliana
mouse-ear cress
 Genome known since 2000 (http://www.arabidopsis.org/)
40
 Tools of systems biology
 Gene ontology analysis
 Molecular Regulatory Networks Modeling
 Model organisms
 Mus musculus
 Arabidopsis thaliana
 Selected methods of molecular biology
 Preparation of transgenic organisms
Outline
41
42
Individula ICM cells of the embryo could be isolated and later re-introgressed into the new
embryo. These ICM cells are called embryonic stem (ES) cells. It is very important technique
that allows production of transgenic mice.
The isolated ES cells are transformed via foreign DNA construct and it is injected within the
embryo. The transformed cell becomes a part of the embryo and might result into formation of
different tissue types, among them the spermatogonia or oogonia. i.e. the tissue that provides
progenitor for sperm or egg cells in the resulting chimera. Thus, the progeny of those
chimeras will inherit the modified cell with certain probability and these individuals will carry
the transgene in every cell of their body. Thus, the trangenic mice will be produced.
This is very important mainly with regard of the knockout mutant (K.O.) production. In the
modified ES, the genes might be specifically eliminated via DNA recombination. In that way,
function of many of the mice genes was identified.
E.g. the gene NODAL is expressed in the anterior portion of the primitive streak that is
equivalent to the Hensen’s node. nodal/nodal embryos are lethal, they do not undergo
gastrulation and from almost no mesoderm.
43
Transformation of Arabidopsis by
Agrobacterium tumefaciens
44
Transformation of Arabidopsis by
Agrobacterium tumefaciens
Transfer of bacterial DNA into plant cells
45
Transformation by cocultivation of leaf discs
46
Transformation by cocultivation of calluses
47
Transformation by biolistic delivery of DNA
48
Transformation of inflorescence
http://www.bch.msu.edu/pamgreen/green.htm
49
Transformation of inflorescence
http://www.bch.msu.edu/pamgreen/green.htm
50
 Tools of systems biology
 Gene ontology analysis
 Molecular Regulatory Networks Modeling
 Model organisms
 Mus musculus
 Arabidopsis thaliana
 Selected methods of molecular biology
 Preparation of transgenic organisms
 PCR
 Design and preparation of primers (Dr. Hana Konečná)
Outline
51