CG020 Genomika Přednáška 12 Nástroje systémové biologie Modelové organismy, PCR a zásady navrhování primerů Jan Hejátko Funkční genomika a proteomika rostlin, Mendelovo centrum genomiky a proteomiky rostlin, Středoevropský technologický institut (CEITEC), Masarykova univerzita, Brno hejatko@sci.muni.cz, www.ceitec.muni.cz §Zdrojová literatura §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) Genomika 12 FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus §Arabidopsis thaliana § §Vybrané metody molekulární biologie §Příprava transgenních organismů §PCR §Design a příprava primerů (Dr. Hana Konečná) § Osnova FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie Osnova FGP_logo_color 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 0,000391801 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 0,000535055 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 0,000285143 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 0,000374736 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 0 yes AT4G12520 4:7421055-7421738 WT MT OK 0,0195111 15,8516 9,66612 -3,90043 9.60217e-05 0,000528904 yes 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 0 yes 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. FGP_logo_color Molecular Regulatory Networks Modeling □Vascular tissue as a developmental model for GO analysis and MRN modeling Lehesranta etal., Trends in Plant Sci (2010) FGP_logo LMFR_con 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 FGP_logo LMFR_con 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 http://www.illumina.com/Images/products/HiSeq2000_Workflow.jpg mRNA cDNA cDNA FGP_logo LMFR_con 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 0,000391801 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 0,000535055 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 0,000285143 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 0,000374736 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 0 yes AT4G12520 4:7421055-7421738 WT MT OK 0,0195111 15,8516 9,66612 -3,90043 9.60217e-05 0,000528904 yes 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 0 yes 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. FGP_logo_color 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 FGP_logo_color 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/. FGP_logo_color Gene Ontology Analysis □Several tools allow statistical evaluation of enrichment for genes associated with specific processes FGP_logo_color Gene Ontology Analysis □Several tools allow statistical evaluation of enrichment for genes associated with specific processes FGP_logo_color Gene Ontology Analysis □Several tools allow statistical evaluation of enrichment for genes associated with specific processes FGP_logo_color Gene Ontology Analysis □Several tools allow statistical evaluation of enrichment for genes associated with specific processes FGP_logo_color Gene Ontology Analysis □Several tools allow statistical evaluation of enrichment for genes associated with specific processes FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí Osnova □Vascular tissue as a developmental model for MRN modeling Benitez and Hejatko, submitted Molecular Regulatory Networks Modeling FGP_logo LMFR_con 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 FGP_logo LMFR_con 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 FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development Molecular Regulatory Networks Modeling □Specifying mobile elements and their model behaviour 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)(p in(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. FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development CREATOR: gd-jpeg v1.0 (using IJG JPEG v62), default quality Molecular Regulatory Networks Modeling □Preparing the first version of the model and its testing 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. FGP_logo LMFR_con 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:1005823559, [22]) FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development Molecular Regulatory Networks Modeling □Preparing the next version of the model and its testing Benitez and Hejatko, submitted In comparison to the model shown on slide 21, the final version of the model contains the predicted interactions (dashed lines). FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development □Good model should be able to simulate reality Benitez and Hejatko, submitted Molecular Regulatory Networks Modeling CREATOR: gd-jpeg v1.0 (using IJG JPEG v62), default quality FGP_logo LMFR_con 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 t logical 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 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). 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. 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)(p in(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). FGP_logo LMFR_con 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) 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). 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. 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)(p in(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). FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development Molecular Regulatory Networks Modeling Benitez and Hejatko, submitted □The good model should be able to simulate reality FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development Molecular Regulatory Networks Modeling Benitez and Hejatko, submitted □Simulation of mutants FGP_logo LMFR_con Signaling and Hormonal Regulation of Plant Development §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus § Osnova FGP_logo_color File:GFP Mice 01.jpg File:Knockoutmouse80-72.jpg §malé nároky na chovnou plochu § §relativně velké množství mláďat (3-14, v průměru 6-8) § §velikost genomu se blíží velikosti genomu člověka (cca 3000 Mbp), podobně jako počet genů (cca 24K) § §20 chromozomů (19+1) § §vhodná pro široké spektrum fyziologických experimentů (anatomicky i fyziologicky podobná člověku) § §možno poměrně snadno získávat K.O. mutanty i transgenní linie § § Mus musculus myš domácí, house mouse a mouse File:Muizenkooi met houten muizen (3).JPG More info about mouse at http://www.informatics.jax.org/greenbook/index.shtml. FGP_logo_color §Genom známý od roku 2002 (http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/mouse/) Mus musculus myš domácí, house mouse § FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus §Arabidopsis thaliana Osnova FGP_logo_color §malé nároky na kultivační plochu Arabidopsis thaliana huseníček polní, mouse-ear cress §velké množství semen (20.000/rostlinu a více) § §malý a kompaktní genom, (125 MBp, cca 25.000 genů, prům. velikost 3 kb) § §5 chromozomů §vhodná pro široké spektrum fyziologických experimentů § §velká přirozená variabilita (cca 750 ekotypů (Nottingham Arabidopsis Seed Stock Centre)) Col-0 Columbia 0 Ler-0 Landsberg 0 Ws-0 Wassilewskija 0 http://seeds.nottingham.ac.uk/ 05arab FGP_logo_color Arabidopsis thaliana huseníček polní, mouse-ear cress §Genom známý od roku 2000 (http://www.arabidopsis.org/) FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus §Arabidopsis thaliana § §Vybrané metody molekulární biologie §Příprava transgenních organismů § Osnova FGP_logo_color 05_17.jpg 0004D704Macintosh HD B746699A: 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. FGP_logo_color galls Transformace Arabidopsis prostřednictvím Agrobacteria tumefaciens FGP_logo_color agrobacterium_transformation_2 agrobacterium_transformation agrobacterium_transformation_3 p35S_CKIi2 Transformace Arabidopsis prostřednictvím Agrobacteria tumefaciens přenos bakteriální DNA do rostlinné buňky FGP_logo_color cocultivation_scheme Transformace kokultivací listových disků FGP_logo_color Transformace kokultivací kalusů fa1 FGP_logo_color biolistic_transformation Transformace „nastřelováním“ DNA FGP_logo_color Transformace květenství At_infiltration_1 At_infiltration_2 At_infiltration_3 http://www.bch.msu.edu/pamgreen/green.htm FGP_logo_color Transformace květenství At_infiltration_4 At_infiltration_5 At_infiltration_6 At_infiltration_8 http://www.bch.msu.edu/pamgreen/green.htm At_infiltration_7 FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus §Arabidopsis thaliana § §Vybrané metody molekulární biologie §Příprava transgenních organismů §PCR Osnova FGP_logo_color PCR FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus §Arabidopsis thaliana § §Vybrané metody molekulární biologie §Příprava transgenních organismů §PCR §Design a příprava primerů (Dr. Hana Konečná) § Osnova FGP_logo_color §Nástroje systémové biologie §Analýza genové ontologie §Modelování molekulárních regulačních sítí § §Modelové organismy §Mus musculus §Arabidopsis thaliana § §Vybrané metody molekulární biologie §Příprava transgenních organismů §PCR §Design a příprava primerů (Dr. Hana Konečná) § Shrnutí FGP_logo_color Diskuse