## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(rbcmodel) ## ----search_less-------------------------------------------------------------- test<-search_enzyme("Synechococcus",data="comprehensive") #filter columns and restrict entries for readability test[1:10,c(1:5,9,12,15,18,21,24,26,27,29)] ## ----search_nomutant---------------------------------------------------------- test<-test[test$mutant=="FALSE",] #no longer restricting entry number test[,c(1:4,9,12,15,18,21,24,26,27,29)] #removed mutant column, as they are all not mutants now ## ----search_check------------------------------------------------------------- search_enzyme("Synechococcus",data="comprehensive")[c(33,41),] ## ----enzyme_from_scratch------------------------------------------------------ SUP05_Rbc<-new_enzyme(kcat_val=7.2,Kc_val=36,Ko_val=68,S_val=14) SUP05_Rbc ## ----enzyme_from_scratch2----------------------------------------------------- SUP05_Rbc2<-new_enzyme(kcat_val=7.2,Kc_val=36,Ko_val=68,S_val=14,temp=22,S_T=30) SUP05_Rbc2 ## ----compare_vulgaris--------------------------------------------------------- search_enzyme("Beta",data="comprehensive")[3:4,] ## ----search_vulgaris---------------------------------------------------------- df1<-search_enzyme("Beta",data="comprehensive") df1 ## ----merge_median_vulgaris---------------------------------------------------- df2<-merge_entries(df1,"identifier",df1$identifier,"median",new_id="Vulgaris_merged") df2 ## ----merge_smaller_output----------------------------------------------------- df3<-merge_entries(df1,"identifier",df1$identifier,"median",new_id="Vulgaris_merged",keep_merged=FALSE) df3 ## ----merge_orr_bad------------------------------------------------------------ df4<-merge_entries(df1,"identifier",c(df1$identifier[1],df1$identifier[4]),"last",new_id="Vulgaris_merged",keep_unmerged=FALSE) df4 ## ----merge_orr_correct-------------------------------------------------------- df4<-merge_entries(df1,"identifier",c(df1$identifier[4],df1$identifier[1]),"last",new_id="Vulgaris_merged",keep_unmerged=FALSE) df4 ## ----merge_median_marcosii---------------------------------------------------- df5<-merge_entries(df1,"identifier",df1$identifier,"median",new_id="Vulgaris_merged") df5<-merge_entries(df5,"identifier",c("Vulgaris_merged",df1$identifier[1]),"last",new_id="Marcosii_merged",keep_unmerged=FALSE) df5 ## ----create_marcosii---------------------------------------------------------- marcosii_Rbc<-Enzyme("Marcosii_merged",data=df5) marcosii_Rbc ## ----creat_thalassiosira------------------------------------------------------ ta_df<-search_enzyme("antarctica")[2:3,] ta_merged<-merge_entries(ta_df,"identifier",ta_df$identifier,"median",new_id="t_antarctica_merged") Enzyme("t_antarctica_merged",data=ta_merged) ## ----search_sc---------------------------------------------------------------- search_enzyme("costatum") ## ----search_sc_genus---------------------------------------------------------- sc_df<-search_enzyme("Skeletonema",data="comprehensive") sc_df ## ----merge_sc----------------------------------------------------------------- sc_merged<-merge_entries(sc_df,"identifier",sc_df$identifier,"median",new_id="Skeletonema_merged") sc_merged<-merge_entries(sc_merged,"identifier",c("Skeletonema_merged",sc_df$identifier[1:2]),"last",new_id="Costatum_merged",keep_unmerged=FALSE) sc_merged Enzyme("Costatum_merged",data=sc_merged) ## ----search_bh---------------------------------------------------------------- search_enzyme("horologicalis") diatom_df<-search_enzyme("Diatoms",data="comprehensive") ## ----search_diatom------------------------------------------------------------ search_enzyme("average") diatom_base<-search_enzyme("average")[14,] ## ----create_bh---------------------------------------------------------------- bh_Rbc<-Enzyme("horologicalis_Young_2016") bh_Rbc ## ----fix_bh------------------------------------------------------------------- bh_Rbc<-modify_enzyme(bh_Rbc,S_val=diatom_base$S_val) bh_Rbc ## ----find_gamma_entries------------------------------------------------------- gpb_df<-search_enzyme("Gammaproteobacteria",data="comprehensive")[,c(1:4,7,9:10,12,15,18,21,24,26,27,29)] gpb_df<-gpb_df[gpb_df$form=="1Aq",] gpb_df<-gpb_df[gpb_df$group=="Bacteria",] gpb_df<-gpb_df[gpb_df$primary,] #now to remove the primary and group columns, which we don't need anymore gpb_df<-gpb_df[,-c(5,14)] gpb_df ## ----merge_ta----------------------------------------------------------------- gpb_df<-search_enzyme("Gammaproteobacteria",data="comprehensive")[,c(1:4,9:10,12:13,15:16,18:19,21,24,26,27,29)] gpb_df<-gpb_df[gpb_df$form=="1Aq",] gpb_merged<-merge_entries(gpb_df,"identifier",gpb_df$identifier,"median",new_id="gpb_1Aq_merged") gpb_merged<-merge_entries(gpb_merged,"identifier",c("gpb_1Aq_merged","marinus_Iaq_Hayashi_1998"),"last",new_id="Ta_merged",keep_unmerged=FALSE) gpb_merged ta_1Aq<-Enzyme("Ta_merged",data=gpb_merged) ta_1Aq ## ----fix_ta------------------------------------------------------------------- ta_1Aq<-modify_enzyme(ta_1Aq,kcat_T=30,Kc_T=25,Ko_T=25,S_T=30) ta_1Aq ## ----DHScale_from_scratch----------------------------------------------------- SUP05_DH<-new_DHScale(kcat_dH=35.6,Kc_dH=40.1,Ko_dH=15.5,S_dH=-25.1) SUP05_DH ## ----create_tr---------------------------------------------------------------- tr_dH<-search_DHScale("repens") tr_merged<-merge_entries(tr_dH,"identifier",tr_dH$identifier,"median",new_id="t_repens_dH_merged") DHScale("t_repens_dH_merged",data=tr_merged) ## ----search_ca---------------------------------------------------------------- search_DHScale("album") ## ----search_ca_genus---------------------------------------------------------- ca_dH<-search_DHScale("Chenopodium") ca_dH ## ----create_album------------------------------------------------------------- ca_merged<-merge_entries(ca_dH,"identifier",ca_dH$identifier,"median",new_id="Chenopodium_merged") ca_merged<-merge_entries(ca_merged,"identifier",c("Chenopodium_merged",ca_dH$identifier[1]),"last",new_id="album_merged",keep_unmerged=FALSE) ca_merged DHScale("album_merged",data=ca_merged) ## ----search_Zm---------------------------------------------------------------- search_DHScale("Zea") C4_df<-search_DHScale("C4 plants",data="abridged") ## ----------------------------------------------------------------------------- C4_merged<-merge_entries(C4_df,"identifier",C4_df$identifier,"median",new_id="C4_merged") zm_merged<-merge_entries(C4_merged,"identifier",c("C4_merged","95"),"last",new_id="Zmays_merged",keep_unmerged=FALSE) DHScale("Zmays_merged",data=zm_merged) ## ----find_c4_avg-------------------------------------------------------------- C4_average<-search_DHScale("C4 plants",data="averaged") C4_average ## ----create_zm---------------------------------------------------------------- zm_dH<-DHScale("mays_galmés_2016_dH", data="abridged") zm_dH ## ----fix_zm------------------------------------------------------------------- zm_dH<-modify_DHScale(zm_dH,kcat_dH=C4_average$kcat_dH,Ko_dH=C4_average$Ko_dH) zm_dH ## ----create_DHScale_Av_diatoms------------------------------------------------ form1D_df<-search_DHScale("1D") form1D_merged<-merge_entries(form1D_df,"identifier",form1D_df$identifier,"median",new_id="1D_merged") form1D_merged[9,] Rbc_form1D<-DHScale("1D_merged",data=form1D_merged) #print to show it's missing values Rbc_form1D search_DHScale("Anabaena") Rbc_form1D<-modify_DHScale(Rbc_form1D,Kc_dH=38.8,Ko_dH=26.7) Rbc_form1D ## ----change_PGS--------------------------------------------------------------- ta_1Aq modify_enzyme(ta_1Aq,PGS="canon") ## ----PGS_in_dependence-------------------------------------------------------- #define a DHScale first avg_1A<-DHScale(search_DHScale("1A",data="averaged")[1,1]) #set PGS pathway to get CO2_dependence() to work Rbc_ta<-CO2_dependence(ta_1Aq,avg_1A,PGS="canon") Rbc_ta(100,200,5)