From 723390b69e4afa31dd9ba4bda835be7f79ad9481 Mon Sep 17 00:00:00 2001 From: Francisco Zorrilla Date: Wed, 10 Jan 2024 20:44:58 +0100 Subject: [PATCH] fix typos in notebook text --- notebooks/1_fba.ipynb | 28 +++++++------- notebooks/2_fba.ipynb | 90 +++++++++++++++++++++---------------------- 2 files changed, 59 insertions(+), 59 deletions(-) diff --git a/notebooks/1_fba.ipynb b/notebooks/1_fba.ipynb index a81a869..5b308ac 100644 --- a/notebooks/1_fba.ipynb +++ b/notebooks/1_fba.ipynb @@ -125,7 +125,7 @@ " iMM904\n", " \n", " Memory address\n", - " 0x07faa4bcbf4f0\n", + " 0x07feff150e670\n", " \n", " Number of metabolites\n", " 1226\n", @@ -145,7 +145,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -180,7 +180,7 @@ " NameExo 1 3 beta glucan glucohydrase\n", " \n", " Memory address\n", - " 0x07faa59f2d700\n", + " 0x07feff1985850\n", " \n", " Stoichiometry\n", " \n", @@ -198,7 +198,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -296,7 +296,7 @@ " NameGlucose-6-phosphate isomerase\n", " \n", " Memory address\n", - " 0x07faa5a804af0\n", + " 0x07fefe0ac4be0\n", " \n", " Stoichiometry\n", " \n", @@ -314,7 +314,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -357,7 +357,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also view reaction upper and lower bounds, large numbers, typically around 1000 or more, are used as infinite limits (unconstained fluxes). \n", + "We can also view reaction upper and lower bounds, large numbers, typically around 1000 or more, are used as infinite limits (unconstrained fluxes). \n", "Because the `pgi.lower_bound` < 0, and `pgi.upper_bound` > 0, pgi is reversible." ] }, @@ -537,7 +537,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will consider cytosolic atp as our metabolite, which has the id `atp_c` in our test model. However, if you want to see the IDs of the frist N metabolites in the reconstruction, you can run the code below:" + "We will consider cytosolic atp as our metabolite, which has the id `atp_c` in our test model. However, if you want to see the IDs of the first N metabolites in the reconstruction, you can run the code below:" ] }, { @@ -604,19 +604,19 @@ " NameATP C10H12N5O13P3\n", " \n", " Memory address\n", - " 0x07faa382fcd90\n", + " 0x07feff161bf10\n", " \n", " FormulaC10H12N5O13P3\n", " \n", " Compartmentc\n", " \n", " In 149 reaction(s)\n", - " FRUK, GLYK, NADK, ACCOAC, RBK, ASNS1, DURIK1, GLUCYS, SHKK, CTPS2, PFK26, ADK1, PRAIS, PIN3K_SC, PRAGSr, HEX4, DHAK, GLNS, ARGSS, GLUK, HSK, GALKr, HMPK1, AASAD2, GTHS, FMNAT, THFGLUS, PYDXNK,...\n", + " PNTK, NDPK7, PC, AASAD1, ASPTRS, METAT, NMNAT, FACOAL181, PFK_3, ATPPRT, HSK, PRPPS, ADNK1, ATPtm_H, INSK, ETHAK, PRAIS, LEUTRS, GMPS2, RNTR1, NNATr, RBFK, SLCBK1, DGK1, ARGSS, GLUK, PFK, NDPK1,...\n", " \n", " " ], "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -766,7 +766,7 @@ " NamePGI1\n", " \n", " Memory address\n", - " 0x07faa59e7aac0\n", + " 0x07feff18d3c10\n", " \n", " FunctionalTrue\n", " \n", @@ -776,7 +776,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1066,7 +1066,7 @@ "source": [ "1. The distribution of predicted growth rates appears to be bimodal, with a small peak on the left and a larger peak on the right, can you explain why it has this shape? \n", "2. Can you verify the consistency between gene and reactions knockouts results using a gene or a reaction of your choice?\n", - "3. Can you verify the essentiality of your gene of choice from the previous excercise using relevant databases (e.g. KEGG and the SGD)?\n", + "3. Can you verify the essentiality of your gene of choice from the previous exercise using relevant databases (e.g. KEGG and the SGD)?\n", "4. Do you expect these results to change if we change the medium where we are growing our yeast model? " ] }, diff --git a/notebooks/2_fba.ipynb b/notebooks/2_fba.ipynb index 557e179..896262f 100644 --- a/notebooks/2_fba.ipynb +++ b/notebooks/2_fba.ipynb @@ -63,7 +63,7 @@ "source": [ "The availability of nutrients has a major impact on metabolic fluxes and `cobrapy` provides some helpers to manage the exchanges between the external environment and your metabolic model. In experimental settings the “environment” is usually constituted by the growth medium, i.e. the concentrations of all metabolites and co-factors available to the modeled organism. However, constraint-based metabolic models only consider fluxes. Thus, you cannot simply use concentrations since fluxes have the unit `mmol / [gram of dry-cell weight * hour]` (i.e. concentration per gram dry weight of cells and hour).\n", "\n", - "Also, you are setting an upper bound for the particular import flux and not the flux itself. There are some crude approximations. For instance, if you supply 1 mmol of glucose every 24h to 1 gram of bacteria you might set the upper exchange flux for glucose to `1 mmol / [1 gDW * h]` since that is the nominal maximum that can be imported. There is no guarantee however that glucose will be consumed with that flux. Thus, the preferred data for exchange fluxes are direct flux measurements as the ones obtained from timecourse exa-metabolome measurements for instance.\n", + "Also, you are setting an upper bound for the particular import flux and not the flux itself. There are some crude approximations. For instance, if you supply 1 mmol of glucose every 24h to 1 gram of bacteria you might set the upper exchange flux for glucose to `1 mmol / [1 gDW * h]` since that is the nominal maximum that can be imported. There is no guarantee however that glucose will be consumed with that flux. Thus, the preferred data for exchange fluxes are direct flux measurements as the ones obtained from time-course exo-metabolome measurements for instance.\n", "\n", "So how does that look in COBRApy? The current growth medium of a model is managed by the medium attribute." ] @@ -261,16 +261,16 @@ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, "execution_count": 6, @@ -420,7 +420,7 @@ "# Get genes IDs\n", "id_essential_genes_v2=[gene.id for gene in essential_genes_v2]\n", "\n", - "# Print number of esssential genes\n", + "# Print number of essential genes\n", "print(\"Essential genes with ethanol carbon source:\",len(id_essential_genes_v2))" ] }, @@ -522,50 +522,50 @@ { "data": { "text/plain": [ - "['Q0275',\n", - " 'YDR298C',\n", - " 'YGR183C',\n", - " 'YBR196C',\n", - " 'YHR001W_A',\n", - " 'YEL024W',\n", + "['YHR001W_A',\n", + " 'YDR377W',\n", + " 'YBR039W',\n", " 'YDL181W',\n", - " 'YDL067C',\n", - " 'Q0085',\n", - " 'Q0045',\n", + " 'YHR051W',\n", + " 'YPL078C',\n", + " 'YLR038C',\n", " 'YLR395C',\n", " 'YLL041C',\n", - " 'Q0130',\n", - " 'YCR012W',\n", - " 'YPR191W',\n", - " 'YKL060C',\n", - " 'YGL187C',\n", - " 'YJL166W',\n", - " 'YLR038C',\n", - " 'YFR033C',\n", + " 'YJR121W',\n", + " 'Q0045',\n", " 'YML081C_A',\n", - " 'YHR051W',\n", " 'YDL004W',\n", - " 'Q0105',\n", - " 'YKL016C',\n", - " 'YLR377C',\n", - " 'YBL045C',\n", + " 'YEL024W',\n", " 'YPL262W',\n", - " 'YMR256C',\n", + " 'YER065C',\n", + " 'YGL191W',\n", + " 'Q0085',\n", + " 'YKL060C',\n", + " 'YKR097W',\n", + " 'Q0080',\n", " 'YDR050C',\n", + " 'YJL166W',\n", + " 'YPR191W',\n", + " 'YGR183C',\n", + " 'YLR295C',\n", + " 'YDR298C',\n", + " 'YCR012W',\n", + " 'YGL187C',\n", + " 'YLR377C',\n", + " 'YBL045C',\n", " 'YBL099W',\n", + " 'Q0105',\n", " 'YDR529C',\n", - " 'YLR295C',\n", - " 'YKR097W',\n", - " 'YPL271W',\n", + " 'YDL067C',\n", " 'Q0250',\n", " 'YOR065W',\n", - " 'YER065C',\n", - " 'YBR039W',\n", - " 'YDR377W',\n", - " 'Q0080',\n", - " 'YJR121W',\n", - " 'YPL078C',\n", - " 'YGL191W']" + " 'YPL271W',\n", + " 'YBR196C',\n", + " 'Q0130',\n", + " 'YKL016C',\n", + " 'YFR033C',\n", + " 'YMR256C',\n", + " 'Q0275']" ] }, "execution_count": 16,