rumba¶
- addMissingReactions(SampledModel, completeModel)¶
Verify the consistency between the sampled model and the complete reference model by checking that all reactions from complete model are in the sampled model
USAGE:
model = addMissingReactions(SampledModel, completeModel)
- INPUTS:
SampledModel: Sampled model completeModel: The complete reference model
- OUTPUTS:
model: Consistent sampled model wrt the complete model
- classifyRxns(completeModel, sampledModel, maxMetConn, maxNumPnts, verboseTag, LoopRxnsToIgnore)¶
This function get all incoming and outgoing reaction for each metabolite of the ‘sampledModel’and score them (fraction of all incoming/outgoing flux that passes through the metabolite).
- USAGE:
model = addMissingReactions(SampledModel,completeModel)
- INPUTS:
completeModel: The complete reference model SampledModel: Sampled model maxMetConn: The maximum connectivity of a metabolite to
consider. All branch points with a higher connectivity will be ignored. (default = 30)
- MaxNumPnts: Maximum number of points to use form the
sampled models. Extra points will be removed to improve memory usage and speed up calculations. (default = minimum number of points in the model or 500 points, whichever is smaller)
- verboseTag: 1 = print out progress and use waitbars. 0 =
print only minimal progress to screen.
- LoopRxnsToIgnore: list of rxns associated with loop within the model,
default- reaction loops defined usinf FVA
- OUTPUTS:
- MetConnectivity: A structure that present for each
metabolite present in ‘sampledModel’ the following sets of fields:
ConnRxns - the reactions that are connected to the metabolite Sij - The stoichiometric coefficient for the metabolite in each reaction in ConnRxns RxnScore - Score for each reaction in ConnRxns Direction - The direction of reaction flux for each sample point MetNotUsed - Whether or not the metabolite is used in the condition
ConnectedMet: List of metabolites described in ‘MetConnectivity’
- Authors: - Nathan E. Lewis, May 2010-May 2011
Anne Richelle, May 2017
- compareConditions(MetConnectivity1, ConnectedMet1, MetConnectivity2, ConnectedMet2)¶
This function compare reaction score of both sampling condition and identify metabolites that change significantly
USAGE:
[MetsAndRxns, pVal_up, pVal_down, Dir_1, Dir_2] = compareConditions(MetConnectivity1, ConnectedMet1, MetConnectivity2, ConnectedMet2)
- INPUTS:
- MetConnectivity1: A structure that present, for each
metabolite present in the model sampled under first condition the following sets of fields:
ConnRxns - the reactions that are connected to the metabolite
Sij - The stoichiometric coefficient for the metabolite in each reaction in ConnRxns
RxnScore - Score for each reaction in ConnRxns
Direction - The direction of reaction flux for each sample point
MetNotUsed - Whether or not the metabolite is used in the condition
ConnectedMet1: List of metabolites described in MetConnectivity1 MetConnectivity2: Same as MetConnectivity1 but for model
sampled under the second condition
ConnectedMet2: List of metabolites described in MetConnectivity2
- OUTPUTS:
- MetsAndRxns: Cell arrays containing in the first column
the list of metabolites that significantly change under both sampling conditions and in the second column the reactions that are connected to these metabolites
- pVal_up: p-value associated to upregulated
MetsAndRxns
- pVal_down: p-value associated to downregulated
MetsAndRxns
- Dir_1: reaction directionality for model sampled under first
condition (1 producing metabolite, -1 consuming the metabolite)
- Dir_2: Same as Dir_1 but for model
sampled under the second condition
- normalizePoints(model1, model2, NormalizePointsParam, LoopRxnsToIgnore)¶
Normalize the sampled points by the net network flux (NormalizePointsParam = 1) or by growth rate (NormalizePointsParam = 2)
USAGE:
[model1, model2] = normalizePoints(model1, model2, NormalizePointsParam, LoopRxnsToIgnore)
- INPUTS:
model1: Model sampled under first condition model2: Model sampled under second condition NormalizePointsOption: Option to normalize sample points to (1) the
same median of magnitude of flux through all non-loop gene-associated reactions, or (2) the optimal growth rate. (default = 1)
- LoopRxnsToIgnore: list of rxns associated with loop within the model,
default- reaction loops defined usinf FVA
- OUTPUTS:
model1: Normalized sampled model under first condition model2: Normalized sampled model under second condition
- pValDistForModelOverlap(model1, model2)¶
Compute for each reaction in common in both sampled modelw the magnitude of median flux value change
USAGE:
[rxnsInCommon, MedianChange] = pValDistForModelOverlap(model1, model2)
- INPUTS:
model1: Model sampled under first condition model2: Model sampled under second condition
- OUTPUTS:
rxnsInCommon: Reactions shared by both sampled models MedianChange: Magnitude of median flux value change for
each reaction listed in ‘rxnsInCommon’
- rumba(model1, model2, completeModel, sampling, maxMetConn, RxnsOfInterest, GenesOfInterest, NormalizePointsOption, PValCuttoff, MaxNumPoints, LoopRxnsToIgnore, verboseTag)¶
RUMBA predicts which reactions significantly change their flux at metabolic branch points under two conditions
USAGE:
[RUMBA_outputs, UpRegulated, DownRegulated, MetConnectivity1, MetConnectivity2] = rumba(model1, model2, completeModel)
- INPUTS:
- model1: Model under first condition, exchange reactions
are constrained with the data related to the first condition. If model already sampled (‘sampling’ = 0). The sampling points is in an mxn matrix with m reactions and n points included as a field in the model(i.e., model1.points). Set ‘sampling’ = 1 to set the model constrained under the first conditions
- model2: Model under second condition. Same
format as model1.
- completeModel: The complete reference model. This is used
to verify consistency between the sampled models.
- OPTIONAL INPUTS:
- sampling: 0, if no sampling needed (default)
1, if sampling of the models under both conditions
- maxMetConn: The maximum connectivity of a metabolite to
consider. All branch points with a higher connectivity will be ignored (default = 30)
- RxnsOfInterest: Reactions for which predictions are desired.
Specifying only desired reactions speeds up algorithm (default = all reactions)
GenesOfInterest: Genes associated with the reactions of interest (default = all genes) NormalizePointsOption: Option to normalize sample points to (1) the
same median of magnitude of flux through all non-loop gene-associated reactions, or (2) the optimal growth rate. (default = 1)
- PValCuttoff: P-value cutoff used to decide which changes in
branch point flux to call significant (two- tailed p-value, so .05 will mean 0.25 on both tails). (default = 0.05)
- MaxNumPoints: Maximum number of points to use form the
sampled models. Extra points will be removed to improve memory usage and speed up calculations. (default = minimum number of points in the model or 500 points, whichever is smaller)
- verboseTag: 1 = print out progress and use waitbars (default).
0 = print only minimal progress to screen.
- LoopRxnsToIgnore: list of rxns associated with loop within the model,
(default - reaction loops defined using FVA)
- OUTPUTS:
- RUMBA_outputs: Structure containing all information about
each gene-reaction pair. For gene-reaction pair for which the differential branch-point information is possible to calculate: the list of connected metabolites (‘ConnectedMets’), the associated up-regulation p-value (‘pValue_up’), the associated down-regulation p-value (‘pValue_down’)and the reaction directionality for both model (‘direction’).The structure also contains the list of gene-reaction pairs for which no differential branch-point information can be determined (because of loops, unused pathways, etc.).
- UpRegulated: first columnGene-reaction pairs towards which flux is
significantly upregulated during the shift; second column : list of metabolites connected to the gene-reaction pair; third column : Magnitude of absolutes flux change
- DownRegulated: same structure as UpRegulated but for gene-reaction pairs
from which flux is significantly downregulated during the shift
- MetConnectivity1: A structure that present for each metabolite present in model1
the following sets of fields:
ConnRxns - the reactions that are connected to the metabolite
Sij - The stoichiometric coefficient for the metabolite in each reaction in ConnRxns
RxnScore - Score for each reaction in ConnRxns
Direction - The direction of reaction flux for each sample point
MetNotUsed - Whether or not the metabolite is used in the condition
MetConnectivity2: Same as MetConnectivity1 but for model2