# 15.1.1.2.1.2. cobra.flux_analysis.gapfilling¶

## 15.1.1.2.1.2.1. Module Contents¶

class cobra.flux_analysis.gapfilling.GapFiller(model, universal=None, lower_bound=0.05, penalties=None, exchange_reactions=False, demand_reactions=True, integer_threshold=1e-06)[source]

Class for performing gap filling.

This class implements gap filling based on a mixed-integer approach, very similar to that described in [1] and the ‘no-growth but growth’ part of [2]_ but with minor adjustments. In short, we add indicator variables for using the reactions in the universal model, z_i and then solve problem

minimize sum_i c_i * z_i s.t. Sv = 0

v_o >= t lb_i <= v_i <= ub_i v_i = 0 if z_i = 0

where lb, ub are the upper, lower flux bounds for reaction i, c_i is a cost parameter and the objective v_o is greater than the lower bound t. The default costs are 1 for reactions from the universal model, 100 for exchange (uptake) reactions added and 1 for added demand reactions.

Note that this is a mixed-integer linear program and as such will expensive to solve for large models. Consider using alternatives [3]_ such as CORDA instead [4,5]_.

Parameters: model (cobra.Model) – The model to perform gap filling on. universal (cobra.Model) – A universal model with reactions that can be used to complete the model. lower_bound (float) – The minimally accepted flux for the objective in the filled model. penalties (dict, None) – A dictionary with keys being ‘universal’ (all reactions included in the universal model), ‘exchange’ and ‘demand’ (all additionally added exchange and demand reactions) for the three reaction types. Can also have reaction identifiers for reaction specific costs. Defaults are 1, 100 and 1 respectively. integer_threshold (float) – The threshold at which a value is considered non-zero (aka integrality threshold). If gapfilled models fail to validate, you may want to lower this value. exchange_reactions (bool) – Consider adding exchange (uptake) reactions for all metabolites in the model. demand_reactions (bool) – Consider adding demand reactions for all metabolites.

References

 [1] Reed, Jennifer L., Trina R. Patel, Keri H. Chen, Andrew R. Joyce, Margaret K. Applebee, Christopher D. Herring, Olivia T. Bui, Eric M. Knight, Stephen S. Fong, and Bernhard O. Palsson. “Systems Approach to Refining Genome Annotation.” Proceedings of the National Academy of Sciences 103, no. 46 (2006): 17480–17484. [2] Kumar, Vinay Satish, and Costas D. Maranas. “GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions.” Edited by Christos A. Ouzounis. PLoS Computational Biology 5, no. 3 (March 13, 2009): e1000308. doi:10.1371/journal.pcbi.1000308. [4] Schultz, André, and Amina A. Qutub. “Reconstruction of Tissue-Specific Metabolic Networks Using CORDA.” Edited by Costas D. Maranas. PLOS Computational Biology 12, no. 3 (March 4, 2016): e1004808. doi:10.1371/journal.pcbi.1004808. [5] Diener, Christian https://github.com/cdiener/corda
__init__(model, universal=None, lower_bound=0.05, penalties=None, exchange_reactions=False, demand_reactions=True, integer_threshold=1e-06)[source]
extend_model(exchange_reactions=False, demand_reactions=True)[source]

Extend gapfilling model.

Add reactions from universal model and optionally exchange and demand reactions for all metabolites in the model to perform gapfilling on.

Parameters: exchange_reactions (bool) – Consider adding exchange (uptake) reactions for all metabolites in the model. demand_reactions (bool) – Consider adding demand reactions for all metabolites.
update_costs()[source]

Update the coefficients for the indicator variables in the objective.

Done incrementally so that second time the function is called, active indicators in the current solutions gets higher cost than the unused indicators.

add_switches_and_objective()[source]

Update gapfilling model with switches and the indicator objective.

fill(iterations=1)[source]

Perform the gapfilling by iteratively solving the model, updating the costs and recording the used reactions.

Parameters: iterations (int) – The number of rounds of gapfilling to perform. For every iteration, the penalty for every used reaction increases linearly. This way, the algorithm is encouraged to search for alternative solutions which may include previously used reactions. I.e., with enough iterations pathways including 10 steps will eventually be reported even if the shortest pathway is a single reaction. A list of lists where each element is a list reactions that were used to gapfill the model. iterable RuntimeError – If the model fails to be validated (i.e. the original model with the proposed reactions added, still cannot get the required flux through the objective).
validate(reactions)[source]
cobra.flux_analysis.gapfilling.gapfill(model, universal=None, lower_bound=0.05, penalties=None, demand_reactions=True, exchange_reactions=False, iterations=1)[source]

Perform gapfilling on a model.

See documentation for the class GapFiller.

Parameters: model (cobra.Model) – The model to perform gap filling on. universal (cobra.Model, None) – A universal model with reactions that can be used to complete the model. Only gapfill considering demand and exchange reactions if left missing. lower_bound (float) – The minimally accepted flux for the objective in the filled model. penalties (dict, None) – A dictionary with keys being ‘universal’ (all reactions included in the universal model), ‘exchange’ and ‘demand’ (all additionally added exchange and demand reactions) for the three reaction types. Can also have reaction identifiers for reaction specific costs. Defaults are 1, 100 and 1 respectively. iterations (int) – The number of rounds of gapfilling to perform. For every iteration, the penalty for every used reaction increases linearly. This way, the algorithm is encouraged to search for alternative solutions which may include previously used reactions. I.e., with enough iterations pathways including 10 steps will eventually be reported even if the shortest pathway is a single reaction. exchange_reactions (bool) – Consider adding exchange (uptake) reactions for all metabolites in the model. demand_reactions (bool) – Consider adding demand reactions for all metabolites. list of lists with on set of reactions that completes the model per requested iteration. iterable

Examples

>>> import cobra.test as ct
>>> from cobra import Model
>>> from cobra.flux_analysis import gapfill
>>> model = ct.create_test_model("salmonella")
>>> universal = Model('universal')