A review of parameters and heuristics for guiding metabolic pathfinding

Kim, Sarah M.; Peña, Matthew I.; Moll, Mark; Bennett, George N.; Kavraki, Lydia E.

doi:10.1186/s13321-017-0239-6

Cited by 26 publications

(24 citation statements)

References 66 publications

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“…Performance of PhDSeeker was compared with several state-of-the-art metabolic pathfinding methods included in a very recent review 42 . Based on the availability of the algorithms, the comparison was made using AGPathFinder (search based on group-of-atoms-tracking and thermodinamics) 18 , LPAT (search based on maximization of atoms transferred from source to target) 17 , FMM (search based on the minimization of the number of known pathways to be combined in the solution) 43 and RouteSearch (search based on atom-tracking and thermodinamics) 44 .…”

Section: Resultsmentioning

confidence: 99%

Metabolic pathways synthesis based on ant colony optimization

Gérard

Stegmayer

Milone

2018

Sci Rep

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One of the current challenges in bioinformatics is to discover new ways to transform a set of compounds into specific products. The usual approach is finding the reactions to synthesize a particular product, from a given substrate, by means of classical searching algorithms. However, they have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. We present here a novel bio-inspired algorithm for synthesizing linear and branched metabolic pathways. It allows relating several compounds simultaneously, ensuring the availability of substrates for every reaction in the solution. Comparisons with classical searching algorithms and other recent metaheuristic approaches show clear advantages of this proposal, fully recovering well-known pathways. Furthermore, solutions found can be analyzed in a simple way through graphical representations on the web.

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Section: Resultsmentioning

confidence: 99%

Metabolic pathways synthesis based on ant colony optimization

Gérard

Stegmayer

Milone

2018

Sci Rep

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“…A concept somewhat related to synthesis planning is finding pathways in metabolic networks, see e.g. [ 22 ] for a recent review. The work most closely related to ours is [ 23 ], which models metabolic pathways as a type of hyperpaths and gives a method for enumerating pathways between a set of source compounds and a target compound in the network.…”

Section: Previous Workmentioning

confidence: 99%

“…However, their algorithm enumerates all pathways, not the K best according to a quality measure. Other papers reviewed in [ 22 ] do consider finding pathways in ranked order, but the modeling there is as simple paths in standard graphs.…”

Section: Previous Workmentioning

confidence: 99%

Finding the K best synthesis plans

et al. 2018

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In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it. We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs by modeling individual synthesis plans as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, a polynomial time algorithm to find the K shortest hyperpaths can be used to compute the K best synthesis plans for a given target molecule. Having K good plans to choose from has many benefits: it makes the synthesis planning process much more robust when in later stages adding further chemical detail, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates. A bond set gives rise to a HoR in a natural way. However, our modeling is not restricted to bond set based approaches—any set of known reactions and starting materials can be used to define a HoR. We also discuss classical quality measures for synthesis plans, such as overall yield and convergency, and demonstrate that convergency has a built-in inconsistency which could render its use in synthesis planning questionable. Decalin is used as an illustrative example of the use and implications of our results.

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“…Existing graph-based search approaches have utilized a combination of heuristics and cutoffs to limit the number of infeasible pathways [13]. One of the most widely used heuristics is the conservation of chemical structure from the start to the target compound.…”

Section: Introductionmentioning

confidence: 99%

Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

et al. 2020

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Background: The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others. To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways. Results: A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest. Conclusions: This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.

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A review of parameters and heuristics for guiding metabolic pathfinding

Cited by 26 publications

References 66 publications

Metabolic pathways synthesis based on ant colony optimization

Metabolic pathways synthesis based on ant colony optimization

Finding the K best synthesis plans

Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

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