Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

Abstract

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.

Finding branched pathways in metabolic network via atom group tracking

“…Despite its novel characteristics, BPFinder does not figure out a certain solution to all the challenges with regard to the de novo design of synthetic branched pathways. For example, similar to other pathfinding methods [14,19,26,29] for synthetic design of metabolic pathways, BPFinder focuses on searching potentially useful branched pathways without reference to any subcellular structure or specific organism. The returning pathways should be carefully carried out further analysis and study in depth before experimental implementations.…”

“…Recently, people find that tracking the movements of atoms from the source compound to the target compound is an effective way of avoiding hub metabolites in finding linear pathways [26]. A number of atom tracking methods, such as LPAT [26], MetaRoute [27], CFP [13], PathTracer [28], HPAT [29] and RouteSearch [22] have been successfully proposed to avoid hub metabolites when finding linear metabolic pathways.…”

“…It is thus desirable to develop appropriate pathfinding tools for guiding the design of synthetic metabolic pathways that may not exist in a single organism [14,19,26]. A number of pathfinding methods including Tinker [19], BPAT-M [14], BPAT-S [26], LPAT [26] and HAPT [29] were developed to search pathways for the design of synthetic pathways that may span multiple organisms through the entire reactions in biochemical database.…”

NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks

“…Many algorithms in the field of metabolic pathfinding have been developed for identifying pathways between metabolites within a metabolic network ( Kim et al., 2017 ). These algorithms generally fall into two categories: graph-based approaches ( Croes et al., 2005 , 2006 ; Kim et al., 2020 ; Simeonidis et al., 2003 ), which utilize the connectivity between metabolites (nodes) as determined by the reactions (edges) in the network, and stoichiometric approaches ( Pharkya et al., 2004 ; Klamt et al., 2017 ; Chowdhury and Maranas, 2015 ; Pey et al., 2011 ), which use optimization to predict stoichiometrically-balanced pathways to generate a given metabolite. A critical challenge for both approaches is limiting the identification of false-positive pathways due to a few highly connected metabolites, e.g.…”

Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

“…Different methodologies have been developed in the past to (i) represent metabolic networks as mathematical graph structures, and (ii) to find pathways within the graph from a given source to a target metabolite. Different approaches have been explored to bias a biochemically blind graph search algorithm towards biologically meaningful pathways, such as the exclusion of cofactors from the network, defining reactant pairs through the chemical similarity of compounds 6 , precomputing recurring subpaths 7 , and atom or substructure conservation throughout the pathway 6,8 . Atom conservation in general, and carbon conservation specifically, have been shown to be a valuable criterion for finding biologically meaningful pathways [9][10][11][12][13][14] .…”