Pseudomonas putida NCIMB 10007 of bicyclic (rac)-camphor to ∆2,5-3,4,4-trimethylpimelyl-CoA, the first aliphatic pathway intermediate, have been determined to help establish the relevant induction profile of each of the oxygen-dependent enzymes. The efficacy of both relevant substrates and pathway metabolites as inducers has been established. Further, inhibitors with characterised functionality have been used to indicate that the pertinent regulatory controls operate at the level of transcription of the corresponding genes.For the first time, the differential rates of synthesis of all the key monooxygenases involved in the catabolism by  Pseudomonas putida NCIMB 10007 -camphor degradative pathway are catalysed by four monooxygenases. The pathway is initiated by cytochrome P450 monooxygenase (cytP450MO: A = camA + camB + camC), which promotes the initial activation of the bicyclic ring of both enantiomers of camphor equally. The next key oxygen-dependent reactions are catalysed by the enantiocomplementary 2,5-diketocamphane 1,2-monooxygenase  and 3,6-diketocamphane 1,6-monooxygenase  isoenzymes which serve to cleave the chiral bicyclic diketones generated respectively from the (+)- and (−)-enantiomers of (rac)-camphor to the common achiral monocyclic intermediate 2-oxo-∆3-4,5,5-trimethylcyclopentenylacetic acid  generates 5-hydroxy-3,4,4-trimethyl-∆2-pimelyl-CoA-δ-lactone (HTP-CoA), an unstable lactone that undergoes spontaneous hydrolytic cleavage to yield ∆2,5-3,4,4-trimethylpimelyl-CoA, a carboxylic acid ester which is notable for being the first aliphatic intermediate in the camphor degradation pathway. Unlike cytP450MO and OTEMO which both function exclusively as CAM-plasmid coded activities, the diketocamphane monooxygenases (DKCMO) are reduced flavin mononucleotide (FNR)-dependent two-component monooxygenases (fd-TCMOs) which are able to deploy not only CAM-plasmid coded putidaredoxin reductase (PdR) but alternatively three chromosome-coded enzymes, the dimeric flavin reductase [FR] Fred [The key facilitating reactions in the (FR] Fred .P. putida NCIMB 10007 on a composite succinate-camphor medium ) against the culture density (mg dry cell weight [mL of culture−1]) generates a so-called differential plot which gives a measure of the rate of synthesis of an enzyme in relation to total cell synthesis ), although the individual role(s) of the corresponding repressor proteins and whether they also exhibit any additional cooperative function(s) is currently uncharacterised. One possible involvement could be in providing the transcription-based mode of action implicit in the recorded action of isobutyryl-CoA, which when tested at the relatively low level of 0.05 mM proved to be a very effective feedback corepressor of (rac)-camphor-induced OTEMO (camG [orf3]) in P. putida NCIMB 10007. That all of these recorded regulatory controls triggered by (or genes  establisrac)-camphor degradation pathway, although toxic to P. putida NCIMB 10007 at >6 mM ), ‘back induction’ (cytP450MO and the DKCMOs by OTE) and ‘cross induction’ . In addition, the enzymes exo-hydroxycamphor dehydrogenase [P. putida NCIMB 10007 that these and the cited related prior studies have established is none the less impressive. Equivalent broad specificity-based controls have been characterised in various pseudomonads, including P. putida A.3.12, that can catabolise a variety of aromatic compounds via the β-ketoadipate pathway [Collectively, the presented data characterize for the first time the key initial induction profiles of all the key oxygen-dependent enzymes involved in the catabolism of bicyclic  that broad specificity of both induction (repressor proteins) and catalysis (enzymes) could either represent a way of vesting xibility , or alte


The consolidation of pathway databases, such as KEGG, Reactome and ConsensusPathDB, has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy.We propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place.Our method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.The online version of this article (10.1186/s12859-018-2355-3) contains supplementary material, which is available to authorized users. Pathways are sets of genes corresponding to functionally related interacting proteins. Pathway data is available from many databases dependent on biological focus. The fragmented nature of pathways across multiple databases makes it difficult to perform inclusive analysis of all known data. To address this issue, many attempts have been made to consolidate pathway databases such as ConsensusPathDB (CPDB) , PathwayRedundancy Control in Pathway Databases (ReCiPa)  uses a pPathcards described a multistep procedure to reduce pathway redundancy, also through pathway merging . Two thrA major application of pathway data sets is pathway enrichment analysis. Both Pathcards and ReCiPa explored the capability of their reduced pathway dataset to improve enrichment results. Enrichment analysis of 830 differential expression sets was performed using the super-pathways generated within Pathcards. The enrichment results from super-pathways tended to be more significant than the enrichment scores of their constituent pathways. Similarly within the ReCiPa study enrichment analysis was performed using genes differentially expressed in obesity. After merging, the top 20 most significantly enriched pathways showed less overlap and greater significance towards the disease, compared to the original dataset.Pathway Distiller implemented an alternative approach by removing redundancy from enriched pathway sets following enrichment analysis . Pathwayelim and weight, which use the Gene Ontology topology to select a representative subset of highly enriched GO terms [All of the approaches discussed to this point have used merging and consolidation to address redundancy. Alexa et al. (2006) demonstrated that redundancy in GO enrichment results could be reduced by selecting a subset of representative terms . PathwayGO terms . The enrGO terms , do not Within this paper we show that set cover can be used to reducing redundancy by selecting subsets of representative pathways. We describe a set of algorithms for reducing redundancy in pathway datasets, as well as a separate algorithm for reducing redundancy from pathway enrichment results. The proportional set cover algorithm and hitting set cover algorithm aim to identify a minimum subset of pathways required to cover the genes in highly redundant, consolidated pathway databases. The generated set covers are not designed to depict the full range of possible pathway boundaries and their accompanying cellular functions, but rather they provide a simplified set of pathways to represent the actions of genes within the dataset. Since the pathways are not merged database and biological information remains directly applicable and functional specificity is not lost through pathway size expansion. The proposed method also removes the risk of biologically distinct pathways being merged. The algorithm’s ability to remove overlap is not limited by thresholds, conferring an advantage compared to approaches such as Pathcards and ReCiPa in which redundancy between pathway pairs can only be removed if the overlap exceeds the threshold. Set cover algorithms also consider redundancy between multiple pathways, rather than just comparing pathway pairs.p-value to cover each differentially regulated gene. Enriched pathways are not merged or altered and the number of enriched pathways required to cover the dataset is reduced. The resulting pathways set can therefore be used as an optimized summary output, conveniently showing the most important pathways for describing the differentially regulated gene set. By increasing the number of differentially regulated genes covered by the most highly enriched pathways, researchers examining the top 10 or 20 pathways are provided with a more inclusive portrayal of the gene set.We also developed the enrichment set cover algorithm for handling pathway enrichment data and applied it to a set of enriched osteoarthritis pathways . In contWe downloaded pathway data from ConsensusPathDB (CPDB), an opensource online collection of pathways, that incorporates 32 sources including KEGG, Wikipathways, PDB, Reactome. CPDB makes these resources available as a single download, which we acquired on 24/09/2015 containing 4011 pathways. We applied the set cover algorithm to the CPDB data set, analyzing it’s effectiveness at: reducing pathway overlap; reducing pathway size variability; and preserving the maximum number of genes in the data set. We found that standard set cover caused unacceptable increases in pathway size, therefore we modified the algorithm and assessed the modified algorithms capability to meet the previous three objectives.Set cover is a well-defined algorithm in computer science for handling overlapping sets of sets. For example, set cover is used by CLASS, a bioinformatics program that maps RNA sequence data to transcripts . Set covSet cover algorithms deal with elements and sets, which relate to genes and pathways respectively. All the unique genes in the data set are collectively referred to as the universe. The aim is to produce a reduced selection of sets (pathways), which collectively cover all the elements (genes) in the universe (dataset). This subset of the original data is called the cover set . Each tip-values. We designed an algorithm that would select the set of pathways with the lowest p-values capable of covering all the genes in the dataset. This ensures that the filtered results return the most enriched pathways available for each gene.When dealing with enrichment analysis data the aim is to reduce redundancy between pathways, while preserving the order of enrichment significance denoted by the To measure overlap across different algorithms we measured the mean number of pathways that each gene appears in. Within the raw data genes appeared in a mean of 12.4 pathways. We refer to this metric as the overlap score.vi). The set cover values correspond to the number of uncovered genes each pathway contains  is the pathway’s gene set and R is the set of all uncovered genes.where . We therefore explored methods that avoid preferential selection of large pathways.GC) parameter. Setting GC below 100% allows the algorithm to finish before the final elements have been covered. We experimented setting GC to 90, 95, 99 and 100% of the number of genes in the data set.As the set cover algorithm approaches completion and the final sets are added to the cover set, increases in data coverage are gained at the expense of redundancy reduction. This is because the final sets required to cover the few remaining genes tend to have the most overlap with other pathways already in the set cover. In addition, fewer pathways are available to cover the final few genes, restricting options to control pathway size. To allow a user-defined compromise between the gene coverage, pathway redundancy and pathway size we introduce the Gene Coverage .where The set-covering problem can be reformulated into the equivalent set-hitting problem. In this formulation genes and pathways are visualized as bi-partite graph in which the pathways are connected to the genes that they contain. In this depiction it is clear that some genes are only linked to a single pathway, which must be selected if the gene is to be covered. The importance of pathways can therefore be considered as a factor of how infrequent their genes are. The hitting set cover is therefore designed to reduce redundancy as much as possible without directly selecting for pathway size.j within the pathways of the data set (fj), then assigned the gene’s value vj as 1/ fj . We wish to reduce redundancy (gene overlap) between enriched pathways and it is essential that the pathways with the lowest possible p-values are selected. Eq. p-values to be selected, unless all of their genes are covered by other enriched pathways with even lower p-values.Pathway analysis is a frequently used method; therefore a modified set cover algorithm to address this situation could be highly useful. The universe represents differentially expressed genes and the sets are enriched pathways generated through enrichment analysis. Enrichment analysis results represent entirely different input data compared to the pathway datasets used in the previous algorithms, as the enriched pathways already have scores . However, many of these were very large increasing the mean size to 87.2 (standard deviation 160.1). These pathways have reduced informativeness since functional specificity is lost. Figure When The proportional set cover algorithm was designed to preferentially select moderately sized pathways. This returned a cover set of 1336 pathways with controlled size variation  shown in Fig. GC is reduced the tendency of the standard set cover to select very large pathways becomes more exaggerated. Decreasing GC also improves the ability of the proportional set cover algorithm to select moderately sized pathways. The hitting set algorithm also tends to select smaller pathways when GC is reduced, since larger pathways often contain more frequent genes. Reducing GC affects pathway size since in the later stages of the algorithm, fewer pathways are available to cover the remaining genes, reducing the available options. Therefore, lowering GC has the ability to help control pathway size when the proportional set cover and hitting set cover algorithms are used.Figure Since the databases that contribute to CPDB contain pathways of different sizes, the set cover generated may preferentially select pathways from some databases more than others.Table p-value lower than 0.05, following the Benjamini-Hochberg correction for multiple testing. Set cover for enrichment analysis reduced the number of pathways required to cover the differentially expressed genes to 23  . From th6 . From p-values from pathway enrichment determine the order in which pathways were considered for inclusion in the cover set. Pathways were omitted if all of the differentially expressed genes that they covered were also covered by more enriched pathways. Note that overlap tends to be higher in the bottom left triangle as pathways added later were often smaller subcomponents of larger pathways. We can see that ‘extracellular matrix organization’, the most enriched pathway, was placed in the cover set first. Next was ‘collagen biosynthesis and modifying enzymes’; however, all of the differentially expressed genes in this pathway are also covered by the larger pathway ‘extracellular matrix organization’, as indicated by the red cell in the ‘collagen biosynthesis and modifying enzymes’ row, ‘extracellular matrix organization’ column. The corresponding cell in the ‘extracellular matrix organization’ row reveals that 24% of the differentially expressed genes in ‘extracellular matrix organization’ are also in ‘collagen biosynthesis and modifying enzymes’.The heat map in Fig. Figure p-values.Some pathways in the enrichment set cover do still show high levels of overlap, for example ‘wnt signalling network’ is included despite 89% of its differentially expressed genes being covered by ‘signal transduction’. This is acceptable because ‘signal transduction’ is more highly enriched than ‘wnt signalling network’, yet the ‘wnt signalling network’ is worth including as it contains three differentially expressed genes that are not in ‘signal transduction’. If ‘wnt signalling network’ had been excluded then these genes would not have been described by the most significant pathway available to represent them. The unmodified top ten enriched pathways only cover 78.0% of the enriched genes. Using the set cover enrichment algorithm increases this figure to 85.2% without disrupting the pathway order given by the enrichment GC parameter allows further reductions in pathway redundancy; for example, if only 95% of the genes in the CPDB dataset were covered redundancy can be reduced by up to 88%. In addition reducing GC increases pathway size control when the proportional set cover and hitting set cover algorithms are used.We described algorithms suitable for reducing overlap in large pathway data sets allowing multiple databases to be amalgamated without excessive redundancy impeding the usefulness of the resource. Standard set cover is the best algorithm to reduce the number of pathways required to cover the data set, but significantly increases pathway size, which can be controlled by proportional set cover or hitting set cover. The proportional set cover is the best algorithm for controlling pathway size and the hitting set cover is the preferred choice for covering all of the genes in the dataset with minimal pathway redundancy. We showed that reducing the p-values. As an application we used the modified set cover algorithm to reduce the results of enrichment analysis from a large osteoarthritis data set. We found that 5 out of the 10 top ranking pathways could be omitted as they were subsets of more highly enriched pathways. Overlap between pathways returned from enrichment data is not always immediately obvious and requires further consideration. By reducing this redundancy, data interpretation is made more intuitive. Reducing redundancy also allows the user to explore substantially more of the data set using the same number of pathways.For pathway enrichment analysis we aimed to reduce redundancy while selecting the most significantly enriched pathways based on The enrichment set cover algorithm presented within this study differs from existing methods implemented by ReCiPa and Pathcards, since enrichment analysis is performed prior to reduction of redundancy. This is because the different sets of pathway boundaries available in the full dataset may optimally fit the differentially expressed genes. For example, comparison of the ‘apoptosis’ taken from KEGG, Reactome and Wikipathways, reveals that many of the proteins are specific to a single database . This isp-values.Set cover uses greedy heuristic methods, which provide good approximations of the optimal solution in a time effective manner. These methods are extremely efficient and can be run in a matter of minutes, however it should be noted that they do not guarantee an optimal solution. This is particularly true for the proportional set cover algorithm where the randomness of early selections influences the result. However, all possible outcomes result in reduced redundancy. The enrichment set cover algorithm is exempt from these considerations unless multiple pathways have identical p-values) utilize relative measurements taken across the dataset and are not affected by the overall composition of pathway redundancy. The only assumptions made are that some variability will be present in both the size of pathways and the redundancy between pathway pairs. Since pathway datasets are unlikely to be entirely uniform in each of these aspects, the methods described are capable of iteratively selecting pathways on the basis of their relative vi scores across all foreseeable datasets.It should also be noted that set cover algorithms, including the algorithms presented within this paper, are capable of reducing redundancy regardless of structure of pathway overlap within the initial data set , 21. ThaWe have provided a method to dramatically reduce redundancy in pathways facilitating a more concise portrayal of cellular processes, while avoiding the issues introduced by pathway merging. Our algorithms are publicly available and have wide applicability to analysis of pathway datasets from any organism.Additional file 1:Table S1. Enriched pathways from the osteoarthritis dataset . The set cover column indicated the 23 pathways that were included in the set cover. (DOCX 19 kb)


Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways.Our work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases.We present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically.The online version of this article (10.1186/s12859-019-2779-4) contains supplementary material, which is available to authorized users. Due to the technological and scientific progress, we see a tremendous increase in the knowledge and the amount of collected data in the area of molecular biology and biochemistry over the past years, and computational tools play a major role in this development. One example of increasingly investigated and abundant data are metabolic pathways, i.e., network structures of molecular interactions of biological systems. Collections of such pathways form more complex and hierarchical biological networks, and their careful analysis and understanding are important aspects for many life sciences researchers. Research efforts provide new experimental results, which expand the known networks or require modifications and revisions of previous data. Various initiatives and public databases exist to maintain and curate this growing set of biological network data.An important step for researchers to make sense of such large networks of biological pathways is to explore visualizations such as pathway diagrams and network layouts, and use them to communicate their respective scientific results in the context of larger biological networks. Automatic network layout algorithms thus become indispensable in the sense that manually creating diagrams of large networks is a very time-consuming if not impractical task, especially considering that the underlying data may change frequently and require permanent layout updates. Sometimes even drastic layout changes are needed. For example, glucose is traditionally considered as a fast supply of energy, while it is nowadays demonstrated that it also affects cancer metabolism . A manuaRecon 2  is accomplished by minimizing the objective function objratio=wratio·δ, where δ is defined as δ=|Bx−R·By| for the user-specified target aspect ratio R. Our default is R=4/3.[Expected aspect ratio (CS2)] is done by minimizing the objective function γx and γy are displacements between pairwise block centers along x and y axes, which are defined as [Long shared boundary (CS3)] is achieved by minimizing the objective function Finally, we minimize the sum of three objective terms as follows: woverlay=10,wcompact=1000, and wratio=1 for the weights in our system.Note that by default, we empirically employ xi,yi) for each bc(i) but add a right-top reference point  to facilitate adjusting block boundaries. Our hard (CH2–FH2) and soft (CS1–FS2) constraints are summarized as: Overlap-free block placement: As defined previously.(CH2) Minimal block width and height: The minimum drawing area must be preserved.(FH1) Attached boundary: Attached boundaries must stay.(FH2) Compact layout: As defined previously.(CS1) Expected aspect ratio: As defined previously.(CS2) Long shared boundary: As defined previously.(CS3) Area maximization: Drawing area should be maximized.(FS1) Block aspect ratio: Aspect ratio of each block should not be changed drastically.(FS2) Once we have a compact layout for the block placement, we then fine adjust the four boundaries of each block for better utilizing the screen space. This allows us to align block boundaries to avoid bends when routing the edges. The optimization partially includes the aforementioned constraints in “pi−xi≥Wi and qi−yi≥Hi, where Wi and Hi are the width and height of the block computed previously. Note that this is the constraint employed to guarantee minimal drawing space of each block bc(i).Our objective is to preserve the minimal drawing space computed from the previous step in “x and y axes and are formulated as: We also incorporate hard constraints to retain the shared boundaries computed from the previous optimization. Since we already know the shared boundary between adjacent blocks, the idea here is simple. We force the distance between the shared boundary to 0, while restricting the corresponding upper bounds always locating higher than the lower bounds among the two blocks. These constraints are added by investigating all the shared boundaries along We again introduce several soft constraints for better usage of screen space, including compact layout (CS1), expected aspect ratio (CS2), area maximization (FS1), long attached boundary (FS2).[Area maximization (FS1)] is achieved by minimizing the distance between the block boundaries and the four boundaries of the map domain. This objective is therefore defined as xi=xdistL(i),xupper(i)−pi=xdistU(i),yi=ydistL(i), and yupper(i)−qi=ydistU(i).[Block aspect ratio (FS2)] is similar to constraint expected aspect ratio (CS2), where we apply the idea to each block. The objective κi=|pi−xi−(qi−yi)|.Finally, we minimize all objective terms by summarizing them as: objfloorplan, and set warea=1000 and wblockratio=100 by default in our system.We empirically employ the same weight value for The grid pattern is commonly chosen in urban planning even though its infrastructure cost is high, because the frequent intersections and the orthogonal geometry of the grid pattern help pedestrians with detecting orientation and selecting the path to desired destinations . This isVD. To reduce the visual complexity, we place this orange node on the boundary of each block to emphasize its difference  and global flow networks GN=, respectively. We first build a local flow network GM blocks using a TreeMap  to discrwork GM “” sectionwork GM “a. Based GM  for each edge em= as: Since our goal here is to create a grid network suitable for winding an edge to avoid intersections with the drawn orthogonal layout, we build the network ch(e) counts to one if edge em is intersected with any edge lh in the orthogonal layout, while we assign smaller penalty if the crossing angle is close to 90∘.to penalize if the edge is long or has intersections with existing orthogonal layout. Note that Until here, the basic information of node relationship is presented (black arrows in the result images). To highlight the duplicated metabolites, we incorporate additional colored edges between duplicated metabolites in different urban blocks in order to present the transaction flows between a pair of reactions sharing the same metabolites.Again, we construct global flow networks for connecting pairs of identical chemical components appearing in different urban blocks  indicates the value of used capacity from the computed networks, and S, D, and B are edges connected to the supply, demand, and road nodes, respectively. The edge weight function wg) is simply defined as |va−vb|. The MAX_CAPACITY is a user-defined constant to limit the maximum flow on a road. The problem is again solved by the same classic flow algorithm [where lgorithm .Our system has been implemented on a desktop PC with an AMD Ryzen 7 1800X CPU  and 64GB RAM. The source code was written in C++ using Qt Graphics for rendering maps and user interface, Boost Graph Library for the graph data structure and minimum cost maximum flow algorithm, CGAL for computational geometry algorithms, and IBM CPLEX for the MIP-based optimization. Since our framework is designed to incorporate different graph layout algorithms for decomposed sub-graphs, we introduced orthogonal layout algorithms here due to their high computational complexity and familiarity in most of the database , 4. UserSystems Biology Markup Language (SBML) [Systems Biology Graphical Notation Markup Languages (SBGNML) [Metabopolis [The biological relationship is often recorded using the e (SBML) , an exte(SBGNML) , which iabopolis .overlap-free block placement (CH2) constraints are bottlenecks in our approach. We can formulate them as lazy constraints in modern MIP solvers so they will be only included when the result violates the constraint. This is done together with introducing pairwise relative positioning (CH3), to restrict the solution space in a reasonable way.Since the space partitioning scheme in Metabopolis is achieved by solving a MIP problem, we cannot only control solution space, but profit from algorithmic speedup by solvers such as IBM CPLEX, Gurobi, SCIP, or Xpress. As compared to other constrained optimization techniques such as greedy algorithm, simulated annealing approach, or genetic algorithm, MIP is more prominent here in the sense that it also provides the optimal solution by time, which cannot be achieved or predicted by other heuristic approaches. Especially in one of our use cases, where we generate high quality maps to serve as an overview map for databases, we need a reliable approach that generates a diagram with high readability. With modern MIP techniques, the formulation can be also relaxed to find a feasible, but possibly suboptimal, solution in shorter time. Moreover, it turned out that Our constrained floor-planning algorithm relies on an input graph skeleton for guiding block placement, which helps preserving the mental map of users as well as limiting the search space for the NP-hard floor-planning problem. If all edges of the graph are removed, the problem will then become conventional floor-planning problem, which may not be sufficiently solved using MIP solvers. We suggest users to add at least one edge to each block for better control of the layout.ColorBrewer color sets, are incorporated for selection. The high level category names (ex. names of ontology labels or compartments) are placed at the center of each block, while we rotate the name 90 degrees when the block is vertically extremely long. At the intermediate level, we also enlarge the reaction names by a factor of 3, so that names become readable when the screen space is sufficiently large. The font intensity of category names is decreased as the zoom level increase, while the names of metabolites and reactions change contrarily. Metabopolis also includes traditional interactions such as highlighting reactions and metabolites, as well as corresponding neighbors.We also incorporate a content-adaptive navigation scheme to support the layout navigation. The zoom-levels have been categorized into three, including the overview level, the intermediate level, and the detailed level, where the font size and intensity are changed accordingly. Four color schemes, including the predefined, the monotone, the pastel, and the Glycolysis Gluconeogenesis (orange block) shows the cytoplasmatic oxidation of glucose to produce ATP. Using our diagram (each category is represented by a different color), we immediately observe that the entire process only happens in Glycolysis Gluconeogenesis since all positions of the corresponding reactions are constrained within one block. However, the produced ATP is placed on the boundary of the block here, which means that it also serves as reactant of other reactions outside the Glycolysis Gluconeogenesis. In biology, ATP is the key energy molecule utilized to drive other biological reactions. The green route shows an example of ATP conversion in the cytoplasm to enable urea synthesis (the blue route). Compared to Glycolysis Gluconeogenesis, reactions involved in urea synthesis are more complicated, since they require to bring ammonia from mitochondria to the cytoplasm to complete the synthesis. This is visualized by showing the reactions involved actually belong to Urea Cycle, Alanine and Aspartate Metabolism, and Transport pathways in our diagram.The Metabopolis diagram representing the major categories of the human metabolic network is shown in Fig. KEGG map [Figure KEGG map , since bKEGG map . We alsoReconMap [Figure ReconMap , 69. To biological pathway ontology (the standard identifiers), allow researchers to share the corresponding knowledge by querying for categories of interest [Since pathway diagrams provide a way to understand fundamental metabolisms and the responses of diseases to drugs, researchers increasingly pursue network-centric approaches to investigate the functionality and controllability of pathways. There are two typical pathway categories: small pathway maps  often summarize a set of core chemical reactions and large pathway maps , which collect extensive processes among one species. The former pathways such as Fig. interest .V|,|E|, and density (Den) correspond to node number, edge number, and density before node duplication, respectively. Similarly, |VD|,|ED|, and density (DenD) are the respective values after node duplication. The edge density function for Den and DenD is defined as |E|/(|V|2−|V|) [Table V|2−|V|)  in TablePrefuse Force-Directed Layout [Compound Spring Embedder Layout (CoSE) [Table d Layout , Compount (CoSE) , and MetFor validating our approach we reached out to domain experts who manually create pathways in their daily research and interviewed them after they investigated several maps generated using Metabopolis. This includes one of our co-authors, Dr. Filipa L. Sousa, one member from ReconMap , three mKEGG pathway maps [Cytoscape [Dr. Sousa mentioned that compared to conventional network visualization such as the overview map from way maps  and a foytoscape ), one haReconMap [Graph Drawing Contest 2017 [CellDesigner [CellDesigner. Dr. Funahashi is impressed by the layout controllability of the maps generated by Metabopolis, while he suggests to fully control the sub-patterns to make the maps easier to memorize and relax the shapes of rectangles to create distinct block structures. Dr. Minoru Kanehisa and his team members have been working on manually creating pathway maps for KEGG pathway database [Dr. Ronan Fleming from ReconMap  has initest 2017  and consDesigner  for manudatabase  for 20 yAlthough all domain experts agree that the manually adjusted layout still performs better in terms of visual quality , they have inspected our automatically generated diagrams with great interest and forsee its future potential. We were recommended to include the layout algorithm in conventional software packages, so that both biologists and bioinformatics community can connect our layout to existing biological databases, and to incorporate search functions to facilitate sophisticated pathway analysis, including retrieving additional information regarding genes, catalyzing reactions and other biological information such as KOs, COGs, etc. Advanced editing is also advised to revise the datasets. After the discussion, we have improved the display of all nine combinations of directional/bidirectional edges shown in Fig. This paper presents the very first approach to automatically design biological pathway diagrams in urban map style by integrating a constrained floor-plan technique together with a visual hierarchical representation using a network flow algorithm for edge routing. Our approach ensures the appropriate partitioned space for categories with biological meanings, while seeking the balance between size of categories and neighborhood relationship. This has been done by formulating the problem as a mixed integer programming model. We devise a visual hierarchical design for metabolites by borrowing the concept of urban maps, where we reasonably bind the line sets connecting to identical metabolites to restricted roads bounded by categories. The paths have been distributed by flow-network algorithms. An interface for editing, navigating and highlighting target metabolites is also provided for further customization.Cytoscape [Metabopolis is the first work aiming at automatic generation of entire large metabolic pathways, thus we open a myriad of opportunities for a domain where there were only few and mostly non-customized manually designed maps until now. Future work will involve the creation of a visual web repository, where biologists can continuously add or create their maps together with sharing the maps with the rest of the community. Everyone is welcomed to contribute to the knowledge collection and suggest preferable visual representations since the layout is expected to be created associated with preferred tasks. We will allow users to create and update the diagram with domain-specific information so that we can automatically transform this knowledge to mathematical equations for the layout optimization. To achieve this, we have been in collaboration with CellDesigner  on plugiytoscape  to initiytoscape , a tutorAdditional file 1: AppendicesIn Appendices, we first compare and describe the experimental results generated using conventional layout algorithms and our approach. Afterward, a tutorial of Metabopolis has been included to explain the usage of the software. (PDF 7840 KB)


Currently, the in vitro bioengineering approach for the RA-I receptor is not realizable given the lack of knowledge of its morphogenesis. This paper demonstrates our first attempt to interpret the cellular morphogenesis of the RA-I receptor. We found indications of extrinsic mechanical force nearby the RA-I receptor in the developing fingertip. Using a mechanical compression device, the axon of dorsal root ganglion (DRG) neurons buckled in vitro into a profile that resembled the morphology of the RA-I receptor. This work encourages further implementation of this bioengineering approach in tactile receptor-related research.Rapidly adapting type I (RA-I) mechanoreceptors play an important role in sensing the low-frequency vibration aspects of touch. The structure of the RA-I mechanoreceptor is extremely complex regardless of its small size, limiting our understanding of its mechanotransduction. As a result of the emergence of bioengineering, we previously proposed an  These receptors are located at the middle region between the epidermis and the dermis . Although they play an important role in sensing the low-frequency vibration aspects of touch, their mechanotransduction is not completely understood. Most of the research concerning mechanotransduction of RA-I receptors has been conducted while including mechanical properties of the surrounding environment (skin), which are highly varied between species and individuals. This limitation still exists because the isolation of RA-I receptors is extremely difficult. Regardless of its small size, the structure of the RA-I receptor is the most complex among mechanoreceptors, consisting of spiral-like axons, lamellar cells (Schwann cells), and a collagen capsule  and interesting to most researchers. How and why the receptor forms this complex configuration is not well understood. Histological observations reveal that axons of RA-I receptors begin at the somata, which lies in the dorsal root ganglion (DRG), and travel a long distance towards the fingertip. Despite the various periods of development between species, the first axon terminal is typically straight when it is innervated in the dermal papillae. After reaching the apex of the dermal papillae, this axon terminal continues to deform, whereas the development of its surrounding components (such as lamellar cells and collagen capsule) starts later5. The other axon terminals penetrate the receptor laterally and appear to follow the course of the first penetrated axon terminal. Inevitably, the deformation of the first penetrated axon terminal is essential for the development of RA-I receptors. Which factor  causes the deformation of the first penetrated axon terminal is unclear.To overcome the existing limitation, we previously proposed a bioengineering approach by which RA-I receptor-like morphology could be represented in vitro. In the chemical category, chemical gradients and laser-induced photolysis have been used to control the concentration of attractive proteins nearby growth cones7. In the physical category, topographical8, electrical9, optical13, and microfluidic14 cues, as well as a hybrid approach, such as optofluidic flow16, have been employed for the purpose of axonal guidance. In recent studies, microfluidic flow has been applied directly to provide pressure on growth cones14; alternatively, a focused laser beam has been used to trap and rotate birefringent beads16, exerting shear force on growth cones. Both approaches have resulted in axonal growth control at a fine spatial resolution. These works demonstrate the promising ability to create the complex structure of an axon terminal exclusively using physical cues. Considering the morphology of axon terminals in RA-I receptors, a role for physical cues  in the deformation of the first penetrated axon terminal is reasonable to suspect.In recent years, researchers have identified various factors that may alter the morphology of axon terminals in vivo. Our findings help explain a portion of the morphogenesis of the RA-I receptors. Ultimately, the bioengineering approach described in this paper may provide a novel ability to examine the developmental and functional aspects of RA-I in vitro.In this study, we conducted a series of experiments to investigate the effect of mechanical forces on the deformation of axon terminals with regard to the development of the RA-I receptor. We determined the relationship between the compression of collagen fibres and the deformation of axonal terminals using two-photon microscopy on stained serial sections. These findings indicate the existence of extrinsic mechanical forces at the developing RA-I position. A stretchable chamber was used to provide extrinsic mechanical force to the axon terminals innervating a dermal papillary structure (made of silicone). We observed that the axon terminals deformed in the same manner as the developing RA-I receptors 17. On the other hand, the collagen fibres at the base of dermal papillae were dispersed at Pd1 . The calculation of the Mann-Whitney U test revealed significant differences between Pd1 and Pd4 as well as between Pd4 and Pd7 (p < 0.001). The difference between Pd4 and Pd7 was less significant than that between Pd1 and Pd4 , as well as between Pd4 and Pd7 . These results indicate that the collagen fibres were compressed during the development process.In search for evidence of an extrinsic mechanical force on nearby RA-I receptors in the developing mouse fingertip, we used an immunostaining method on serial sections with the plane perpendicular to the longitudinal axis of finger  1, Pd4, and Pd7). Given that collagen is the most abundant protein fibre in the dermis and that it provides strength and cushion, typically assisting the axon terminal, any changes in collagen morphology would likely reflect the existence of mechanical forces. Therefore, we focused on the changes in collagen fibres with regard to the morphological change in axon terminals at the apex of the dermal papillae (where RA-I is located). At Pd1, the axon terminals were mostly straight Fig. . At Pd4 Pd1 Fig. . During Pd1 Fig.  regardlePd4 Fig. . The cha19. The dermal environment surrounding axonal terminals in dermal papillae is also viscoelastic. Therefore, the axon terminal should theoretically buckle in the same manner as a thin elastic rod in an elastic medium when sustaining a compressive load from one end20. In short, the axon terminals of developing RA-I mechanoreceptors are hypothesized to buckle due to the extrinsic mechanical force -4-(4-(Dibutylamino) Styryl) Pyridinium Dibromide). The stained profiles were consistent with those observed by phase-difference microscopy Fig. . The bucAlthough the buckled axon terminals were observed in the apex of the dermal papillary structure, whether the dermal papillary structure itself was required for the deformation of the axon terminal is unclear. To answer this question, we performed an experiment where the DRGs faced a flat structure Fig. . An epid14 have demonstrated the effect of fluid flow on the growth of axons. The DRGs were placed near the bases of dermal papillae structures . The culture medium was carefully exchanged everyday to avoid the addition of any external force in the culture region given that previous studiesres Fig. . On the res Fig. . After ores Fig. . Evidencres Fig. . These rin vitro experiments, especially at the apexes of dermal papillae structure, is tedious. Therefore, we performed a simulation with an elastic finite element (FE) model, including the dermal papillae structure and collagen gel, with respect to their measurements. The model of the axon terminal was omitted for the sake of simplicity. In addition, we focused more on the stress that occurred in the extracellular environment than the stress on the inside of the axon terminal itself. The model was validated and then used to determine the magnitude and distribution of stress in the apexes of dermal papillae structures. This approach is illustrated in detail in Supplementary Fig. Next, we quantitatively examined the effect of the extrinsic mechanical force on the buckling of axon terminals. However, direct measurement of the force occurring during the in vitro compression process and compared these data with the calculated displacement of the following position in the model. Figure in vitro experiment  with the Young’s modulus of 144 Pa23, Eq. λ of 26.4 μm, which is consistent with Fig. λ = 2 × the length of the axon terminal (Euler buckling). Given that this value is significantly smaller than the calculated stress at the following position of the FE model, it additively validates the practicality of axonal buckling in this study. Interestingly, Eq. To determine the minimal stress required for the buckling of the axon terminal, we then treated the axon as a bundle of microtubules . Theoretically, the critical force for the buckling of microtubules in an elastic medium is presented by the following equations22. Given that microtubules are the main components contributing to the stiffness of an axon, the axon is expected to inherit their buckling behaviour. Various papers have promoted the idea where the axon, as a bundle of microtubules, may buckle under compression. Although supported by the results of mathematical simulations24, the buckling of the axon under compression has rarely been demonstrated experimentally26. In this study, we demonstrated that an in vitro extrinsic mechanical force could cause the deformation of axon terminals in the same manner as a developing RA-I receptor. The deformation process, in which the axon terminals are initially straight and then bend so that their lateral morphology becomes sinuous, is similar to those previously described for the RA-I developmental proces27. Of note, the dye FM1-43X used in this study inhibits the behavioural responses of sensory neurons to mechanical stimuli28. Therefore, the buckling of axon terminals presented in in vitro experiments was exclusively a physical phenomenon and occurred almost immediately after compression. This phenomenon is different from the physiological phenomenon where the mechanical stimuli enhances axon outgrowth by activating its mechanosensitive channels, like TRP channels29. With regard to such physiological phenomenon, the behavioural response of the axon terminal after being compressed for a period of time  will be investigated in our future work.Microtubules in living cells buckle on a short wavelength when they are pressed while being surrounded by cytoskeletonIn vivo, a decrease in distance between two neighbouring collagen fibres in the dermis and a decrease in dermal thickness were observed, suggesting that the extrinsic mechanical forces (compression) are generated nearby the RA-I receptor in a period of time (from Pd1 to Pd7). The origin of the extrinsic mechanical force during the development of the RA-I receptor remains unknown. In a recent study, Seidenberg-Kajabova et al.30 observed volar pad prominence and regression (12th and 13th week in human foetus). Given that the bone maintained linear growth  in the same manner as shown in the developmental process of the RA-I receptor from Pd1 to Pd4, it is important to note that the complexity of the inner activity of the axon should not be underestimated. Tamada et al.31 demonstrated that the rotation of growth cone filopodia can drive axonal turning. Additionally, to achieve the final spring-like shape (like matured RA-I receptors), rotational force is also necessary in addition to the compression force. The inner activity of the axon can be reasonably hypothesized to be the origin of this rotational force.Although our last in vitro. Our method uses a simple yet effective system, consisting of a mechanical device (3D printable) and a stretchable chamber. This approach is expected to help reveal a number of features and effects of the surrounding environment on neurons and RA-I receptors that have not been observed, for example, the underlying mechanism by which the RA-I mechanoreceptor continues to develop after Pd4.In future work, the bioengineering approach presented in this paper can be employed for reconstruction of desired RA-I mechanoreceptors 7 and 3D printer32) could achieve a smaller resolution, hence resulting in s a skin model with improved detail. A detailed skin model is required to address the effect of dermal papillary structure during the development of RA-I receptors. Second, future studies should introduce the co-culture of DRG and Schwann cells to obtain a better replica of RA-I mechanoreceptors. The protocol available in34 should be used to investigate the myelination. Third, to investigate the mechanotransduction of RA-I receptors, one should also consider neural recordings. Various techniques, such as electrode arrays, calcium imaging, patch clamp, and voltage sensitive dye proteins, are broadly used in neuroscience. A recent study35 demonstrated that DRG neurons are robust in culture, maintaining normal electrophysiological functions for a month. Non-contact methods, such as calcium imaging and voltage-sensitive dye proteins, could have an advantage compared with electrode array and patch clamp techniques given that these latter techniques typically require a stable culture environment. As mentioned above, the dye FM1-43X used in this study may inhibit the behavioural responses of neurons to mechanical stimuli, and future studies that involve the functional aspect of RA-I receptors should consider another dye to visualize the morphology of axon terminals.Finally, a number of limitations in our approach at the present stage should be noted. Further investigations of the developmental aspect of RA-I mechanoreceptors requires improvements in our technique. First, the width between two apexes of the dermal papillary structure used in this study was large due to limitations of the laser cutter. A mould made by more advanced fabrication technology  of mechanoreceptors, the study of which is limited with traditional approaches.To conclude, we demonstrated that the axon terminal derived from dorsal root ganglion can buckle Pregnant female mice (Mus musculus) were obtained from SLC  or by mating in Nagoya University. Embryonic day (E) zero was defined as the day of vaginal plug identification. The Animal Care and Use Committee of Nagoya University approved all protocols for animal experiments. All experiments and methods were performed in accordance with relevant guidelines and regulations.μm). Frozen sections were treated with the antibodies against PGP 9.5 . After washes, sections were treated with Alexa Fluor 546-conjugated secondary antibody  and subjected to two-photon microscopy  using a 25X water-immersed objective lens . Dermal collagen fibres were detected by autofluorescence obtained with emission at 450–550 nm36 by a wide-pass filter .Mice were fixed with periodate-lysine-paraformaldehyde (PLP) fixative. The distal pulp of mice was fixed in 4%PFA, immersed in 20% sucrose, embedded in Optimal Cutting Temperature (O.C.T) compound , and then frozen and sectioned perpendicularly to the longitudinal axis  and Python statistical libraries . To reduce noise, all images were enhanced in contrast for 0.3% by FIJI software and converted into grey-scale beforehand.μm in height and 500 μm in width. Silicone  was poured into the mould and incubated overnight at room temperature. Then, we carefully broke the mould and placed the silicone structure in 70% ethanol for two hours. Subsequently, UV light was used to sterilize the silicone structure for one hour. The epidermis model without papillae was created using the same methodology.The model for the epidermis with papillae, which mimics the concave-convex dermal papillary structure, was created as follows. First, an acrylic mould was fabricated using a laser cutter  with a 10-mm × 20-mm bottom face and 2-mm depth  of ICR mouse were used in this study. The DRGs were extracted from the mouse at E13 (Embryonic day 12) or E14 and preserved in medium (containing serum). A silicone chamber  was uniaxially stretched 40% using a manual-operated stretcher . Then, the epidermis-like structure (model for epidermis) was placed at the fixed tip of the chamber. In the pre-stretched chamber, isolated dorsal root ganglions at E13 or E14 were mounted in collagen gel  and cultured in DMEM/F12 containing horse serum (5%), foetal bovine serum (5%), N2 supplement , EGF , bFGF  and NGF 2.5 S .The entire chamber was covered by a 60-cm dish to protect the chamber from moisture and preserved in the incubator for 15 min. The culturing medium was added after the collagen gel had completely solidified. After one day, the chamber was relaxed to the original shape, thereby compressing the gel-embedded DRG neuronal axons  was used to fabricate the dermis model. Then, the skin model was compressed under similar conditions (without DRGs), as in compression experiments of DRG axon terminals. Microscopic images before and after compression were obtained using an inverted fluorescence microscope , and the displacement of microbeads was measured using FIJI.39ith sample in the FE model, and di denotes the displacement of ith sample obtained by observing fluorescent beads. In the final experiment, the Young’s modulus of collagen gel was set as 50 Pa, resulting in the smallest RMSE  was developed and validated based on the inner deformation of collagen gel (Supplementary Fig. entclass1pt{minimaSupplementary Information


Oestrogen receptor, Wnt, and PI3K-FOXO pathway assays have been described previously. Here, we report model development for androgen receptor, Hedgehog, TGFβ, and NFκB pathway assays, biological validation on multiple cell types, and analysis of data from published clinical studies . Multiple pathway analysis of clinical prostate cancer (PCa) studies showed increased AR activity in hyperplasia and primary PCa but variable AR activity in castrate resistant (CR) PCa, loss of TGFβ activity in PCa, increased Wnt activity in TMPRSS2:ERG fusion protein-positive PCa, active PI3K pathway in advanced PCa, and active PI3K and NFκB as potential hormonal resistance pathways. Potential value for future clinical practice includes disease subtyping and prediction and targeted therapy response prediction and monitoring.Signal transduction pathways are important in physiology and pathophysiology. Targeted drugs aim at modifying pathogenic pathway activity, e.g., in cancer. Optimal treatment choice requires assays to measure pathway activity in individual patient tissue or cell samples. We developed a method enabling quantitative measurement of functional pathway activity based on Bayesian computational model inference of pathway activity from measurements of  They can be categorized as hormonal nuclear receptor pathways , developmental pathways , growth factor pathways  and immune pathways .Signal transduction pathways control basic cellular processes such as cell division, differentiation and migration and play important roles in disease pathophysiology5.During the past few decades, targeted drugs have become clinically available. They aim at correcting disease pathophysiology and ‘target’ specific locations in signalling pathways to modify pathway activity. While frequently developed for the treatment of cancer and immune-mediated diseases, treatment of other diseases is being pioneered. Depending on the clinical application, the goal of treatment can be to increase specific pathway activity (for example to activate specific immune cells) or to inhibit it . In general, only a subset of patients with a specific disease responds to a targeted drug, and choosing the right drug for a patient is a major challenge. Developing diagnostic assays to reliably predict therapy response has proven difficult. Efforts to predict response in cancer patients based on genome mutation analysis fail in the majority of patients despite being effective in selected casesfunctional activity of signal transduction pathways in a cell/tissue sample are expected to improve prediction of therapy response. During the past decade, we have developed a new computational approach to quantify signal transduction pathway activity in individual cell or tissue samples based on measurements of mRNA levels of direct target genes of a transcription factor belonging to a respective signalling pathway7. The general concept and development of the first pathway computational models  have been described previously8. We describe here the development of similar computational pathway models for quantitative measurement of activity of the androgen receptor (AR), Hedgehog (HH), TGFβ, and NFκB pathways.Assays that measure 10. Physiological activation of the HH pathway results from ligand  binding to the PTCH membrane receptor, leading to activation of the GLI transcription factor and transcription of GLI target genes13. TGFβ ligands bind membrane TGFβ-type II receptors to recruit TGFβ-type I receptors and induce a transcription factor complex composed of an R-SMAD  and SMAD4, initiating transcription of target genes16. The transcription factor of the nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB) pathway is typically activated by a cytokine such as TNFα18. All these signalling pathways can be activated in disease by abnormal availability of ligand, abnormal crosstalk with another pathway, e.g., the PI3K pathway, or  mutations in key pathway genes19.The androgen receptor is a member of the nuclear receptor family, and upon binding androgens such as testosterone or dihydrotestosterone (DHT), it becomes transcriptionally activeIn addition to biological validation of the pathway model-based assays on a variety of cell and tissue types, example clinical studies have been analysed to illustrate potential clinical utility. First envisioned applications are cancer subtyping and therapy response prediction. We expect our models to also have potential for diagnosis, subtyping and management of other diseases as well as for drug development and life sciences applications.8. In brief, the computational network model for a pathway is constructed to infer the probability that the pathway-associated transcription factor is actively transcribing its target genes  or in vitro , and differential expression upon pathway activation. Based on this accumulated experimental evidence information, a ‘direct target gene evidence score’ was constructed, and candidate genes were ranked according to this score. For the final selection, consistency of evidence obtained by multiple expert research groups and on multiple cell types was taken into account . Approximately 25–35 target genes per pathway were selected for creation of the computational pathway models. This number is high enough to ensure robustness and sensitivity of the pathway assay while limiting the selection to only the highest evidence target genes to enable maximal specificity. Importantly, target genes were only selected based on evidence for reproducible and specific transcription factor-induced transactivation across various cell types, irrespective of the function of encoded proteins, since we interpret target gene levels solely as evidence of transcription factor activity associated with a signalling pathway.Target genes for AR, HH, TGFβ and NFκB pathway models were selected according to the same principles as described for Wnt and ER pathway models using available scientific literature. For each putative target gene, evidence was assessed for its gene promotor region containing a response element motif for the respective transcription factor, functionality of the respective promoter , binding of the transcription factor to the respective response/enhancer element www.genome.ucsc.edu)20. Typical errors encountered during manual curation were alignment of probeset sequences with introns of the target genes and probesets positioned on the opposite strand, and in some rare cases, probesets were even found on completely different chromosomes than the respective target gene. During the same process, probesets that were missing in Bioconductor were added to the list. The final selection of probesets can be found in the Supplementary Information.Probesets on the Affymetrix HG-U133Plus2.0 microarray associated with the target genes were selected based on the Bioconductor package available in R and manual curation using the latest information available on the UCSC Genome Browser . For this purpose, Affymetrix data can be used from a variety of sources, such as cell line experiments, patient-derived xenograft (PDX) mice, or clinical samples, including data from public datasets  database of preclinical or clinical studies). In the current study, models were only calibrated once and subsequently frozen and validated on a variety of independent datasets from various cell and tissue types of preclinical and clinical studies .P that the respective pathway is active in the sample. Such a probability P may be used directly as read-out or first be transformed into a log2odds value log2(P/(1 − P)), as the latter may show more detail if P is close to either 0 or 1. The log2odds value is referred to as ‘Pathway activity score’, can be used in a quantitative manner to identify differences in pathway activity between samples, and is used in all figures and results in this paper. Optionally, for standardization purposes, the log2odds values can be converted to a 0–100 scale.After model parameters have been calibrated, they are frozen, and mRNA probeset measurements of a newly measured sample can be entered into the model, after which Bayesian inference is used to estimate the probability P or on the log2odds values  resulting in activation of the ER transcription factor22. The PI3K pathway is a growth factor pathway activated by growth factors such as Epidermal Growth Factor (EGF), resulting in activation of PI3K and Akt with consequent removal of the FOXO3 transcription factor from the nucleus and loss of its transcriptional activity24. Interpretation of analysis results of the Wnt and ER pathway models is straightforward since they measure transcriptional activity of β-catenin/TCF and ER transcription factor, respectively; for the PI3K pathway, this is more complex. In brief, the PI3K pathway model measures activity of the FOXO transcription factor, which is inversely related to PI3K pathway activity in the absence of cellular oxidative stress25. In the presence of oxidative stress, the FOXO transcription factor becomes alternatively phosphorylated and activated to include transcription of target genes that protect against oxidative stress, i.e., the Superoxide Dismutase 2 (SOD2) gene. The expression level of this FOXO target gene is measured in every sample based on Affymetrix probeset hybridization values and is used to distinguish between the two functional activity states of FOXO. For several tissue types, including prostate tissue, mean SOD2 levels in normal (non-oxidative stress) tissue have been determined, and an upper threshold for non-oxidative stress SOD2 was set at 2 SDs over the mean value8. In samples with active FOXO and elevated SOD2 expression level (>threshold), FOXO activity is considered as oxidative stress-induced. When FOXO is active in the oxidative stress mode, it does not function any more as a readout of PI3K pathway activity, and formally, no conclusion can be drawn on activity of this pathway8. In the analysed datasets, SOD2 levels were below the pre-determined oxidative stress threshold for prostate tissue and PI3K pathway activity could be directly inferred from the FOXO activity score8.On a number of prostate cancer datasets, a multiple pathway analysis was performed. In addition to the pathway models described here, the previously described Bayesian model for measurement of growth factor PI3K pathway activity, as well as the Bayesian models for oestrogen receptor (ER) and  Wnt pathways, were used as described previouslywww.ncbi.nlm.nih.gov/geo). A list with all GEO datasets that were used, with associated publications, is available in the Supplemental Information.All calibration and validation datasets, as well as datasets used for exploration of potential clinical utility, consisted of Affymetrix HG-U133Plus2.0 data available from GEO  intensity values, poly-A RNA (sample preparation spike-ins) and labelled cRNA (hybridization spike ins) controls, GAPDH and ACTB 3′/5′ ratio, centre of intensity and values of positive and negative border controls determined by affyQCReport package in R, and an RNA degradation value determined by the AffyRNAdeg function from the Affymetrix package in R29. Samples that failed the QC process were removed from the analysis  was performed on Affymetrix data from each individual sample based on 12 different quality parameters following Affymetrix recommendations and previously published literatureIn principle, two-sided Wilcoxon signed-rank statistical tests were performed. In case another statistical method was more appropriate due to the content of a specific dataset, this is indicated in the legend of the figure. For pathway correlation statistics, both Pearson correlation and Spearman rank correlation tests were performed; since the results were similar, only the Pearson correlation coefficient and associated p value is reported.30 . The HH pathway is known to be active in basal cell carcinoma (BCC) and inactive in normal skin samples; hence, fifteen BCC and four healthy skin samples were used as active and inactive calibration samples, respectively. The resulting pathway scores on calibration data showed excellent separation between normal skin and BCC samples30 Fig. .Figure 231. Untreated fibroblasts had low HH pathway activity, which increased after stimulation with SHH ligand  for HH activity in brain tissue  fell into the HH-active category  and separated well between control and stimulated samples with the threshold at a lower pathway activity score than in the calibration samples . The lung cancer cell line A549 is known to be responsive to the ligand TGFβGFβ Fig. . After fles Fig. . In primles Fig. . In variles Fig. . Finallyles Fig. . Overall37 . The computational model was calibrated on samples from a subtype of diffuse large B-cell lymphoma (DLBCL1) with a known active NFκB pathway, while a specific B cell type served as NFκB-inactive samples37 Fig. . The cal37 Fig. 37–39. A e41 Fig. . In the FNγ Fig. . In skins46 Fig. . Sampless46 Fig. . Inflammnes Fig. . Finallyles Fig. . In conc50. The model was calibrated using data from AR-expressing LNCaP cell lines stimulated with, or deprived of, the AR-activating ligand dihydrotestosterone (DHT)51 . Prostate cancer is driven by the AR signalling pathway, and an important treatment consists of androgen depletion, e.g., by castration)51 Fig. . The mod)51 Fig. . A simil)51 Fig. . Finally)51 Fig. . In conc53. In nearly all patient samples , the HH pathway was active as a consequence of the constitutively activated GLI transcription factor  cells from healthy individuals and patients with ALS were differentiated to oligodendrocytes, cells that provide metabolic support to neurons and generate myelin sheaths58. Oligodendrocytes from healthy persons appeared to have an active TGFβ pathway, while oligodendrocytes from ALS patients were deficient in TGFβ pathway activity , a neurological disease in which demyelination of neurons has been describedity Fig. .59. Accordingly, the NFκB pathway model scored NFκB activity significantly higher in the majority of patients with PF ependymoma A compared to that in non-inflammatory subtype B  ependymoma has been associated with an inflammatory profilee B Fig. .To illustrate clinical relevance of measuring multiple pathway activities, in Affymetrix datasets from clinical prostate studies, ER, AR, PI3K, Wnt, TGFβ, HH and NFκB pathway activities were analysed Fig. . IndividIn summary, AR pathway activity was increased in cancer-adjacent, hyperplasia and PCa tissue and was nearly always associated with PI3K pathway activity in hyperplasia and variably in primary PCa. Wnt pathway activity was increased in TMPRSS2:ERG fusion gene-positive primary PCa but, together with TGFβ pathway activity, was frequently lost in advanced and CR disease. In CR PCa, both AR and PI3K pathway activity were highly variable.per individual patient sample, as an illustration, pathway activity scores are shown per patient for GSE3325  target genes, preferably shown in various cell types and by multiple research groups that are expert in a certain signalling pathway. Although in principle target gene mRNA levels, serving as input for the computational pathway models, can be measured using any modality (like qPCR or RNA sequencing), we have in the first instance chosen Affymetrix HG-U133Plus2.0 microarrays for model calibration and validation purposes because of publicly available preclinical and clinical study data in repositories such as GEO and because processing is standardized. For calibration and biological validation (after freezing the models) of TGFβ, HH, AR and NFκB pathway models, Affymetrix datasets were selected containing data from samples with a ‘ground truth’ pathway activity. For TGFβ and Hedgehog pathway models, epithelial calibration datasets were chosen; subsequently, the models also performed well on brain, blood and bone-derived samples. The NFκB pathway model was calibrated on blood samples and worked equally well when used on epithelial and brain cell types. For the AR pathway model, prostate tissue was the only available cell type for validation purposes. In conclusion, biological validation results were successfully obtained for these pathway models, including on sample data from patients with various diseases, i.e., for the NFκB pathway model allergic contact dermatitis, multiple sclerosis, and lymphomas, for the HH pathway BCC and medulloblastoma, and for the AR pathway PCa.Over the past decade, we have developed a new method enabling quantitative measurement of activity of the major signal transduction pathways in a wide variety of cell and tissue types based on computational inference of pathway activity from measurements of direct target genes of the transcription factor, meaning minimal involvement of cell type-specific proteins, to reduce cell type-specific influences on target gene expression. Also important is that target mRNAs were not selected based on relevance for a specific disease, e.g., cancer or tissue type. Last but not least, Bayesian network models can deal well with variability in input data, including conflicting data such as occasional target genes that are not expressed in a specific sample despite the transcription factor being active, or target genes that are expressed despite an inactive transcription factor. Consequently, when expression levels of individual pathway target genes vary between samples, for example, between different cell types, the Bayesian reasoning principle still allows robust interpretation of these mRNA levels. This likely also explains why the pathway models can deliver reliable pathway activity measurements across patient samples despite the variation that is inherent to such samples. Even when minimal/maximal pathway activity scores vary across cell types, as illustrated by HH and TGFβ pathway model validation results, the models remained capable of discriminating between low and high pathway activity without adapting model parameters.One reason for applicability of pathway models without model adaptation on multiple cell types and irrespective of disease type is that pathway target genes were chosen preferentially as We believe that the pathway models can also be used on cell/tissue types for which no validation results were presented on the premise that a few reference samples from the respective cell type are available with ‘ground truth’ pathway activity. Even without this, comparison between a pathway activity score in normal and disease tissue of a single patient may provide information with respect to potential pathogenic pathway activity.www.kegg.jp) and WikiPathways (www.wikipathways.org), which use the denomination ‘pathways’ not only for signal transduction pathways but also for various other intracellular mechanisms61. These methods are however not intended for measuring pathway activity in a single test sample, which is a crucial requirement for diagnostic use and forms the core of the approach described here. Their use lies in discovering which ‘pathways’ differ between two (or more) groups of samples that are compared in a data-driven manner. Furthermore, they do not define a rational biological measure of activation based on signalling biology like we present here. Instead, they are based on algebraic notions such as overrepresentation of a set of differentially expressed genes in a pathway list or rankings of a pathway’s gene set in an ordered list of differentially expressed genes between the groups to compare, while differences between mRNA, protein and activated protein levels are not taken into account. As far as validation is concerned, these other methods usually report a p-value to indicate how likely it would have been to obtain the reported results on random(ized) data, but as far as we know, they have not been biologically validated on sample sets with ‘ground truth’ pathway activity data. Such a true biological validation is important to ensure correct assessment of functional activity of signalling pathways in an individual patient sample prior to use for diagnostic applications. For illustration purpose, we have compared our pathway analysis approach with GSEA on a sufficiently large dataset with solid ‘ground truth’ information, consisting of 32 colon adenoma samples and 32 matched normal colon samples (GSE8671) . In colon adenoma the Wnt pathway is the dominant active signalling pathway63. Our analysis of this dataset confirmed the Wnt pathway as the most active pathway in the adenoma tissue samples, with the PI3K pathway as a second active pathway8. In comparison, GSEA analysis listed the first Wnt-related pathway at rank 87 of listed ‘pathways’ upregulated in the adenoma versus control tissue group, with a false-discovery rate corrected p-value of 0.038, while other Wnt-related pathways were not significantly upregulated. In contrast, many other pathways were listed in the top 10 up- or downregulated pathways without any clear biological relation to colon adenomas versus normal colon.A number of other RNA-based pathway analysis tools are available, such as Gene Set Enrichment Analysis (GSEA) and DAVID, using pathway information from databases such as KEGG , intraconazole, and vismodegib65.To illustrate potential diagnostic value of a pathway model-based test for a clinical or preclinical research question, example clinical datasets were analysed. For the Hedgehog pathway, we analysed a dataset from a clinical study containing a large number of patient samples from Ewing sarcomain vitro cell culture model was generated for amyotrophic lateral sclerosis (ALS). ALS is a neurodegenerative disease for which no treatment exists, with largely unknown pathophysiology, in which demyelination of neurons is thought to play a pathogenic role66. In this study, iPS cell lines were generated from patients with ALS and healthy individuals and were differentiated to oligodendrocytes as an experimental model system for ALS67. TGFβ pathway activity in oligodendrocytes is important for myelination of neurons58. Pathway analysis revealed a remarkable loss of TGFβ pathway activity in oligodendrocytes from ALS patients, suggesting a pathogenic role for loss of TGFβ pathway activity in this disease. This finding is in full agreement with reports describing loss-of-function mutations in the ZNF512B gene that result in loss of TGFβ pathway activity and are associated with susceptibility to, and more aggressive, ALS69. Recently, an elegant mouse model revealed that loss of TGFβ pathway activity in glia cells causes demyelinating diseases70. In an ALS mouse model, treatment with a TGFβ-activating drug had a therapeutic effect71. Thus, although only a few samples were available for analysis, the results support existing evidence on a role for reduced TGFβ activity in ALS and are expected to provide a means to measure TGFβ pathway activity in brain tissue samples of ALS patients. This example illustrates how signalling pathway models can be used as informative readout of preclinical disease models, for example, to identify new drug targets and screen for compounds to restore the pathway defect, even if only a few samples are available.For the TGFβ pathway model, we analysed a small study in which an elaborate 72. As an illustrative clinical example of NFκB pathway activity, analysis of a series of ependymomas showed high incidence of NFκB activation in Group A subtype, which not only provides further proof for use of the NFκB model in brain tissue samples but also supports a pathogenic role for this pathway in this subtype and a potential new avenue for personalized targeted treatment.Inflammatory processes play a role in many diseases and are associated with NFκB pathway activation. Group A subtype of posterior fossa ependymoma, a rare paediatric brain tumour, has been characterized by an inflammatory expression profile and has a higher mortality rate than those of the Group B subtype74. In contrast to primary PCa, AR pathway activity was highly variable in CR disease, probably caused by variations in local production of androgens or the presence of activating mutations in the androgen receptor76. The PI3K pathway seemed to be activated only in a subpopulation of primary PCa while frequently highly active in advanced PCa. The latter is in agreement with its reported role in metastatic and castrate-resistant prostate cancer78. In primary PCa, it is conceivable that a PI3K pathway activating mutation, or loss of PTEN, drives only a subgroup of cancers, possibly conferring a bad prognosis. Indeed, the presence of a putative protein signature for PI3K pathway activity, consisting of PTEN/pAKT/pS6/stathmin immunohistochemistry staining, was shown to correlate with bad outcome of PCa79. Developmental pathways, such as the TGFβ and Wnt pathways, can play tumour-suppressive and tumour-promoting roles in cancer80. TGFβ pathway activity was frequently reduced or lost, especially in advanced PCa, indicating loss of its tumour suppressive activity, which is a well-known phenomenon in prostate cancer83. One possible mechanism behind this observation may be a direct repressive effect of AR on the SMAD transcription factor of the TGFβ pathway84. The role of Wnt pathway activity in prostate cancer is complex86. In primary PCa, this pathway was frequently activated, and separate analysis of TMPRSS2:ERG fusion gene-negative and -positive cancers revealed that the presence of this fusion protein was the most likely cause of Wnt pathway activity. Indeed, TMPRSS2:ERG fusion is frequently present in prostate cancer and known to activate the Wnt pathway through overproduction of the ERG protein, fully in line with our findings87. This result provides additional evidence that our model can correctly measure Wnt pathway activity in prostate tissue and confirms the presumed tumour-promoting role of the Wnt pathway86. However, in advanced disease, Wnt pathway activity is generally lost, suggesting a tumour-suppressive rather than tumour-promoting role under this condition. In contrast to primary PCa, in prostate hyperplasia a consistent loss of Wnt and TGFβ pathway activity associated with PI3K pathway activity was found, suggesting a distinct pathophysiology with a putative tumour-suppressor role for both Wnt and TGFβ pathways. The frequent PI3K pathway activity, characterized by reduced FOXO activity, resembles that found in benign colon adenomas6. Furthermore, in prostate tissue, FOXO activity has been described to confer a tumour-suppressor role to a co-active Wnt pathway, quite similar to what has been reported for combined FOXO-TGFβ pathway activity in breast tissue89. This suggests that PI3K pathway activity may interfere with tumour-suppressive functions of Wnt and TGFβ pathways by eliminating FOXO activity. Clearly, delineating the role of the Wnt pathway in various forms of prostate disease requires more investigation, and the described pathway model may be of help in standardizing pathway measurements.Signalling pathways often interact, simultaneously or sequentially, to coordinate cellular processes, and analysis of multiple pathways enables a more comprehensive characterization of disease pathophysiology. We selected PCa, with AR pathway activity as its hallmark, as a clinical example to illustrate the value of measuring more than one signalling pathway, including analysis of Wnt and PI3K pathway activity. In addition to expected increased AR pathway activity in primary PCa, AR pathway activity was also increased in cancer-adjacent and hyperplastic prostate tissue, which is likely caused by elevated local androgen levels91. The inverse relation between AR and NFκB pathway activity in cancer-adjacent tissue, hyperplasia, and primary PCa is of potential clinical relevance since inflammation is known to be a bad prognostic marker for both benign hyperplasia and prostate cancer and may be associated with hormonal resistance94. The underlying mechanism is likely the negative interaction between NFκB and AR transcription factors95. Whether this can be extended to advanced cancer could not be investigated due to small sample numbers; however, the one CR tumour with inactive AR pathway and active NFκB pathway is illustrative of a similar relationship where NFκB activity may play a role in castrate resistance95. Taken together, pathway activity results in prostate hyperplasia and cancer were in agreement with expectations based on the literature, providing further validation of the used pathway analysis models. We realize this is only an explorative study; however, upon confirmation of our findings, signalling pathway measurements in patient samples may be of value to predict response to targeted drugs including PI3K inhibitors, for example, in patients with advanced and CR prostate cancer, and provide information on potential (targetable) resistance pathways. Another clinical utility could be quantitative monitoring of AR pathway activity to support bipolar androgen therapy (BAT), an approach in which androgen and anti-androgen therapy are sequentially used96.Measuring multiple pathways in individual samples revealed potential mechanisms of drug resistance. Although measured loss of AR activity in CR disease provides an obvious reason for anti-androgen therapy resistance, NFκB and PI3K pathway activities may also cause hormonal resistance98. To enable application to clinical routine formalin-fixed paraffin-embedded (FFPE) tissue samples, pathway models are being translated from Affymetrix microarray to qPCR and RNA sequencing as input. Simultaneously, multiple clinical studies for clinical validation are in progress.In summary, following biological validation, Bayesian network models for measurement of cellular signal transduction pathway activity were used to analyse sample data of a variety of clinical disorders. We illustrated how this new approach can be used to quantitatively characterize disease pathophysiology with a focus on prostate cancer. Importantly, the described pathway analysis is envisioned to provide clinically actionable results for individual patients, for example, prediction of response and resistance to targeted drugs, providing targets to reverse drug resistance, and monitoring of therapy response. Following extended clinical validation, a major future application will be in diagnostics, aiming at optimizing (targeted) treatment for individual patients. Many additional applications are possible, for example, in cell and tissue culture, regenerative medicine, toxicology, and drug development Supplemental Information


More recently, it is explored in combination with other herbs for skeletal diseases therapy; bone-targeting compounds with pharmacological activities have been isolated from various sources of traditional Chinese medicine (TCM), including Danshen. In this case, some evidence supports that Danshen may treat myelofibrosis (MF) by exerting its antitumor effect. To study the specific mechanism of Danshen in the treatment of MF, we used bioinformatics databases to determine its active ingredients. Then, identification of target proteins related to MF was made using a network pharmacology analysis platform. In our results, 20 key active compounds and 457 key targets of Danshen were identified. In-depth network analysis of the top diseases, functions, and pathways suggested that a common underlying mechanism linked Danshen involvement with MF. Finally, 5 potential targets were confirmed by the analysis; these 5 targets, as well as 20 previously identified compounds, were subjected to molecular docking experiments. The results indicated that cryptotanshinone of Danshen may affect MF by acting on the key genes in the JAK-STAT signalling pathway and the TGF- Myelofibrosis (MF) is a clonal stem cell disorder classified as a chronic myeloproliferative neoplasm (MPN). It is characterized by extensive bone marrow fibrosis, overproduction of inflammatory cytokines, progressive splenomegaly, and anaemia . MF impaβ and tumor necrosis factor-α. Furthermore, Danshen inhibited hyperproliferative fibroblasts, according to a report [Danshen  is a common and important Chinese medicine with well-defined phytochemicals. As a type of complementary and alternative medicine, it has been used widely for a long time and shown significant clinical outcomes in terms of anticoagulant, antioxidant, and anti-inflammatory activities to benefits for vascular protection and cancer treatment –10. The a report . This suAs a type of complementary and alternative medicine, herbal medicine has the advantage of low side-effects, low resistance, and long actuation duration. However, traditional Chinese medical science is composed of several components, targets, and pathways that realize its distinctive therapeutic efficacy by modulating human biological networks in body systems . It is rTraditional Chinese medicine believes in holism; that is, Chinese herbs are used to treat diseases through several components and several targets rather than single components and single targets. The holistic perspective reflected in network pharmacology is perfectly in harmony with the concept of holism in TCM. In addition, other bioinformatics resources including molecular docking software have been exploited, providing good opportunities for rapid system filtering , 17. It Therefore, in this study, a pharmacology network and molecular docking combination approach was introduced to explore the impact of Danshen on MF, as well as its molecular mechanisms. It is expected to provide reliable essential information and the feasibility of Danshen in treatment, probably as a potential therapy for MF. The work involved the following four steps: (1) the ingredients of Danshen along with their corresponding targets and myelofibrosis associated targets were identified by various databases; (2) the interaction relationships in the compound, the compound target, the disease target, and the function and pathways were constructed by networks; (3) the key compound, the key targets, the top functions, and the top canonical pathways were revealed by network analysis; (4) the most potentially valuable targets were obtained and validated through scoring between the top targets and corresponding compounds; and (5) the feasibility of the targets that may be the potential target in treatment of myelofibrosis was compiled by document retrieval. The workflow is displayed in http://lsp.nwu.edu.cn/), which is a unique system pharmacology platform devised for Chinese herbal medicines. To discover the active components of Danshen, we chose compounds meeting the requirements of both OB ≥ 50% and DL ≥ 0.18. Then, 20 compounds in Danshen were obtained. OB represents the percentage of an orally administered dosage of unchanged drug that reaches the systemic circulation, indicating the convergence of the ADME  process. DL has been widely used to filter out compounds with undesirable properties.We collected the chemical compound data of Danshen from the Traditional Chinese Medicine Systems Pharmacology Database  and the TCMSP database. We input all the active compounds into PubChem (https://pubchem.ncbi.nlm.nih.gov/), obtained the canonical SMILES strings of active compounds in Danshen, and imported these canonical SMILES into Swiss target to obtain targets of each compound. Finally, we used the UniProt KB search function (http://www.uniprot.org/) in the UniProt database, entered the protein name, and defined the species as “human”, in order to obtain the gene name for each protein. Eventually, 457 compound targets for Danshen were retrieved without repeating targets of the same compound.To obtain putative targets of potential active compounds in Danshen, we used the SwissTargetPrediction (https://www.ncbi.nlm.nih.gov/omim), the therapeutic targets database  and the human gene database . Only ‘Homo sapiens' proteins related to myelofibrosis were selected.Information regarding MF-associated target genes was gathered from the Online Mendelian Inheritance in Man database (OMIM: https://string-db.org). Only protein interactions with the confidence score 0.400 were considered. We inputted these targets and the data into the network visualization software Cytoscape  to construct the 5 following networks: (1) compound target network; (2) compound-compound target network; (3) disease target network; (4) compound target-disease target network; and (5) disease target-pathway network. Among these networks, topology analysis was achieved in CentiScaPe.To evaluate potential protein interactions among the targets, all gene symbols of the protein targets including Danshen and MF were submitted to the String Database , and KEGG pathway information was obtained. Only terms and enrichment pathway with P-value <0.05 were considered and thought to construct the disease target-pathway network.ClueGo was applied for Gene Ontology (GO) enrichment analysis, and the biological processes with P-value <0.05 were considered significant biological processes. Potential targets were uploaded to the Database for Annotation, Visualization and Integrated Discovery . For improving the accuracy of results, the conduct of docking used SYBYL-X software and systemsDock (http://systemsdock.unit.oist.jp/iddp/home/index/) [All small molecule structures of the 20 compounds in our previous work were obtained from the PubChem Project and were saved to mol2 format files. All protein-ligand complexes with crystal structures of the above-mentioned 5 targeted proteins were directly obtained from the RCSB Protein Data Bank (/index/) . After t/index/) , 21.A total of 202 compounds from Danshen were selected from the TCMSP database, and active components were evaluated using DL and OB approaches . Twenty active compounds  were ultβ signalling pathway, the chemokine signalling pathway, acute myeloid leukaemia, the adipocytokine signalling pathway, leukocyte transendothelial migration, bladder cancer, intestinal immune network for IgA production, epithelial cell signalling in Helicobacter pylori infection, pancreatic cancer, and chronic myeloid leukaemia. The path lists are shown in For displaying intuitive interactions in Danshen, compound-compound target network as shown in As previously described, 5 genes  were selected as core potential targets. Then we placed them into SYBYL-X and systemsDock for analysis of the docking potential with 20 compounds of Danshen . The docMF is a BCR-ABL-negative clonal disorder referred to as a myeloproliferative neoplasm (MPN) . Its cliIn this study, we used a network pharmacology approach to analyze the potential constituent ingredients and targets of Danshen. The compound-compound target network was composed of 189 nodes (20 active compound nodes and 169 compound target nodes) and 456 edges. In the network, we found that some targets were attacked by several compounds including PTGS2, STAT3, HSP90AB1, and ACHE. This fact implied that the active chemicals of Danshen might affect these targets synergistically, virtually showing the multicomponent, multitarget, and multidisease properties of herbal medicine. Its potential effects can be highlighted by this network. The MF targets' PPI network included 103 nodes and 731 edges. Many nodes had higher degrees in this network, including TP53, TNF, VEGFA, IL8, and JAK2. The number of edges of each node was quite large . Thus, these genes could be pivotal genes in the development of MF.A compound target-MF target network was constructed with 234 nodes and 2818 edges. Danshen probably exerts its therapeutic effect on myelofibrosis by binding and regulating particular protein targets. We speculated that the top 20 nodes might be vital targets in the treatment of MF. We considered TP53, TNF, VEGFA, STAT3, and JAK2. TP53 is one of the most significant tumor inhibitor genes that plays a role in inhibiting cancer through two pathways, transcriptional-dependent activity and non-transcriptional-dependent activity in the cytoplasm of the nucleus . When DNβ signalling pathway regulated fibrosis in myeloproliferative diseases [β pathway played a vital role in stem cell quiescence and in progenitor cell differentiation [β signalling directs normal hematopoietic stem cells (HSCs) into quiescence. It is not activated in MF, suggesting that TGF-β may directly and competitively suppress the growth of normal HSCs to boost proliferation of the malignant clone [β signalling pathway.Pathway analyses were conducted using DAVID. The 14 significant pathways (P ≤ 0.05) identified in this study included several related to cancer. In fact, the “JAK-STAT signalling pathway” was one of the top three pathways. The JAK-STAT pathway is a vital effector of cytokine-dependent regulation of gene expression . In partdiseases . Moreoventiation . TGF-β snt clone . In summβ signalling pathway.Molecular docking is the most widely used method for calculating protein-ligand interactions, and we used the SYBL-X and systemsDock to investigate probable binding modes. The results showed that cryptotanshinone had the best activity: it docked well with 5 key targets of MF. The previous research demonstrated that the structure of aromatic ring A of tanshinones may enhance the cytotoxicity . And theβ signalling pathway. Although further experiments are needed to support our findings, the present study revealed the mechanisms of Danshen in the treatment of myelofibrosis. It provides an important basis for further study of the follow-up mechanisms and the optimization of experimental designs, so that experimental research may be more reasonable.In summary, the current study that combines a network pharmacology method and molecular docking illuminates the molecular and pharmacological mechanisms of Danshen against MF from a systematic perspective that cryptotanshinone might play a key role in MF therapy by acting on the key genes of the JAK-STAT signalling pathway and the TGF-