Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways? ”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies.
Understanding the nature of cause and effect is fundamental to all fields of scientific investigation, but the concept of causality can present special difficulties in biology [1]. Experiments that utilize controlled interventions represent the most widely used approach to establishing causality. However, in his seminal work on experimental design, RA Fisher proposed that causation can be inferred from multi-factorial experiments performed with randomization [2]. An extension of this principle provides the foundation for computational approaches to network reconstruction in experimental genetic crosses, such as the recombinant inbred strain panel used in this study. Natural allelic variation is randomized during meiosis to generate a multi-factorial perturbation affecting multiple phenotypic outcomes. This meiotic randomization allows for the inference of quantitative trait loci (QTL) that are causal to phenotype [3]. Recent advances in high-throughput phenotyping technologies have made large-scale measurements of molecular traits possible. Expression QTL (eQTL), metabolic QTL (mQTL) and protein QTL (pQTL) can be used to link thousands of molecular phenotypes to genetic loci, as well as to clinical phenotypes [4]. A typical xQTL study will involve cross sectional sampling of a genetically variable population at a single time point. It is not immediately obvious that such data could provide insight into causal biological mechanisms, which derive from non-linear dynamic processes of gene expression and metabolism. However, a rich body of literature supports the idea that correlation structure in static data can provide insights into causal relationships among the measured variables [5], [6]. The interpretation of a directed edge between nodes and in a graphical model is that intervention on will alter, but intervention on will not alter. In a metabolic reaction, intervention on the substrate concentration will alter the product concentration. Reaction stoichiometry is often well understood [7]. Substrate molecules are converted by known enzymes into products, which in turn act as substrates for subsequent reactions. Reactions are organized into pathways which may converge, branch or intersect to form elaborate networks. More complex pathways involving feedback through allosteric interactions between enzymes and metabolites may also be present. It is not clear to what extent graphical models inferred from mQTL data capture these types of interactions. Several algorithms have been proposed for the inference of causal relationships among phenotypes using genetic data [8]–[14]. These methods employ linear statistical models to infer the relationships between QTL and phenotypes, as well as relationships among phenotypes [15]. Causal edge detection is sensitive to subtle correlation patterns in the data. Inferences have been shown to be subject to a large proportion of false positive edges and can be skewed by environmental and experimental design factors that are not accounted for in the model [16], [17]. Agreement between the graphical model and the true underlying biology is a central goal of systems biology. The topology of networks inferred from xQTL data is often interpreted as a reflection of the underlying biological process - which may be metabolic or regulatory in nature, nonlinear, and involve the dynamic interaction of molecules within cells and tissues. However, the extent to which graphical models derived from static data capture these processes is not well understood, which makes the interpretation of edges challenging. Deterministic models of cellular metabolism can be defined by ordinary differential equations (ODEs) derived from simple laws of mass-balance [18]–[21]. The reaction rates are modeled as non-linear processes, e. g. Michaelis-Menten kinetics and Hill functions, which depend on kinetic rate parameters [22]. Models of this type are powerful because of their ability to make in silico predictions of the response of a system to perturbations. We present a simulation study in which we generate synthetic mQTL data from dynamical models of pathway motifs with two sources of perturbation. We vary the rate parameters in a manner that mimics a genetic cross and we drive the simulations models with an input function that includes stochastic noise. Glucosinolates are secondary metabolites that influence the interaction of plant and pest and have a wide range of important functions in human health [23]–[25]. The economic importance of glucosinolates has led to significant progress in understanding the biochemical pathways and genetics [26], [27]. Glucosinolate biosynthesis occurs in three well understood stages in which amino acids undergo (Figure 1): (1) chain-elongation, (2) formation of glucone moeity, and (3) side-chain modification. In this work, we examine mQTL data from a class of aliphatic glucosinolates in a highly replicated Arabidopsis BaySha recombinant inbred population [28]. The metabolites under investigation participate in side-chain reactions. Genetic analysis reveals shared QTL and wide-spread epistasis in the pathway [29]. In order to address these questions, we have inferred causal networks from mQTL data using simulated metabolic models of common pathway motifs and real data from a well characterized metabolic network. We demonstrate that correlation structure can be shaped by a variety of factors, including, genetic variation, pathway architecture, position in the pathway and feedback. Our results highlight the necessity of biological variation outside of the variation contributed by genetic factors for reliable network inference. Substrate-product relationships are not always reflected in the correlation structure of the system and recovery of the biochemical ordering of species should not be expected. Substrate inhibition, which is pervasive in metabolic pathways, can diminish or mask these relationships and lead to missing edges in network inference. An accurate genetic model is also critical to the inference process, especially when epistasis is involved. Our findings should temper expectations and provide new insights into the interpretation of causal genotype-phenotype networks.
In order to infer a causal relationship between a substrate and its product, non-genetic variation in substrate concentration has to propagate to the product. This is a necessary, but not sufficient condition for causal inference. To see this, suppose that one metabolite is causal to another, and that variation includes a genetic driver, . The linear equations for the causal graphical model can be written as: or equivalently: Suppose there is no propagation of the non-genetic variation, , then: and the traits are conditionally independent given genotype, . It is clear from the equations that, is the term that carries the residual correlation between and. Therefore, variation in metabolites beyond that induced by genotype must be propagated through the biological pathway to create the correlation structure necessary for causal inference. Consider the BaySha data example: , where denotes the QTL on Chrs 4,5 and their interaction. There is a strong correlation between the residuals and () (Figure 7A), which is driven by the propagation of the non-genetic variation, . To see this dependency, we imputed data with no propagation of variation: and are approximately independent with negligible correlation (). A causal edge between and would not be detected with network inference (Figure 7B).
Graphical models provide a framework for estimating causal relationships between genotypes and phenotypes. Models of this type can be used to perform in silico experiments that predict responses to genetic and environmental perturbations. Ideally, these models should inform us about of the response to targeted interventions, such as a drug that alters the properties of a metabolic enzyme. There are numerous reasons for caution in such inferences. The inference models are linear, but the true relationships among relevant variables is likely to be driven by a non-linear dynamical process. It is not clear that these relationships should be captured by linear correlation. Correct interpretation is important, particularly if the graphical models are used to guide intervention strategies. Several algorithms have been proposed for building graphical models in the context of genetic crosses [8]–[14]. These methods all derive models from the correlation and partial correlation structure in the data. We found that the available model building methods produced highly concordant results for models of the size and architectures considered here. Therefore we chose one specific MCMC algorithm to investigate the relationship between an inferred graphical model and the biochemical pathway that gave rise to the data. An advantage of the MCMC algorithm is the ability to sample multiple networks from a posterior distribution. This avoids reliance on a single network, which is problematic when two or more distinct networks can explain the data equally well. Sampling also provides a measure of uncertainty in the inferred network topology. Summarizing an ensemble of networks is challenging. We chose a consensus representation consisting of edges that occur most frequently in the sampled networks. If there is not enough information in the data to reliably establish the existence of an edge, this is reflected in low edge weights of the consensus network. Also, if we observe an edge that is present in most of the sampled networks but with opposing directions in different networks, we can conclude that the edge is present but there is insufficient data to resolve it direction (e. g. , Figure 6C). We analyzed metabolite data and from real and simulated pathways with known network stoichiometry. The Michaelis-Menton kinetics used in our simulated metabolic reactions are special cases of Hill functions and represent a rough approximation to actual enzyme reactions. Similar models have been used to describe gene regulatory networks and other biological phenomena, e. g. [19], [20], [30]. Constraint based modeling provide an alternative approach to delineate metabolic networks from steady-state data [31]. In the steady-state, the system of ODEs reduces to a linear system, but nonlinear relationships may arise between fluxes and pathways [32]. Investigation of the properties of constraint based and other non-correlation based methods for inference in dynamical systems remains an area of active research [33]–[36]. Correlation in metabolite data can be driven by a variety of factors that do not directly relate to the network stoichiometry. In order to capture the biochemical ordering of the pathway, noise has to propagate through the biochemical network. Many biological pathways are buffered by feedback or other stabilizing features that reduce noise propagation and mask the correlations that would imply causal connections. Failure to consistently observe substrate-product correlation may explain some of the differences observed between the plant data and simulations for matching pathway architectures. Our objective is not to confirm that our simulations accurately reflect the plant data or to make generalizations about certain pathway architectures. Rather, we seek to leverage real data from a well-studied biological system and simulated data from pathway motifs to explore a variety of architectures and conditions. A shortcoming of in silico models is their inability to fully capture the richly interconnected nature of biological systems. We considered simple motifs in isolation and modeled them with Michaelis-Menton kinetics. Correlation structure depends on the network architecture, the size and nature of the genetic perturbation, stochastic fluctuation, and enzyme kinetics. The advantage of this simulation is that no biological variation arises from factors outside of what is modeled. Whereas, metabolic systems in vivo contain mechanisms that make them robust, e. g. , buffering, cycling and feedback, but may be impossible to pin-point with real data. In the plant data, many of the substrate-product relationships remain intact after conditioning on QTL (Figure 5). This suggests that a real metabolic pathway may give rise to meaningful biological correlations that reflect the topology of the pathway despite the non-linear nature of the underlying processes. This is promising from the point of view of network reconstruction, but is not without limitation. The architecture of the homo-methionine side-chain was only partially captured, with an additional edge between Allyl and OHP3 that reflects the shunting of flux through the lower branch of the pathway (Figure 6A). The biochemical ordering of the dihomo-methionine side-chain was captured exactly (Figure 6B). We are only to able to detect an undirected connection between MT8 and MSO8 in the hexahomo-methionine side-chain (Figure 6C). Lack of a private QTL or a gradient in the effect size gives rise to likelihood equivalent models from which the direction of causality could not be distinguished. A similar situation was observed when a global model was estimated from the entire panel of metabolites and QTL (Figure 6D). The shared nature of the QTL hindered network reconstruction of the entire pathway. Most of the side-chain members were linked, but the direction of causality was not consistent with the pathway or with the networks constructed for each of the side-chains independently. Allyl and But-3-enyl are unlinked in the metabolic pathway, but are both products in reactions facilitated by AOP2. The causal link between them is likely driven by this co-regulation. Conditioning on QTL genotypes strengthens the correlation among metabolites in most of the simulated pathway motifs (Figure 3). An exception occurs in the branching pathway with substrate inhibition which shows an almost complete loss of correlation between the branchpoint and upper branch metabolites and after conditioning (Figure 3F). In the linear pathway, when reaction rates are not operating at saturation and there are no branches to redirect the flux, any variation in the flux must propagate through each of the metabolites [37]. This results in a uniform correlation structure among the metabolites, which in turn yields weak causal linkages and order ambiguity among metabolite nodes in the graphical model. However, graphical models strongly and consistently associate metabolites to the QTL node controlling their downstream flux in linear pathways (Figure 3A, Text S1). The branching pathway is a linear pathway with a sink that represents demand on a metabolite from another reaction or pathway (Figure 2D). The stoichiometry of the branching pathway was captured exactly with the graphical model (Figure 3D). This suggests that the diversion of flux through side reactions is helpful in defining pathway order. For merging pathways, the correlation structure is dependent on the nature of the reaction at the merge point. When two pathways merge through a bi-substrate reaction (Figure 2B) there is strong association between the substrates that combine, but these are only weakly coupled to the downstream component of the pathway. On the other hand, when two pathways merge through independent reactions, the upstream metabolites and are only weakly correlated with each other, but the there is strong uniform correlation across the two linear components of the pathway (Figure 3C). Ordering metabolites in the independent merging pathway suffers from the same weaknesses as in the linear pathway. These results emphasize the influence of network stoichiometry on the correlation structure of the pathway. Biosynthetic pathways, which often branch to produce two or more end products, are especially prone to inhibition [38]. We examined biosynthetic pathways that were inhibited in two ways: (1) loss of function in one pathway branch and (2) substrate inhibition. In the plant data, loss of function in AOP2 gave rise to an epistatic interaction between loci on Chr 4 and Chr 5 [28], [29]. Ignoring epistatic interactions and model fitting with only main-effect terms led to dense graphs that were difficult to interpret (data not shown). Substrate inhibition is estimated to occur in approximately 20% of enzymes [39]. This process can be viewed as a regulatory mechanism in which accumulation of a substrate represses the reaction velocity. In our simulation, the accumulation of metabolite inhibits the flux through a branched pathway (Figure 2E). The inhibition is reflected in the correlation structure, is negatively correlated with the other metabolites (Figure 3E). QTL disappears, suggesting that substrate inhibition can dominate the effects of genetic perturbations (Figure 3D–E). The correlation structure of this pathway was most sensitive to conditioning on QTL. When substrate inhibition is present, a loss of correlation and genetic control can occur, which makes two connected pathways look independent. These results highlight the importance of an accurate genetic model for network inference, especially in the presence of inhibition and epistasis. Estimation of kinetic parameters in dynamic models requires time course data, which is often sparse, and the computations involved can be challenging [40]. The choice of experimental perturbations and design have been shown to have major influence on parameter estimation, and subsequently the accuracy of the computational model [41]. Complex models of biological systems exhibit parameter sensitivities that span several orders of magnitude [42]. Concentration profiles and model outputs are sensitive to small changes in kinetic rate parameters [43], [44]. The impact of parameter values on concentrations carries over into the correlation structure, and consequently, the downstream network inference. In our simulations, the perturbation is analogous to genetically determined non-competitive inhibition, where is genetically perturbed to be either high or low, thereby changing the flux capacity [45]. This strategy ensures that there is a significant difference between genotype groups and enables us to identify QTL. Random stochastic fluctuations were used as input and propagated through the pathway. Stochastic inputs allow us to examine the out of equilibrium dynamics of the system. The fluctuations themselves represent some of the randomness the pathway encounters from being part of a cellular system that is continuously changing [46], [47]. The models represent continuous excitation of the cell with the assumption that the intra-cellular dynamics can be faithfully modeled with ODEs. Examining system behavior over a spectrum of parameter values and stochastic inputs would offer additional insight into the sensitivity of the correlation structure. Using both real data and simulated data, we tested the ability of graphical models to capture causal relationships between variables from from a variety of metabolic pathway topologies and conditions. We found that the use of linear statistical models to approximate relationships in dynamic non-linear systems from static data has some merit, but the results should be interpreted carefully. It is not realistic to expect to fully recover ordered pathway relationships with causal inference methods. Our results emphasize the necessity of biological variation beyond the genetic factors in the model for reliable network inference. We demonstrated that residual correlation induced between substrate and product in a metabolic reaction can be dominated by variety of factors, including, flux shunting, co-regulation, position in the pathway, genetic factors and inhibition. We found that inhibition can lead to missing edges in graphical models, washing out the genetic signal and making connected pathways look independent. An accurate genetic model is important, especially when epistasis is present. Taken together, these results temper our expectations and explain some of the success and failures of causal graphical models for genotype-phenotype inference.
Metabolic QTL data from a population of 403 Arabidopsis BaySha recombinant inbred lines (RIL) were examined in this study [28]. The data include measurements of aliphatic metabolites and genotypes from markers across the genome. A substantial number of samples have metabolite levels that are below the level of detection (Table S1). We applied a transformation to the scale. QTL mapping was performed for each metabolite with R/qtl [48]. Genome scans for single-QTL and two-QTL models were performed with Haley-Knot regression. The logarithm of odds (LOD) threshold for significance (P) was calculated from permutations [49]. Pathway motifs were used to define systems of ODEs that depend on flux rates, , modeled with Michaelis-Menten kinetics (Figure 2) [22]. If a substrate produces, then the rate of reaction is described by: where is the maximum rate of velocity and is substrate concentration at which half of is attained. When two substrates and combine to produce, , we write: When the accumulation of a metabolite feeds-back to inhibit a flux: where is an affinity constant. This flux form represents substrate inhibition which occurs at high substrate concentrations. As, the reaction flux is uninhibited and approaches standard Michalis-Menton form. The dynamics of a substrate is described with the mass balance equations: where and denote the production and utilization of respectively, the stoichiometric coefficients are given as and and is the genotype. Genetic perturbations are made through the coefficients as either high or low, depending on the genotype or. For simplicity, we assume that each participates in a single reaction and that they are unlinked. In our simulations, we set, and. We also modeled a loss of function mutation by setting for certain genotypes (Figure S3) [50]. There are genotype combinations for each pathway of reactions. Each combination can be viewed as a sample from a randomized genetic population. For every unique genotype combination, we use an input flux that is perturbed by a random process, , modeled as a Brownian path over the interval [51]. The system is propagated, . The perturbations, , and the concentration levels at the end of the simulation are collected as data for correlation analysis and graphical model fitting. The output of each simulation can be viewed a sample in mQTL data. A schematic depicting the entire simulation process is shown in Figure S1. The Pearson correlation is calculated for the variables in each pathway architecture. Residuals are estimated after each metabolite is conditioned on the QTL in the model. The residuals are used to calculate the conditional correlation of the metabolites given the genetic factors in the model. Directed graphical models are estimated using Bayesian Networks with a MCMC algorithm [14]. In pathways with epistasis, we include single degree of freedom variables that represent a composite genotype as variables for inference [52]. The sparsity parameter was set in the range. Each chain was run from two starting points, convergence was verified using correlation of edge weights (posterior probabilities) and the acceptance rate of each chain was in the range of 23%–45%. The results are based on the marginal summary over the ten graphs with the highest posterior probability. Alternative representations over the top and graphs and the four most probable graphs for each pathway are presented in Text S1.
High-throughput profiling data are pervasive in modern genetic studies. The large-scale nature of the data can make interpretation challenging. Methods that estimate networks or graphs have become popular tools for proposing causal relationships among traits. However, it is not obvious that these methods are able to capture causal biological mechanisms. Here we address the power and limitations of causal inference methods in biological systems. We examine metabolic data from simulation and from a well-characterized metabolic pathway in plants. We show that variation has to propagate through the pathway for reliable network inference. While it is possible for causal inference methods to recover the ordering of the biological pathway, it should not be expected. Causal relationships create subtle patterns in correlation, which may be dominated by other biological factors that do not reflect the ordering of the underlying pathway. Our results shape expectations about these methods and explain some of the successes and failures of causal graphical models for network inference.
biochemistry
plant biochemistry
mathematics
genetics
applied mathematics
biology
computational biology
genetics and genomics
What Can Causal Networks Tell Us about Metabolic Pathways?