Georg Basler

Mass-balanced randomization of metabolic networks

Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. Results: We first review the shortcomings of the existing generic sampling scheme—switch randomization—and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties. Contact: basler@mpimp-golm.mpg.de; nikoloski@mpimp-golm.mpg.de Supplementary Information: Supplementary data are available at Bioinformatics online.

Evolutionary significance of metabolic network properties.

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein–protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.

Control of fluxes in metabolic networks

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.

Differential metabolic and coexpression networks of plant metabolism

Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.

Computational Prediction of Essential Metabolic Genes Using Constraint-Based Approaches

In this chapter, we describe the application of constraint-based modeling to predict the impact of gene deletions on a metabolic phenotype. The metabolic reactions taking place inside cells form large networks, which have been reconstructed at a genome-scale for several organisms at increasing levels of detail. By integrating mathematical modeling techniques with biochemical principles, constraint-based approaches enable predictions of metabolite fluxes and growth under specific environmental conditions or for genetically modified microorganisms. Similar to the experimental knockout of a gene, predicting the essentiality of a metabolic gene for a phenotype further allows to generate hypotheses on its biological function and design of genetic engineering strategies for biotechnological applications. Here, we summarize the principles of constraint-based approaches and provide a detailed description of the procedure to predict the essentiality of metabolic genes with respect to a specific metabolic function. We exemplify the approach by predicting the essentiality of reactions in the citric acid cycle for the production of glucose from fatty acids.

Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks

Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases. Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called ‘stoichiometric capacitance’. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic systems changes. Contact: larhlimi@mpimp-golm.mpg.de, or nikoloski@mpimp-golm.mpg.de Supplementary Information: Supplementary tables are available at Bioinformatics online.

Optimizing metabolic pathways by screening for feasible synthetic reactions

BACKGROUND Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze. RESULTS Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis. CONCLUSIONS While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.

Photorespiratory Bypasses Lead to Increased Growth in Arabidopsis thaliana: Are Predictions Consistent with Experimental Evidence?

Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO2 exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions.

A network property necessary for concentration robustness

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

Integrating food webs with metabolic networks: modeling contaminant degradation in marine ecosystems

The seas are continuously pervaded by a broad range of contaminants entering the marine environment from polluted soils, the atmosphere, sewage, water transport or river streams (Shahidul Islam and Tanaka, 2004). Organic pollutants are of particular concern, because of their tendency to accumulate in specific organisms, thus threatening both ecosystem stability and human health (Fleming et al., 2006; Johnston and Roberts, 2009). Among those, polychlorinated biphenyls (PCBs) are synthetic compounds produced by the chemical industry and found throughout global environments (Beyer and Biziuk, 2009). Due to their hydrophobicity, PCBs accumulate in the lipids of marine species, leading to bioaccumulation predominantly at higher trophic levels (Perugini et al., 2004). On the other hand, PCBs can be degraded by the sequential anaerobic and aerobic metabolic processes of microorganisms (Borja et al., 2005). Consequently, the accumulation and fate of these pollutants within ecosystems depend not only on their uptake by and transfer among marine species, but also on microbial biodegradation. The biostimulation of degrading bacteria or their insertion (bioaugmentation) at contaminated sites may represent a promising strategy for bioremediation (Tyagi et al., 2011). Biomass transfer among species of a food web can be modeled using allometric scaling rules describing body size, consumption and metabolism (Yodzis and Innes, 1992; Berlow et al., 2009). Similarly, the flow of contaminants through a food web can be estimated by mass-balance modeling of chemical transfer and adsorption (Wania and Mackay, 1999; Arnot and Gobas, 2004; Breivik et al., 2007; Nichols et al., 2009; Taffi et al., in press). Such modeling approaches can be complemented by measurements of contaminant concentrations (Kelly et al., 2007). Recently, a food web representing the major ecological groups of the Adriatic Sea and their predator-prey interactions (Coll et al., 2007) was integrated with PCB concentration data obtained from an extensive literature review (Taffi et al., in press). The resulting model allowed prediction of the flow rates of PCBs among ecological groups and reproduced the finding that PCBs accumulate mostly in species at higher trophic levels and with lower total biomass. Further, the model allowed estimation of the fate of contaminants, such as their bioaccumulation in marine species. The described efforts facilitated generic estimations of contaminant flows in ecosystems, but largely neglected the contribution of microorganisms to pollutant degradation. Specifically, PCBs can be dechlorinated and degraded by a range of bacteria (Borja et al., 2005). Thus, biodegradation may be an important factor for the persistence of a pollutant within an ecosystem and, moreover, may provide hints for bioremediation strategies based on the aforementioned bacteria. The uptake, degradation and excretion of compounds by microorganisms can be predicted from the knowledge of their metabolic capabilities using constraint-based approaches (Bordbar et al., 2014). Today, detailed metabolic reconstructions are available for a broad range of organisms (Monk et al., 2014), including several bacteria known for their bioremediation capabilities, such as Geobacter spp. (Mahadevan et al., 2011), Shewanella oneidensis (Fredrickson et al., 2008), and Pseudomonas putida (Nogales et al., 2008); and new reconstructions for more organisms are published regularly. Thus, an intriguing challenge is the combination of ecological and metabolic modeling approaches to integrate population-level simulations of ecosystems with cellular modeling of microbial metabolism, similar to the previous integration of reactive transport models with metabolic models (Fang et al., 2011). Taffi et al. (2014) have recently integrated a comprehensive food web of the Adriatic Sea with PCB concentration data and a metabolic model of P. putida. First, the authors conducted an extensive literature review to complement a food web reconstruction (Coll et al., 2007) with data of PCB concentrations among marine species (Taffi et al., in press). Next, linear inverse modeling was used to infer unknown contaminant flows and concentrations based on the mass balance principle. Finally, the ecological network was integrated with the genome-scale metabolic network of P. putida for constraint-based simulation of microbial PCB degradation. The study integrated two methodologically similar modeling approaches within a reaction-based ecological/microbial network representation, relying on the parallels between representing PCB concentrations and flows on the ecosystem level, and metabolite concentrations and reaction fluxes on the microbial cellular level. Therein, marine species groups resemble the representation of metabolites, while contaminant flows are modeled as reactions. This approach enabled the seamless integration of ecological and metabolic modeling techniques, providing the basis for multi-scale simulations of ecosystems. The modeling approach was used to predict the influence of different microbial bioremediation strategies on the fate and distribution of PCBs in marine species of the Adriatic Sea. The effect of varying oxygen levels on microbial PCB degradation revealed a tradeoff between PCB uptake and growth of P. putida. Further, the impact of different bioremediation scenarios on global and local network indices was assessed. Importantly, the generality of the proposed approach facilitates the integration of measured data, the incorporation of established techniques from ecological and metabolic modeling and the direct application of the methodology to other ecological and microbial networks. Thus, it can be used to guide the selection of appropriate bacteria and consortia (Thompson et al., 2005), or the design of genetically engineered bacteria for bioremediation (Singh et al., 2011). The approach will stimulate new developments in ecological modeling and offer insights into the multi-level interplay among ecosystems, microbial networks and biodegradation.