Areejit Samal

The regulatory network of E. coli metabolism as a Boolean dynamical system exhibits both homeostasis and flexibility of response

BackgroundElucidating the architecture and dynamics of large scale genetic regulatory networks of cells is an important goal in systems biology. We study the system level dynamical properties of the genetic network of Escherichia coli that regulates its metabolism, and show how its design leads to biologically useful cellular properties. Our study uses the database (Covert et al., Nature 2004) containing 583 genes and 96 external metabolites which describes not only the network connections but also the Boolean rule at each gene node that controls the switching on or off of the gene as a function of its inputs.ResultsWe have studied how the attractors of the Boolean dynamical system constructed from this database depend on the initial condition of the genes and on various environmental conditions corresponding to buffered minimal media. We find that the system exhibits homeostasis in that its attractors, that turn out to be fixed points or low period cycles, are highly insensitive to initial conditions or perturbations of gene configurations for any given fixed environment. At the same time the attractors show a wide variation when external media are varied implying that the system mounts a highly flexible response to changed environmental conditions. The regulatory dynamics acts to enhance the cellular growth rate under changed media.ConclusionOur study shows that the reconstructed genetic network regulating metabolism in E. coli is hierarchical, modular, and largely acyclic, with environmental variables controlling the root of the hierarchy. This architecture makes the cell highly robust to perturbations of gene configurations as well as highly responsive to environmental changes. The twin properties of homeostasis and response flexibility are achieved by this dynamical system even though it is not close to the edge of chaos.

Genotype networks in metabolic reaction spaces

BackgroundA metabolic genotype comprises all chemical reactions an organism can catalyze via enzymes encoded in its genome. A genotype is viable in a given environment if it is capable of producing all biomass components the organism needs to survive and reproduce. Previous work has focused on the properties of individual genotypes while little is known about how genome-scale metabolic networks with a given function can vary in their reaction content.ResultsWe here characterize spaces of such genotypes. Specifically, we study metabolic genotypes whose phenotype is viability in minimal chemical environments that differ in their sole carbon sources. We show that regardless of the number of reactions in a metabolic genotype, the genotypes of a given phenotype typically form vast, connected, and unstructured sets -- genotype networks -- that nearly span the whole of genotype space. The robustness of metabolic phenotypes to random reaction removal in such spaces has a narrow distribution with a high mean. Different carbon sources differ in the number of metabolic genotypes in their genotype network; this number decreases as a genotype is required to be viable on increasing numbers of carbon sources, but much less than if metabolic reactions were used independently across different chemical environments.ConclusionsOur work shows that phenotype-preserving genotype networks have generic organizational properties and that these properties are insensitive to the number of reactions in metabolic genotypes.

Targeting multiple targets in Pseudomonas aeruginosa PAO1 using flux balance analysis of a reconstructed genome-scale metabolic network

Constraint-based flux balance analysis (FBA) is a powerful tool for predicting target genes that can be engineered by analyzing the redistribution of metabolic fluxes on specific gene modifications. Specifically, the effects of metabolic gene deletions on flux distribution can be examined by forcing the fluxes of different reactions catalyzed by the corresponding gene product to zero. However, the target enzyme needs to be essential for survival of the organism to ensure that efficient chemical inhibition results in cell stasis or death. Here, we investigate the essentiality of enzymes in iMO1056 metabolic model of nosocomial pathogen Pseudomonas aeruginosa by performing in silico enzyme deletions using FBA. We identified 116/113 essential enzymes in rich medium in P. aeruginosa. These were then compared with human metabolic model to identify nonhomologous enzymes that could be possible drug targets. Here, we present a refined list of 41 novel potential targets for P. aeruginosa. These targets were then matched with the enzymes belonging to 97 correlated clusters through which we propose the concept of “one target per cluster.” Our approach relates to the “single drug multiple target (SDMT)” concept and has potential in efficient drug target discovery.

Comparative Proteomic Analyses of Avirulent, Virulent, and Clinical Strains of Mycobacterium tuberculosis Identify Strain-specific Patterns

Mycobacterium tuberculosis is an adaptable intracellular pathogen, existing in both dormant as well as active disease-causing states. Here, we report systematic proteomic analyses of four strains, H37Ra, H37Rv, and clinical isolates BND and JAL, to determine the differences in protein expression patterns that contribute to their virulence and drug resistance. Resolution of lysates of the four strains by liquid chromatography, coupled to mass spectrometry analysis, identified a total of 2161 protein groups covering ∼54% of the predicted M. tuberculosis proteome. Label-free quantification analysis of the data revealed 257 differentially expressed protein groups. The differentially expressed protein groups could be classified into seven K-means cluster bins, which broadly delineated strain-specific variations. Analysis of the data for possible mechanisms responsible for drug resistance phenotype of JAL suggested that it could be due to a combination of overexpression of proteins implicated in drug resistance and the other factors. Expression pattern analyses of transcription factors and their downstream targets demonstrated substantial differential modulation in JAL, suggesting a complex regulatory mechanism. Results showed distinct variations in the protein expression patterns of Esx and mce1 operon proteins in JAL and BND strains, respectively. Abrogating higher levels of ESAT6, an important Esx protein known to be critical for virulence, in the JAL strain diminished its virulence, although it had marginal impact on the other strains. Taken together, this study reveals that strain-specific variations in protein expression patterns have a meaningful impact on the biology of the pathogen.

Environmental versatility promotes modularity in genome-scale metabolic networks

BackgroundThe ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organisms biomass compounds from nutrients in this environment. An organisms metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks.ResultsUsing recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks.ConclusionsOur work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization.

Forman curvature for complex networks

We adapt Formans discretization of Ricci curvature to the case of undirected networks, both weighted and unweighted, and investigate the measure in a variety of model and real-world networks. We find that most nodes and edges in model and real networks have a negative curvature. Furthermore, the distribution of Forman curvature of nodes and edges is narrow in random and small-world networks, while the distribution is broad in scale-free and real-world networks. In most networks, Forman curvature is found to display significant negative correlation with degree and centrality measures. However, Forman curvature is uncorrelated with clustering coefficient in most networks. Importantly, we find that both model and real networks are vulnerable to targeted deletion of nodes with highly negative Forman curvature. Our results suggest that Forman curvature can be employed to gain novel insights on the organization of complex networks.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

Randomizing Genome-Scale Metabolic Networks

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints.

STDP-driven networks and the C. elegans neuronal network

We study the dynamics of the structure of a formal neural network wherein the strengths of the synapses are governed by spike-timing-dependent plasticity (STDP). For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of a real neural network of C. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of the model parameters.

Network reconstruction and systems analysis of plant cell wall deconstruction by Neurospora crassa

BackgroundPlant biomass degradation by fungal-derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction.ResultsTo expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa. To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighted to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network, leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of N. crassa.ConclusionsHere we built a network that serves as a scaffold for integration of diverse experimental datasets. This approach led to the elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi and a novel function for the transcription factor CLR-2. This expanding network will aid in efforts to rationally engineer industrially relevant hyper-production strains.