Systems approaches to modelling pathways and networks.

Peer reviewedPreprin

Analysis of metabolic flux using dynamic labeling and metabolic modeling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms which control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches having been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences are reviewed and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed

Rigidity and flexibility of biological networks

The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

Connecting growth with gene expression: of noise and numbers.

Growth is a dynamic process whereby cells accumulate mass. Growth rates of single cells are connected to RNA and protein synthesis rates, and therefore with biomolecule numbers. Noise in gene expression depends on these numbers, and is thus linked with cellular growth. Whether these global attributes of the cell participate in gene regulation is still largely unexplored. New experimental and modelling studies suggest that systemic variations in biomolecule numbers can coordinate cellular processes, including growth itself, through global regulatory feedback that acts in addition to genetic regulatory networks. Here, we review these findings and speculate on possible implications of this less appreciated layer of gene regulation for cellular physiology and adaptation to changing environments

Genome-scale metabolic network reconstruction of Polaromonas sp. strain JS666: analysis of cDCE degradation rates and design of experiments for bioremediation improvement

Release of chloroethene compounds into the environment often results in groundwater contamination, which puts people at risk of exposure by drinking contaminated water. cDCE (cis-1,2-dichloroethene) accumulation on subsurface environments is a common environmental problem due to stagnation and partial degradation of other precursor chloroethene species. Polaromonas sp. strain JS666 apparently requires no exotic growth factors to be used as a bioaugmentation agent for aerobic cDCE degradation. Although being the only suitable microorganism found capable of such, further studies are needed for improving the intrinsic bioremediation rates and fully comprehend the metabolic processes involved. In order to do so, a metabolic model, iJS666, was reconstructed from genome annotation and available bibliographic data. FVA (Flux Variability Analysis) and FBA (Flux Balance Analysis) techniques were used to satisfactory validate the predictive capabilities of the iJS666 model. The iJS666 model was able to predict biomass growth for different previously tested conditions, allowed to design key experiments which should be done for further model improvement and, also, produced viable predictions for the use of biostimulant metabolites in the cDCE biodegradation

Understanding metabolic robustness of Escherichia coli using genetic and environmental perturbations

Metabolism provides the essential biochemical intermediates and energy that enable life and its growth. In this thesis we studied robustness of Escherichia coli metabolism, by perturbing it with different methods and measuring the response at a molecular level. In Chapter 1, we introduce the latest insight into metabolic regulation and optimality in microbial model organisms. Overall, we identified and described two major gaps in knowledge: the limited amount of known metabolite-protein interactions and the unknown objectives towards which cells optimize their enzyme levels. Moreover, we provide a short introduction to the relevant methods utilized in this thesis. In Chapter 2, we describe a series of experiments which confirmed that CRISPRi is a reliable tool to specifically perturb metabolism in E. coli. We showcase the advantage of using a CRISPRi system integrated in the genome, which is suitable to apply inducible knockdowns of essential genes. We demonstrate this by characterizing growth for a library of over 100 strains and verifying inducibility and specificity with proteomics data. In Chapter 3 we applied the validated CRISPRi setup to perturb and study metabolism systematically. First, we used a pooled CRISPRi library to knock down all metabolic genes in E. coli. By following the appearance of growth defects with next generation sequencing, we show that metabolic enzymes are expressed at higher levels than strictly necessary. We then focused on a panel of 30 CRISPRi strains and characterize their response to lower enzyme levels with metabolomics and proteomics. We show that the metabolome can buffer perturbations of enzyme levels in two different stages: first, metabolites increase enzyme activity to maintain optimal growth and only later they activate gene regulatory feedbacks to specifically upregulate perturbed pathways. In Chapter 4 we employed a different approach to perturb bacterial metabolism, by growing E. coli in different environmental conditions and measuring the response at the metabolome level. We could show that in exponentially growing cells key biosynthetic products as amino acids and nucleotides are kept at relatively stable levels across different environments. We compared our dataset to a matching published proteomics dataset, showing that unlike the proteome, metabolite levels are independent from growth effects

Remnants of an ancient metabolism without phosphate

Phosphate is essential for all living systems, serving as a building block of genetic and metabolic machinery. However, it is unclear how phosphate could have assumed these central roles on primordial Earth, given its poor geochemical accessibility. We used systems biology approaches to explore the alternative hypothesis that a protometabolism could have emerged prior to the incorporation of phosphate. Surprisingly, we identified a cryptic phosphate-independent core metabolism producible from simple prebiotic compounds. This network is predicted to support the biosynthesis of a broad category of key biomolecules. Its enrichment for enzymes utilizing iron-sulfur clusters, and the fact that thermodynamic bottlenecks are more readily overcome by thioester rather than phosphate couplings, suggest that this network may constitute a "metabolic fossil" of an early phosphate-free nonenzymatic biochemistry. Our results corroborate and expand previous proposals that a putative thioester-based metabolism could have predated the incorporation of phosphate and an RNA-based genetic system. PAPERCLIP