Biology subjects

Zielinski, D. C.

Publications and source records attributed to Zielinski, D. C..

3 recordsLinked to original sources

bioRxiv2018

Modeling genome-wide evolution of catalytic turnover rates: Strong epistasis shaped modern enzyme kinetics

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. To address this question, we combine genome-scale metabolic modelling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. A small number of biophysically constrained enzymes suffice to induce diminishing returns epistasis that prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. In addition, multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape is smooth and causes kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data on in vitro and in vivo turnover rates, validating our modelling approach. Our analysis thus suggests that enzyme evolution can be predicted on a genome scale and reveals the mechanisms by which evolutionary forces shape modern kcats and the whole of cell metabolism.

evolutionary biology↗

bioRxiv2017

Functional pathways for metabolic network-based data analysis: the MetPath algorithm

Analyzing biological data using pathways helps identify trends in data tied to the function of a network. A large number of pathway-based analysis tools have been developed toward this goal. These pathways are often manually curated and thus associations are subject to the biases of the curator. A potentially attractive alternative is to define pathways based on the inherent functionality and connectivity of the network itself. Within metabolism, functionality is defined by the production and consumption of metabolites, and connectivity by metabolites participating in reactions through common enzymes. In this work, we present an algorithm, termed MetPath, that calculates pathways for production and consumption of metabolites. We show how these pathways have attractive properties, such as the ability to integrate multiple data types and weight contribution of genes within the pathway by their functional contribution to metabolite production/consumption. Pathways calculated in this manner are condition-specific and thus are custom tailored to the system of interest, in contrast to curated pathways. We find that these pathways predict gene expression correlation better compared to manually-curated pathways. Additionally, we find that these pathways can be used to understand gene expression changes between growth conditions and between cell types. This work serves to better understand the functional pathway structure underlying cell metabolism and helps to enable systems analyses of high-throughput data.

systems biology↗

bioRxiv2017

Topological and Kinetic Determinants of the Modal Matrices of Dynamic Models of Metabolism

Linear analysis of kinetic models of metabolism can help in understanding the dynamic response of metabolic networks. Central to linear analysis of these models are two key matrices: the Jacobian matrix (J) and its modal matrix (M-1). The modal matrix contains dynamically independent motions of the kinetic model, and it is sparse in practice. Understanding the sparsity structure of the modal matrix provides insight into metabolic network dynamics. In this study, we analyze the relationship between J and M-1. First, we show that diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures within M-1. Dominant diagonal elements in J approximate the eigenvalues corresponding to these simple modes, in which a single metabolite is driven back to its reference state on a characteristic timescale. Second, we analyze more complicated mode structures in M-1, in which two or more variables move in a certain ratio relative to one another on defined time scales. We show that complicated modes originate from sub-matrices of topologically connected elements of similar magnitude in J. Third, we describe the origin of these mode structure features based on the network stoichiometric matrix S and the reaction kinetic gradient matrix G. We demonstrate that the topologically-connected reaction sensitivities of similar magnitude in G play a central role in determining the mode structure. Ratios of these reaction sensitivities represent equilibrium balances of half reactions that are defined by linearization of the bilinear mass action rate laws followed by enzymatic reactions. These half-reaction equilibrium ratios are key determinants of modal structure for both simple and complicated modes. The work presented here helps to establish a foundation for understanding the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions.

systems biology↗