Is metabolism goal-directed? Investigating the validity of modeling biological systems with cybernetic control via omic data

Cybernetic models are uniquely juxtaposed to other metabolic modeling frameworks in that they describe the time-dependent regulation of cellular reactions in terms of dynamic metabolic goals. This approach contrasts starkly with purely mechanistic descriptions of metabolic regulation which seek to explain metabolic processes in high resolution — a clearly daunting undertaking. Over a span of three decades, cybernetic models have been used to predict metabolic phenomena ranging from resource consumption in mixed-substrate environments to intracellular reaction fluxes of intricate metabolic networks. While the cybernetic approach has been validated in its utility for the prediction of metabolic phenomena, its central feature, the goal-directed control strategy, has yet to be scrutinized through comparison with omic data. Ultimately, the aim of this work is to address the question Is metabolism-goal directed? through the analysis of biological data. To do so, this work investigates the idea that metabolism is goal-directed from three distinct angles. The first is to make a comparison of cybernetic models to other metabolic modeling frameworks. These mathematical formulations for intracellular chemical reaction networks range from purely mechanistic, kinetic models to linear programming approximations. Instead of comparing these frameworks directly on the basis of accuracy alone, a novel approach to systems biological model selection is developed. This approach compares models using information theoretic arguments. From this point of view, the model that compresses biological data best captures the most regularity in the data generated by a process. This framework is used to compare the flux predictions of cybernetic, constraint-based and kinetic models in several case studies. Cybernetic models, in the test cases examined, provide the most compact description of metabolic fluxes. This method of analysis can be extended to any systems biological model selection problem for the purposes of optimization and control. To further examine cybernetic control mechanisms, the second portion of this dissertation focuses on confronting cybernetic variable predictions with data that is representative of enzyme regulation. More specifically, the dynamic behavior of cybernetic variables, ui, which are representative of enzyme synthesis control are matched with gene expression data that represents the control of enzyme synthesis in cells. This comparison is made for the model system of cybernetic modeling, diauxic growth, and for prostaglandin (PG) metabolism in mammalian cells. Via analysis of these systems, a correlation between the dynamic behavior of cybernetic control variables and the true mechanisms that guide cellular regulation is discovered. Additionally, this result demonstrates potential use of cybernetic variables for the prediction of relative changes in gene expression levels. The last approach taken to test the veracity of cybernetic control is to develop a technique to mine objective functions from biological data. In this approach, returns on investment (ROIs) for various pathways are first established through simultaneous analysis of metabolite and gene expression data for a given metabolic system. Following this, the ROIs are used to determine a metabolic systems observed goal signal. Gene expression data is then mined to select genes that show expression changes that are similar to the goal signal\u27s behavior. This gene list is then analyzed to determine enriched biological pathways. In the final step, these pathways are then surveyed in the literature to establish feasible metabolic goals for the system of interest. This method is applied to analyze diauxic growth and prostaglandin systems and generates objective functions that are relevant to known properties of these metabolic networks from the literature. An enhanced understanding of metabolic goals in mammalian systems generated by this work reveals the potential utility of cybernetic modeling in new directions related to translational research. Overall, this investigation yields support of the notion of dynamic metabolic goals in cells through comparison of metabolic modeling approaches and through the analysis of omic data. From these results, a lucid argument is made for the use of goal-directed modeling approaches and a deeper understanding of the optimal nature of metabolic regulation is gained

A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation

Mu2e Run I Sensitivity Projections for the Neutrinoless mu(-) -> e(-) Conversion Search in Aluminum

The Mu2e experiment at Fermilab will search for the neutrinoless μ−→e− conversion in the field of an aluminum nucleus. The Mu2e data-taking plan assumes two running periods, Run I and Run II, separated by an approximately two-year-long shutdown. This paper presents an estimate of the expected Mu2e Run I search sensitivity and includes a detailed discussion of the background sources, uncertainties of their prediction, analysis procedures, and the optimization of the experimental sensitivity. The expected Run I 5σ discovery sensitivity is Rμe=1.2×10−15, with a total expected background of 0.11±0.03 events. In the absence of a signal, the expected upper limit is Rμe&lt;6.2×10−16 at 90% CL. This represents a three order of magnitude improvement over the current experimental limit of Rμe&lt;7×10−13 at 90% CL set by the SINDRUM II experiment.</jats:p