Dynamic optimization of metabolic networks coupled with gene expression

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle

The Transforming Method Between Two Reversible Functions

This paper presents an original method of designing some special reversible circuits. This method is intended for the most popular gate set with three types of gates CNT (Control, NOT and Toffoli). The presented algorithm is based on two types of cascades with these reversible gates. The problem of transformation between two reversible functions is solved. This method allows to find optimal reversible circuits. The paper is organized as follows. Section 1 and 2 recalls basic concepts of reversible logic. Especially the two types of cascades of reversible function are presented. In Section 3 there is introduced a problem of analysis of the cascades. Section 4 describes the method of synthesis of the optimal cascade for transformation of the given reversible function into another one

Strongly Universal Reversible Gate Sets

It is well-known that the Toffoli gate and the negation gate together yield a universal gate set, in the sense that every permutation of $\{0,1\}^n$ can be implemented as a composition of these gates. Since every bit operation that does not use all of the bits performs an even permutation, we need to use at least one auxiliary bit to perform every permutation, and it is known that one bit is indeed enough. Without auxiliary bits, all even permutations can be implemented. We generalize these results to non-binary logic: If $A$ is a finite set of odd cardinality then a finite gate set can generate all permutations of $A^n$ for all $n$, without any auxiliary symbols. If the cardinality of $A$ is even then, by the same argument as above, only even permutations of $A^n$ can be implemented for large $n$, and we show that indeed all even permutations can be obtained from a finite universal gate set. We also consider the conservative case, that is, those permutations of $A^n$ that preserve the weight of the input word. The weight is the vector that records how many times each symbol occurs in the word. It turns out that no finite conservative gate set can, for all $n$, implement all conservative even permutations of $A^n$ without auxiliary bits. But we provide a finite gate set that can implement all those conservative permutations that are even within each weight class of $A^n$.Comment: Submitted to Rev Comp 201

Dynamic Combinatorial Libraries: From Exploring Molecular Recognition to Systems Chemistry

Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.

Practically Useful Models for Kinetics of Biodiesel Production

© 2019 American Chemical Society. We develop four kinetic models of varying complexity for biodiesel production. The models incorporate both transesterification and saponification, thereby making them practically applicable. We then propose an iterative parameter estimation algorithm to identify a prefixed number of significant rate constants via sensitivity analysis and estimate their kinetic parameters (A and ΔE) using nonlinear regression. Using experimental data on eight different oils, two alcohols, and two catalysts, we show that our models accurately predict the dynamic concentration profiles of various species during the transesterification of oil. Furthermore, we demonstrate the applicability of the best model (based on the values of Mean Absolute Error, Root Mean Square Error, and Akaike Information Criterion) for 11 additional experiments by predicting the final biodiesel properties with significant accuracy. Finally, using N-way ANOVA, we identify the choice of oil, alcohol, and catalyst as the most significant input factors followed by the operating conditions of the reactor

Flux analysis in central carbon metabolism in plants: 13C NMR experiments and analysis

Metabolic flux analysis is crucial in metabolic engineering. This research concentrated on improvements in 13C labeling-based flux analysis, a powerful flux quantification method, particularly oriented toward application to plants. Furthermore, systemic 13C flux analyses were performed on two model plant systems: Glycine max (soybean) embryos, and Catharanthus roseus hairy roots.;The concepts \u27bond integrity\u27, \u27bondomer\u27 and the algorithm \u27Boolean function mapping\u27 were introduced, to facilitate efficient flux evaluation from carbon bond labeling experiments, and easier flux identifiability analysis.;13C labeling experiments were performed on developing soybean (Glycine max) embryos and C. roseus hairy roots. A computer program, NMR2Flux, was developed to automatically calculate fluxes from the labeling data. This program accepts a user-defined metabolic network model, and incorporates recent mathematical advances toward accurate and efficient evaluation of fluxes and their standard deviations. Several physiological insights were obtained from the flux results. For instance, in soybean embryos, the reductive pentose phosphate pathway was active in the plastid and negligible in the cytosol. Also, unknown fluxes (such as plastidic fructose-1,6-bisphosphatase) could be identified and quantified. To the best of the author\u27s knowledge, this is the most comprehensive flux analysis of a plant system to date.;Investigations on flux identifiability were carried out for the soybean embryo system. Using these, optimal labeling experiments were designed, that utilize judicious combinations of labeled varieties of two substrates (sucrose and glutamine), to maximize the statistical quality of the evaluated fluxes.;The identity of four intense peaks observed in the 2-D [13C, 1H] spectra of protein isolated from soybean embryos, was investigated. These peaks were identified as levulinic acid and 5-hydroxymethyl furfural, and were degradation products of glycosylating sugars associated with soybean embryo protein. A 2-D NMR study was conducted on them, and it was shown that the metabolic information in the degradation products can be used toward metabolic flux or pathway analysis.;In addition, the elemental make-up and composition of the biomass of C. roseus hairy roots (crucial toward flux analysis) is reported. 89.2% (+/-9.7%) of the biomass was accounted for.*;*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat