Set Membership Parameter Estimation and Design of Experiments Using Homothety

In this note we address the problems of obtaining guaranteed and as good as possible estimates of system parameters for linear discrete–time systems subject to bounded disturbances. Some existing results relevant for the set–membership parameter identification and outer–bounding are first reviewed. Then, a novel method for characterizing the consistent parameter set based on homothety is offered; the proposed method allows for the utilization of general compact and convex sets for outer–bounding. Based on these results, we consider the one–step input design and identifiability problems in set–membership setting. We provide a guaranteed approach for the one–step input design problem, by selecting optimal inputs for the purpose of parameter estimation. As optimality criterion, the dimension and the outer– bounding volume of the “anticipated ” consistent parameter set is considered. We furthermore derive a sufficient criterion for (one–step) parameter identifiability, i.e. when a point estimate for a parameter can be guaranteed for all possible measurements

The TFAM-to-mtDNA ratio defines inner-cellular nucleoid populations with distinct activity levels

In human cells, generally a single mitochondrial DNA (mtDNA) is compacted into a nucleoprotein complex denoted the nucleoid. Each cell contains hundreds of nucleoids, which tend to cluster into small groups. It is unknown whether all nucleoids are equally involved in mtDNA replication and transcription or whether distinct nucleoid subpopulations exist. Here, we use multi-color STED super-resolution microscopy to determine the activity of individual nucleoids in primary human cells. We demonstrate that only a minority of all nucleoids are active. Active nucleoids are physically larger and tend to be involved in both replication and transcription. Inactivity correlates with a high ratio of the mitochondrial transcription factor A (TFAM) to the mtDNA of the individual nucleoid, suggesting that TFAM-induced nucleoid compaction regulates nucleoid replication and transcription activity in vivo. We propose that the stable population of highly compacted inactive nucleoids represents a storage pool of mtDNAs with a lower mutational load

Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool

Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation

A novel nuclear genetic code alteration in yeasts and the evolution of codon reassignment in eukaryotes.

The genetic code is the cellular translation table for the conversion of nucleotide sequences into amino acid sequences. Changes to the meaning of sense codons would introduce errors into almost every translated message and are expected to be highly detrimental. However, reassignment of single or multiple codons in mitochondria and nuclear genomes, although extremely rare, demonstrates that the code can evolve. Several models for the mechanism of alteration of nuclear genetic codes have been proposed (including "codon capture," "genome streamlining," and "ambiguous intermediate" theories), but with little resolution. Here, we report a novel sense codon reassignment in Pachysolen tannophilus, a yeast related to the Pichiaceae. By generating proteomics data and using tRNA sequence comparisons, we show that Pachysolen translates CUG codons as alanine and not as the more usual leucine. The Pachysolen tRNACAG is an anticodon-mutated tRNA(Ala) containing all major alanine tRNA recognition sites. The polyphyly of the CUG-decoding tRNAs in yeasts is best explained by a tRNA loss driven codon reassignment mechanism. Loss of the CUG-tRNA in the ancient yeast is followed by gradual decrease of respective codons and subsequent codon capture by tRNAs whose anticodon is not part of the aminoacyl-tRNA synthetase recognition region. Our hypothesis applies to all nuclear genetic code alterations and provides several testable predictions. We anticipate more codon reassignments to be uncovered in existing and upcoming genome projects

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity

Fast, inexpensive, and reliable HPLC method to determine monomer fractions in poly(3-hydroxybutyrate-co-3-hydroxyvalerate)

The determination of the monomer fractions in polyhydroxyalkanoates is of great importance for research on microbial-produced plastic material. The development of new process designs, the validation of mathematical models, and intelligent control strategies for production depend enormously on the correctness of the analyzed monomer fractions. Most of the available detection methods focus on the determination of the monomer fractions of the homopolymer poly(3-hydroxybutyrate). Only a few can analyze the monomer content in copolymers such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate), which usually require expensive measuring devices, a high preparation time or the use of environmentally harmful halogenated solvents such as chloroform or dichloromethane. This work presents a fast, simple, and inexpensive method for the analysis of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) with high-performance liquid chromatography. Samples from a bioreactor experiment for the production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) with Cupriavidus necator H16 were examined regarding their monomer content using the new method and gas chromatography analysis, one of the most frequently used methods in literature. The results from our new method were validated using gas chromatography measurements and show excellent agreement. Key points ∙ The presented HPLC method is an inexpensive, fast and environmentally friendly alternative to existing methods for quantification of monomeric composition of PHBV. ∙ Validation with state of the art GC measurement exhibits excellent agreement over a broad range of PHBV monomer fractions

Enhanced incorporation of subnanometer tags into cellular proteins for fluorescence nanoscopy via optimized genetic code expansion

With few-nanometer resolution recently achieved by a new generation of fluorescence nanoscopes (MINFLUX and MINSTED), the size of the tags used to label proteins will increasingly limit the ability to dissect nanoscopic biological structures. Bioorthogonal (click) chemical groups are powerful tools for the specific detection of biomolecules. Through the introduction of an engineered aminoacyl–tRNA synthetase/tRNA pair (tRNA: transfer ribonucleic acid), genetic code expansion allows for the site-specific introduction of amino acids with “clickable” side chains into proteins of interest. Welldefined label positions and the subnanometer scale of the protein modification provide unique advantages over other labeling approaches for imaging at molecular-scale resolution. We report that, by pairing a new N-terminally optimized pyrrolysyl–tRNA synthetase (chPylRS2020) with a previously engineered orthogonal tRNA, clickable amino acids are incorporated with improved efficiency into bacteria and into mammalian cells. The resulting enhanced genetic code expansion machinery was used to label β-actin in U2OS cell filopodia for MINFLUX imaging with minimal separation of fluorophores from the protein backbone. Selected data were found to be consistent with previously reported high-resolution information from cryoelectron tomography about the cross-sectional filament bundling architecture. Our study underscores the need for further improvements to the degree of labeling with minimal-offset methods in order to fully exploit molecularscale optical three-dimensional resolution