Semi-Deterministic vs. Genetic Algorithms for Global Optimization of Multichannel Optical Filters

A new global optimization algorithm is presented and applied to the design of high-channel-count multichannel filters based on sampled Fiber Bragg Gratings. We focus on the realization of particular designs corresponding to multichannel filters that consist of 16 and 38 totally reflective identical channels spaced 100 GHz. The results are compared with those obtained by a hybrid genetic algorithm and by the classical sinc method

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis

Funding Information: This work was sponsored by GlaxoSmithKline Biologicals SA whereby the NOVA University Lisbon was engaged under an Agreement for R and D Services. All authors were involved in the conception and design of the study. PD’s lab performed the experiments/acquired the data. JR, GO, RO analyzed and interpreted the data. All authors were involved in drafting the manuscript or critically revising it for important intellectual content. All authors had full access to the data and approved the manuscript before it was submitted by the corresponding author. Publisher Copyright: © 2022, The Author(s).Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.publishersversionpublishe

Effects of C aenorhabditis elegans sgk‐1 mutations on lifespan, stress resistance, and DAF ‐16/ F ox O regulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100177/1/acel12120.pd

combining first-principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, Fundação para a Ciência e Tecnologia (FCT).Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on threelayered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power.publishersversionpublishe

Bitcoin : une monnaie dématérialisée

International audienc

AphanoDB: a genomic resource for Aphanomyces pathogens.

BACKGROUND: The Oomycete genus Aphanomyces comprises devastating plant and animal pathogens. However, little is known about the molecular mechanisms underlying pathogenicity of Aphanomyces species. In this study, we report on the development of a public database called AphanoDB which is dedicated to Aphanomyces genomic data. As a first step, a large collection of Expressed Sequence Tags was obtained from the legume pathogen A. euteiches, which was then processed and collected into AphanoDB. DESCRIPTION: Two cDNA libraries of A. euteiches were created: one from mycelium growing on synthetic medium and one from mycelium grown in contact to root tissues of the model legume Medicago truncatula. From these libraries, 18,684 expressed sequence tags were obtained and assembled into 7,977 unigenes which were compared to public databases for annotation. Queries on AphanoDB allow the users to retrieve information for each unigene including similarity to known protein sequences, protein domains and Gene Ontology classification. Statistical analysis of EST frequency from the two different growth conditions was also added to the database. CONCLUSION: AphanoDB is a public database with a user-friendly web interface. The sequence report pages are the main web interface which provides all annotation details for each unigene. These interactive sequence report pages are easily available through text, BLAST, Gene Ontology and expression profile search utilities. AphanoDB is available from URL: http://www.polebio.scsv.ups-tlse.fr/aphano/