Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models.

Constraint-based modelling and genome-scale metabolic models (GEMs) have been used extensively to analyze omics data, providing a mechanistic perspective on complex metabolic systems and networks [...

Integrative protocol for quantifying cholesterol-related sterols in human serum samples and building decision support systems.

The growing interest in clinical diagnostics has recently focused on metabolic biomarkers. Here, we present a protocol for sample preparation, extraction of cholesterol-related sterols, and quantification of 10 sterols in human blood serum samples using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). We also describe steps of machine learning techniques to develop novel decision-making systems that offer potential benefits in disease monitoring and surveillance by measuring metabolic pathways.

SAILoR: Structure-Aware Inference of Logic Rules.

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes.

Synthetic biological neural networks: From current implementations to future perspectives.

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities.

Circadian characteristics of term and preterm labors.

The labor is a physiological event considered to have its own circadian (diurnal) rhythm, but some of the data remain conflicting, especially for preterm births. In this retrospective study, we analyzed the circadian trends of labor onset times in the Slovenian birth cohort from 1990 to 2018 with over 550,000 cases of singleton births. The number of term and preterm labor onsets was calculated for each hour in a day and circadian trends were evaluated for each of the study groups by modeling with a generalized Poisson distribution linked with the cosinor regression model using logarithmic link function.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems.

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients.

Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages.

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation.

Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability.

From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets.

A computational design of a programmable biological processor.

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer.

Review and assessment of Boolean approaches for inference of gene regulatory networks.

Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks.

Effectiveness of De-Escalation in Reducing Aggression and Coercion in Acute Psychiatric Units. A Cluster Randomized Study.

Objective: Most guidelines for the management of aggressive behavior in acute psychiatric patients describe the use of de-escalation as the first-choice method, but the evidence for its effectiveness is inconsistent. The aim of the study was to assess the effect of verbal and non-verbal de-escalation on the incidence and severity of aggression and the use of physical restraints in acute psychiatric wards.

Methods: A multi-center cluster randomized study was conducted in the acute wards of all psychiatric hospitals in Slovenia.

Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models.

COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets.

Detecting gene-gene interactions from GWAS using diffusion kernel principal components.

Genes and gene products do not function in isolation but as components of complex networks of macromolecules through physical or biochemical interactions. Dependencies of gene mutations on genetic background (i.e.

OligoPrime: An Information System for Oligonucleotide Management.

With the increasing number of molecular biology techniques, large numbers of oligonucleotides are frequently involved in individual research projects. Thus, a dedicated electronic oligonucleotide management system is expected to provide several benefits such as increased oligonucleotide traceability, facilitated sharing of oligonucleotides between laboratories, and simplified (bulk) ordering of oligonucleotides. Herein, we describe OligoPrime, an information system for oligonucleotide management, which presents a computational support for all steps in an oligonucleotide lifecycle, namely, from its ordering and storage to its application, and disposal.

Guided extraction of genome-scale metabolic models for the integration and analysis of omics data.

Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865.

Affects Expression of Circadian Clock Genes in Human Hepatoma Cells.

Recent research has indicated that dysbiosis of the gut microbiota can lead to an altered circadian clock of the mammalian host. Herein we developed an original system that allows real-time circadian studies of human HepG2 hepatoma cells co-cultured with bacteria. The HepG2 cells with stably integrated firefly luciferase reporter under the control of promoter were co-cultured with strains isolated from human fecal samples from healthy individuals.

Common Transcriptional Program of Liver Fibrosis in Mouse Genetic Models and Humans.

Multifactorial metabolic diseases, such as non-alcoholic fatty liver disease, are a major burden to modern societies, and frequently present with no clearly defined molecular biomarkers. Herein we used system medicine approaches to decipher signatures of liver fibrosis in mouse models with malfunction in genes from unrelated biological pathways: cholesterol synthesis-, notch signaling-, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling-, and unknown lysosomal pathway-. Enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and TRANScription FACtor (TRANSFAC) databases complemented with genome-scale metabolic modeling revealed fibrotic signatures highly similar to liver pathologies in humans.

Field-programmable biological circuits and configurable (bio)logic blocks for distributed biological computing.

Synthetic biology applications often require engineered computing structures, which can be programmed to process the information in a given way. However, programming of these structures usually requires significant amount of trial-and-error genetic engineering. This process is to some degree analogous to the design of application-specific integrated circuits (ASIC) in the domain of digital electronic circuits, which often require complex and time-consuming workflows to obtain a desired response.

CosinorPy: a python package for cosinor-based rhythmometry.

Background: Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.

Results: We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis.

Correction to: Computational analysis of viable parameter regions in models of synthetic biological systems.

[This corrects the article DOI: 10.1186/s13036-019-0205-0.].

ViDis: A Platform for Constructing and Sharing of Medical Algorithms.

The literature and the Internet provide different sources, in which medical community as well as patients can browse through medical algorithms. These algorithms are dispersed and use different formats of presentation. We present visualized diagnosis (ViDis), a web platform aimed to construction and sharing of graphical representations of medical algorithms in a single place and in a unified format.

Computational analysis of viable parameter regions in models of synthetic biological systems.

Background: Gene regulatory networks with different topological and/or dynamical properties might exhibit similar behavior. System that is less perceptive for the perturbations of its internal and external factors should be preferred. Methods for sensitivity and robustness assessment have already been developed and can be roughly divided into local and global approaches.

Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks.

Background: Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting.