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. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

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. The induced labors were taken as the control group since the timing of labor depends mostly on the working schedule of personnel and not on the intrinsic rhythmic characteristics. For induced labors, the main peak in the number of labor cases was observed in the late morning hours (around 10 AM) for all gestational ages. The prominence of this peak becomes smaller in spontaneous premature labors with gradually disrupting rhythmicity in very preterm and extremely preterm cases. Labors starting with spontaneous contractions peak between 6 and 7 AM and lose the rhythmicity at 35 weeks of gestation while labors starting with a spontaneous rupture of membranes peak at 1 AM and lose the rhythmicity at 31 weeks of gestation, suggesting differences in underlying mechanisms. According to our knowledge, this is the first study that shows differences of circadian trends between different types of spontaneous labors, i.e., labors initiated with contraction and labors initiated with a spontaneous rupture of membranes. Moreover, the obtained results represent evidence of gradual disruption of rhythmicity from mild to extreme prematurity.

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. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

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. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.

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. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

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. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

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. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.

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. The research was carried out in two phases, a baseline period of five consecutive months and an intervention period of the same five consecutive months in the following year. The intervention was implemented after the baseline period and included training in verbal and non-verbal de-escalation techniques for the staff teams on experimental wards. Results: In the baseline study period, there were no significant differences in the incidence of aggressive behavior and physical restraints between the experimental and control groups. The incidence rates of aggressive events, severe aggressive events, and physical restraints per 100 treatment days decreased significantly after the intervention. Compared to the control group, the incidence rate of aggressive events was 73% lower in the experimental group (IRR = 0.268, 95% CI [0.221; 0.342]), while the rate of severe events was 86% lower (IRR = 0.142, 95% CI [0.107; 0.189]). During the intervention period, the incidence rate of physical restraints due to aggression in the experimental group decreased to 30% of the rate in the control group (IRR = 0.304, 95% CI [0.238; 0.386]). No reduction in the incidence of restraint used for reasons unrelated to aggression was observed. After the intervention, a statistically significant decrease in the severity of aggressive incidents (p < 0.001) was observed, while the average duration of restraint episodes did not decrease. Conclusion: De-escalation training is effective in reducing the incidence and severity of aggression and the use of physical restraints in acute psychiatric units. Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT05166278].

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. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.

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., epistasis) are believed to play a role in understanding molecular underpinnings of complex diseases such as inflammatory bowel disease (IBD). However, the process of identifying such interactions is complex due to for instance the curse of high dimensionality, dependencies in the data and non-linearity. Here, we propose a novel approach for robust and computationally efficient epistasis detection. We do so by first reducing dimensionality, per gene via diffusion kernel principal components (kpc). Subsequently, kpc gene summaries are used for downstream analysis including the construction of a gene-based epistasis network. We show that our approach is not only able to recover known IBD associated genes but also additional genes of interest linked to this difficult gastrointestinal disease.

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. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities.

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. OligoPrime is easy to use since it is accessible via a web browser and does not require any installation from the end user's perspective. It allows filtering and search of oligonucleotides by various parameters, which include the exact location of an oligonucleotide, its sequence, and availability. The oligonucleotide database behind the system is shared among the researchers working in the same laboratory or research group. Users might have different roles which define the access permissions and range from students to researchers and primary investigators. Furthermore, OligoPrime is easy to manage and install and is based on open-source software solutions. Its code is freely available at https://github.com/OligoPrime. Moreover, an implementation of OligoPrime, which can be used for testing is available at http://oligoprime.xyz/. To our knowledge, OligoPrime is the only software solution dedicated specifically to oligonucleotide management. We strongly believe that it has a large potential to enhance the transparency of use and to simplify the management of oligonucleotides in academic laboratories and research groups.

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 Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value < 0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the variability ( > 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.

Escherichia coli 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 PERIOD2 promoter were co-cultured with E. coli strains isolated from human fecal samples from healthy individuals. The two E. coli strains differ in the phylogenetic group and the number of ExPEC virulence-associated genes: BJ17 has only two, and BJ23 has 15 of 23 tested. In the first 24 h, the E. coli BJ17 affected the HepG2 circadian clock more than BJ23. Cosinor analysis shows a statistically significant change in the amplitude of PER1 and 2 and the phase advance of PER3. A high percentage of necrotic and apoptotic cells occurred at 72 h, while a correlation between the number of ExPEC genes and the influence on the HepG2 core clock gene expression was observed. Our study reveals that the E. coli genetic background is important for the effect on the mammalian circadian clock genes, indicating possible future use of probiotic E. coli strains to influence the host circadian clock.

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-Cyp51, notch signaling-Rbpj, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling-Ikbkg, and unknown lysosomal pathway-Glmp. 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. The diverse genetic models of liver fibrosis exposed a common transcriptional program with activated estrogen receptor alpha (ERα) signaling, and a network of interactions between regulators of lipid metabolism and transcription factors from cancer pathways and the immune system. The novel hallmarks of fibrosis are downregulated lipid pathways, including fatty acid, bile acid, and steroid hormone metabolism. Moreover, distinct metabolic subtypes of liver fibrosis were proposed, supported by unique enrichment of transcription factors based on the type of insult, disease stage, or potentially, also sex. The discovered novel features of multifactorial liver fibrotic pathologies could aid also in improved stratification of other fibrosis related pathologies.

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. We describe a design of programmable biological circuits that can be configured without additional genetic engineering. Their configuration can be changed in vivo, i.e. during the execution of their biological program, simply with an introduction of programming inputs. These, e.g., increase the degradation rates of selected proteins that store the current configuration of the circuit. Programming can be thus performed in the field as in the case of field-programmable gate array (FPGA) circuits, which present an attractive alternative of ASICs in digital electronics. We describe a basic programmable unit, which we denote configurable (bio)logical block (CBLB) inspired by the architecture of configurable logic blocks (CLBs), basic functional units within the FPGA circuits. The design of a CBLB is based on distributed cellular computing modules, which makes its biological implementation easier to achieve. We establish a computational model of a CBLB and analyse its response with a given set of biologically feasible parameter values. Furthermore, we show that the proposed CBLB design exhibits correct behaviour for a vast range of kinetic parameter values, different population ratios, and as well preserves this response in stochastic simulations.

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. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. CONCLUSION: CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.

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. ViDis is accessible as a web application, which can run on an arbitrary platform with a modern web browser. The platform's user friendly interfaces allow the users with different backgrounds to construct, share, and browse through medical algorithms. Visualization of the algorithms can be created using a flowchart diagram notation that is commonly applied in the design of computer software and is very intuitive to use and understand. Algorithms can be viewed in two different formats, that is, in the format of flowchart diagrams or in the format of sequential steps that guide the user from the beginning to the end of a medical procedure in dependence on his or her decisions made in each step of the process. ViDis enables registered users to create, edit, and share visualized medical algorithms and guest users to view these visualizations. To the best of our knowledge, this is the first platform for efficient sharing of medical algorithms with the community. We believe that ViDis provides an excellent platform for sharing medical knowledge and information among diagnosticians, clinicians, researchers, and patients.

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.].

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. Local methods focus only on the local area around nominal parameter values. This can be problematic when parameters exhibits the desired behavior over a large range of parameter perturbations or when parameter values are unknown. Global methods, on the other hand, investigate the whole space of parameter values and mostly rely on different sampling techniques. This can be computationally inefficient. To address these shortcomings 'glocal' approaches were developed that apply global and local approaches in an effective and rigorous manner. RESULTS: Herein, we present a computational approach for 'glocal' analysis of viable parameter regions in biological models. The methodology is based on the exploration of high-dimensional viable parameter spaces with global and local sampling, clustering and dimensionality reduction techniques. The proposed methodology allows us to efficiently investigate the viable parameter space regions, evaluate the regions which exhibit the largest robustness, and to gather new insights regarding the size and connectivity of the viable parameter regions. We evaluate the proposed methodology on three different synthetic gene regulatory network models, i.e. the repressilator model, the model of the AC-DC circuit and the model of the edge-triggered master-slave D flip-flop. CONCLUSIONS: The proposed methodology provides a rigorous assessment of the shape and size of viable parameter regions based on (1) the mathematical description of the biological system of interest, (2) constraints that define feasible parameter regions and (3) cost function that defines the desired or observed behavior of the system. These insights can be used to assess the robustness of biological systems, even in the case when parameter values are unknown and more importantly, even when there are multiple poorly connected viable parameter regions in the solution space. Moreover, the methodology can be efficiently applied to the analysis of biological systems that exhibit multiple modes of the targeted behavior.