pastboon: an R package to simulate parameterized stochastic Boolean networks.

Summary: Influencing the behavior of a Boolean network involves applying perturbations, which, in standard deterministic Boolean networks, is equivalent to modifying the update rules. Nevertheless, manipulating update functions to make a Boolean network exhibit the desired dynamics is challenging, as it requires extensive knowledge of the rationale behind the logical equations. Moreover, modifying logical rules can inadvertently alter essential functional and behavioral characteristics of the network.

AmiR-P3: An AI-based microRNA prediction pipeline in plants.

Background: MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest.

SL-scan identifies synthetic lethal interactions in cancer using metabolic networks.

Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction.

RT-DOb, a switch gene for the gene pair {Csf1r, Milr1}, can influence the onset of Alzheimer's disease by regulating communication between mast cell and microglia.

In biology, homeostasis is a central cellular phenomenon that plays a crucial role in survival. The central nervous system (CNS) is controlled by exquisitely sensitive homeostatic mechanisms when facing inflammatory or pathological insults. Mast cells and microglia play a crucial role in CNS homeostasis by eliminating damaged or unnecessary neurons and synapses.

Engineering a Novel Metabolic Pathway for Improving Cellular Malonyl-CoA Levels in Escherichia coli.

Cellular pool of malonyl-CoA in Escherichia coli is small, which impedes its utility for overproduction of natural products such as phenylpropanoids, polyketides, and flavonoids. In this study, we report the use of a new metabolic pathway to increase the malonyl-CoA concentration as a limiting metabolite in E. coli.

Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production.

Purpose: Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain.

Glucose Metabolism: Optimizing Regenerative Functionalities of Mesenchymal Stromal Cells Postimplantation.

Mesenchymal stromal cells (MSCs) are considered promising candidates for regenerative medicine applications. Their clinical performance postimplantation, however, has been disappointing. This lack of therapeutic efficacy is most likely due to suboptimal formulations of MSC-containing material constructs.

Critical assessment of genome-scale metabolic models of .

Genome-scale metabolic models (GEMs) have enabled researchers to perform systems-level studies of living organisms. Flux balance analysis (FBA), as a constraint-based technique, enables computation of reaction fluxes and prediction of the metabolic phenotypes of a cell under a set of specified conditions. The quality of a GEM is important for obtaining accurate predictions.

Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria.

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E.

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level.

Systematically gap-filling the genome-scale metabolic model of CHO cells.

Objective: Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power.

Impact of hfq and sigE on the tolerance of Zymomonas mobilis ZM4 to furfural and acetic acid stresses.

Zymomonas mobilis, as an ethanologenic microorganism with many desirable industrial features, faces crucial obstacles in the lignocellulosic ethanol production process. A significant hindrance occurs during the pretreatment procedure that not only produces fermentable sugars but also releases severe toxic compounds. As diverse parts of regulation networks are involved in different aspects of complicated tolerance to inhibitors, we developed ZM4-hfq and ZM4-sigE strains, in which hfq and sigE genes were overexpressed, respectively.

Rps27a might act as a controller of microglia activation in triggering neurodegenerative diseases.

Neurodegenerative diseases (NDDs) are increasing serious menaces to human health in the recent years. Despite exhibiting different clinical phenotypes and selective neuronal loss, there are certain common features in these disorders, suggesting the presence of commonly dysregulated pathways. Identifying causal genes and dysregulated pathways can be helpful in providing effective treatment in these diseases.

A system architecture for parallel analysis of flux-balanced metabolic pathways.

Elementary flux mode (EFM) analysis is a well-studied method in constraint-based modeling of metabolic networks. In EFM analysis, a network is decomposed into minimal functional pathways based on the assumption of balanced metabolic fluxes. In this paper, a system architecture is proposed that approximately models the functionality of metabolic networks.

prediction of enzymatic reactions catalyzed by acid phosphatases.

In present work, we describe a methodology for prediction of an enzymatic reaction for which no experimental data are available except for a gene sequence. As a challenging case, we have developed the method for identifying the putative substrates of monoester phosphatases, commonly known as acid phosphatase enzymes, which have no strong substrate specificity. Finding a preferable substrate for each one is an important task to unravel pathways involved in plant phosphate metabolism.

A Systems-Based Approach for Cyanide Overproduction by for Gold Bioleaching Enhancement.

With the constant accumulation of electronic waste, extracting precious metals contained therein is becoming a major challenge for sustainable development. is currently one of the microbes used for the production of cyanide, which is the main leaching agent for gold recovery. The present study aimed to propose a strategy for metabolic engineering of to overproduce cyanide, and thus ameliorate the bioleaching process.

A meta-analysis of gene expression data highlights synaptic dysfunction in the hippocampus of brains with Alzheimer's disease.

Since the world population is ageing, dementia is going to be a growing concern. Alzheimer's disease is the most common form of dementia. The pathogenesis of Alzheimer's disease is extensively studied, yet unknown remains.

A reconciliation of genome-scale metabolic network model of Zymomonas mobilis ZM4.

Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z.

A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production.

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization.

Beyond Taxonomic Analysis of Microbiomes: A Functional Approach for Revisiting Microbiome Changes in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most prevalent cancers in the world, especially in developed countries. In different studies, the association between CRC and dysbiosis of gut microbiome has been reported. However, most of these works focus on the taxonomic variation of the microbiome, which presents little, if any, functional insight about the reason behind and/or consequences of microbiome dysbiosis.

Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319.

Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications.

Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach.

Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs.

The metabolic network model of primed/naive human embryonic stem cells underlines the importance of oxidation-reduction potential and tryptophan metabolism in primed pluripotency.

Background: Pluripotency is proposed to exist in two different stages: Naive and Primed. Conventional human pluripotent cells are essentially in the primed stage. In recent years, several protocols have claimed to generate naive human embryonic stem cells (hESCs).

Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications.

Background: A genome-scale metabolic network model (GEM) is a mathematical representation of an organism's metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains.

Objectives: In the present study, we have evaluated the predictive power of two GEMs, namely Bsu1103 (for 168) and MZ1055 (for WSH002).