An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

\ua9 2023 by the authors. This study conducts a comprehensive investigation to optimize the degradation of crystal violet (CV) dye using the Fenton process. The main objective is to improve the efficiency of the Fenton process by optimizing various physicochemical factors such as the Fe2+ concentration, H2O2 concentration, and pH of the solution. The results obtained show that the optimal dosages of Fe2+ and H2O2 giving a maximum CV degradation (99%) are 0.2 and 3.13 mM, respectively. The optimal solution pH for CV degradation is 3. The investigation of the type of acid for pH adjustment revealed that sulfuric acid is the most effective one, providing 100% yield, followed by phosphoric acid, hydrochloric acid, and nitric acid. Furthermore, the examination of sulfuric acid concentration shows that an optimal concentration of 0.1 M is the most effective for CV degradation. On the other hand, an increase in the initial concentration of the dye leads to a reduction in the hydroxyl radicals formed (HO•), which negatively impacts CV degradation. A concentration of 10 mg/L of CV gives complete degradation of dye within 30 min following the reaction. Increasing the solution temperature and stirring speed have a negative effect on dye degradation. Moreover, the combination of ultrasound with the Fenton process resulted in a slight enhancement in the CV degradation, with an optimal stirring speed of 300 rpm. Notably, the study incorporates the use of Gaussian process regression (GPR) modeling in conjunction with the Improved Grey Wolf Optimization (IGWO) algorithm to accurately predict the optimal degradation conditions. This research, through its rigorous investigation and advanced modeling techniques, offers invaluable insights and guidelines for optimizing the Fenton process in the context of CV degradation, thereby achieving the twin goals of cost reduction and environmental impact minimization

Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE

Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to compute similarity between nodes in different networks and an alignment strategy (AS) to find high-scoring alignments with respect to the total NCF over all aligned nodes (or node conservation). But, they then evaluate quality of their alignments via some other measure that is different than the node conservation measure used to guide the alignment construction process. Typically, one measures the amount of conserved edges, but only after alignments are produced. Hence, a recent attempt aimed to directly maximize the amount of conserved edges while constructing alignments, which improved alignment accuracy. Here, we aim to directly maximize both node and edge conservation during alignment construction to further improve alignment accuracy. For this, we design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs each conserved edge so that edges with highly NCF-similar end nodes are favored. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation, and which can be used with any NCF or combination of multiple NCFs. Using WAVE on top of established state-of-the-art NCFs leads to superior alignments compared to the existing methods that optimize only node conservation or only edge conservation or that treat each conserved edge the same. And while we evaluate WAVE in the computational biology domain, it is easily applicable in any domain.Comment: 12 pages, 4 figure

Variation in NOD2 Augments Th2- and Th17 Responses to Myelin Basic Protein in Multiple Sclerosis

Variations in the gene for the nucleotide-binding oligomerisation domain (NOD) 2 have been associated with Crohn's disease but not multiple sclerosis (MS). Here we investigate the effect of three polymorphisms in the NOD2 gene (rs5743277, rs2066842 and rs5743291) on cytokine production and CD4+ T cell proliferation elicited by human myelin basic protein (MBP) in blood mononuclear cell (MNC) cultures from 29 patients with MS. No polymorphism was observed at rs5743277. No associations with the rs2066842 polymorphism were found. Concerning rs5743291, none were homozygous for the minor allele. Seven of 29 (24%) patients were heterozygous, and five of these (71%) exhibited increased MBP-induced CD4+ T cell proliferation versus four of 22 (18%), who were homozygous for the major allele (p<0.04). Interleukin (IL)-5 was induced by MBP in MNC from the same five carriers versus two (9%) homozygotes (p<0.004); four carriers (57%) versus three non-carriers (14%) exhibited IL-17 responses to MBP (p<0.04). By contrast, we found no association between the polymorphisms investigated and interferon-gamma-, tumor necrosis factor-alpha-, IL-2, -4- or IL-10 responses to MBP. These results indicate that the rs5743291 polymorphism influences T helper (Th) cell 2- and Th17 cell responses in MNC from MS patients

An introductory guide to aligning networks using SANA, the Simulated Annealing Network Aligner

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological {\em networks} holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology -- the "structure" of the network -- is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment -- which is an essentially solved problem -- network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used. Here we introduce SANA, the {\it Simulated Annealing Network Aligner}. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between 2 or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks. Availability: https://github.com/waynebhayes/SAN

Interaction of Pattern Recognition Receptors with Mycobacterium Tuberculosis.

Tuberculosis (TB) is considered a major worldwide health problem with 10 million new cases diagnosed each year. Our understanding of TB immunology has become greater and more refined since the identification of Mycobacterium tuberculosis (MTB) as an etiologic agent and the recognition of new signaling pathways modulating infection. Understanding the mechanisms through which the cells of the immune system recognize MTB can be an important step in designing novel therapeutic approaches, as well as improving the limited success of current vaccination strategies. A great challenge in chronic disease is to understand the complexities, mechanisms, and consequences of host interactions with pathogens. Innate immune responses along with the involvement of distinct inflammatory mediators and cells play an important role in the host defense against the MTB. Several classes of pattern recognition receptors (PRRs) are involved in the recognition of MTB including Toll-Like Receptors (TLRs), C-type lectin receptors (CLRs) and Nod-like receptors (NLRs) linked to inflammasome activation. Among the TLR family, TLR1, TLR2, TLR4, and TLR9 and their down-stream signaling proteins play critical roles in the initiation of the immune response in the pathogenesis of TB. The inflammasome pathway is associated with the coordinated release of cytokines such as IL-1β and IL-18 which also play a role in the pathogenesis of TB. Understanding the cross-talk between these signaling pathways will impact on the design of novel therapeutic strategies and in the development of vaccines and immunotherapy regimes. Abnormalities in PRR signaling pathways regulated by TB will affect disease pathogenesis and need to be elucidated. In this review we provide an update on PRR signaling during M. tuberculosis infection and indicate how greater knowledge of these pathways may lead to new therapeutic opportunities