Search Results (15)

Nucleic acid strand displacement reactions (SDRs) are fundamental building blocks of dynamic DNA nanotechnology. A detailed understanding of their kinetics is crucial for designing efficient sequences and regulating reaction networks with applications in biosensing, synthetic biology, biocomputing, and medical diagnostics. Since the development of toehold-mediated strand displacement, researchers have devised many strategies to adjust reaction kinetics. These efforts have expanded the available tools in DNA nanotechnology. This review summarizes the basic principles and recent advances in activation strategies, emphasizing the role of strand proximity as a central driving force. Proximity-based approaches include toehold docking, associative toeholds, remote toeholds, and allosteric designs, as well as strategies that operate without explicit toehold motifs. These methods enable flexible and scalable construction of DNA reaction networks. We further discuss how combining different activation and kinetic control approaches gives rise to dynamic networks with complex and dissipative behaviors, providing new directions for DNA-based nanotechnology. Full article

This study aimed to evaluate the anticancer activity of lamellar alkaloid derivatives extracted from the tunicate Didemnum abradatum from Moucha Island (Djibouti), focusing on their antiviability against human cell lines and using biocomputational analyses via the Integrated Biomolecular Profiling and Mechanism Evaluation (IBProME) method to understand their mechanisms of action. Two alkaloids were isolated, lamellarin D and lamellarin T, whose structures were confirmed by state-of-the-art analytical techniques. Cell viability tests were performed on PC3, A549 and JIMT-T1 cell lines, and IBProME analyses were used to predict their interactions with p53 protein and evaluate their toxicological and pharmacokinetic profiles. The results showed that lamellarin D was particularly effective against prostate and lung cancer cells, with respective IC₅₀ values of 5.25 µg/mL and 8.64 µg/mL, close to those of doxorubicin. In contrast, lamellarin T showed less marked activity but remains promising. Computational analyses via IBProME highlighted differences in chemical reactivity between the two compounds, with lamellarin D being more reactive. Toxicity tests revealed that lamellarin D exhibited lower acute toxicity than lamellarin T. In terms of pharmacokinetic properties, both molecules showed low absorption and moderate bioavailability, although lamellarin T displayed more marked lipophilicity. These results suggest that lamellars, particularly lamellarin D, have therapeutic potential for the treatment of certain types of cancer. Full article

The cochlear neuromodulator provides substantial auditory perception to those with impaired hearing. The accurate insertion of electrodes into the cochlea is an important factor, as misplaced may lead to further damage. The impedance measurement may be used as a marker of the electrode insertion guidance. It is feasible to investigate the impact of the dielectric properties of the cochlea tissue layers on the electrode insertion guidance using sophisticated bio-computational methods that are impractical or impossible to perform in cochlear implant (CI) patients. Although previous modeling approaches of the cochlea argued that the capacitive impact of the tissue layer can be neglected using the quasi-static (QS) approximation method, it is widely accepted that tissue acts as a frequency filter. Thus, the QS method may not always be appropriate due to short-duration pulses. This study aimed to investigate the impact of the frequency-dependent dielectric properties of the cochlea tissue layers on the impedance variation by following a systematic approach. The volume conductor model of the cochlea layers was developed, the dielectric properties of each tissue layer were attained, and the cochlea neuromodulator settings were applied to obtain the results based on both QS and transient solution (TS) methods. The results based on the QS and TS methods were compared to define to what extent these parameters affect the outcome. It was suggested that the capacitive impact of the cochlea layers should be considered after a certain frequency level. Full article

Introduction: Upper-limb loss results in significant functional impairment and a reduced quality of life. A human–machine interface (HMI) using surface electromyography (sEMG) establishes a link between the user and a hand prosthesis to recognize hand gestures and motions. Bio-impedance analysis (BIA) is a non-invasive way of assessing body composition and is adapted for hand motion interpretation with promising results. However, an optimized BIA recording strategy has not yet been achieved due to various parameters (e.g., the large scale of the neuromodulator settings and variations in the tissue dielectric properties). This paper investigates the impact of the dielectric properties of the tissue layers on the bio-impedance variation based on different simulation frequency spectra using the transient modeling method. The model can provide helpful insight into the effect of dielectric properties on the impedance variation of the upper limbs, which is otherwise challenging to investigate in practical studies. Method: The 3D realistic human upper arm model was developed based on the image data set. The dielectric properties of each tissue layer were attained based on each frequency level and the time-based current pulse was applied. The electrical potential variation for each frequency level was recorded to calculate impedance variation based on the applied current level. The unseen current distribution across the upper arm’s fat, muscle, and bone layers under the skin was also simulated to aid in selecting the most responsive area for BIA towards an optimal simulation frequency level. The results were obtained based on 10 Hz, 1 kHz, 10 kHz, 100 kHz, 500 kHz, and 1 MHz levels. Results: The results show that the frequency-based dielectric properties of the tissue layer have a significant impact on impedance variation. Conclusion: In this study, a 3D bio-computational model of the human arm was developed to investigate the impact of dielectric properties on impedance. The results of the study may provide helpful insight into an optimized BIA recording strategy. Full article

Network-based biocomputation (NBC) relies on accurate guiding of biological agents through nanofabricated channels produced by lithographic patterning techniques. Here, we report on the large-scale, wafer-level fabrication of optimized microfluidic channel networks (NBC networks) using electron-beam lithography as the central method. To confirm the functionality of these NBC networks, we solve an instance of a classical non-deterministic-polynomial-time complete (“NP-complete”) problem, the subset-sum problem. The propagation of cytoskeletal filaments, e.g., molecular motor-propelled microtubules or actin filaments, relies on a combination of physical and chemical guiding along the channels of an NBC network. Therefore, the nanofabricated channels have to fulfill specific requirements with respect to the biochemical treatment as well as the geometrical confienement, with walls surrounding the floors where functional molecular motors attach. We show how the material stack used for the NBC network can be optimized so that the motor-proteins attach themselves in functional form only to the floor of the channels. Further optimizations in the nanolithographic fabrication processes greatly improve the smoothness of the channel walls and floors, while optimizations in motor-protein expression and purification improve the activity of the motor proteins, and therefore, the motility of the filaments. Together, these optimizations provide us with the opportunity to increase the reliability of our NBC devices. In the future, we expect that these nanolithographic fabrication technologies will enable production of large-scale NBC networks intended to solve substantially larger combinatorial problems that are currently outside the capabilities of conventional software-based solvers. Full article

We present here COOBoostR, a computational method designed for the putative prediction of the tissue- or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types, which best explain the somatic mutation density landscape of any sample of interest. A specific tissue or cell type matching the chromatin mark feature with highest explanatory power is designated as a potential tissue- or cell-of-origin. Through integrating either ChIP-seq based chromatin data, along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed, with comparable or better tissue or cell-of-origin prediction performance (prediction accuracy—normal cells/tissue: 76.99%, precancerous lesions: 95.65%, cancer cells: 89.39%). In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia. Full article

GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems. Full article

Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances. Full article

Background: Pulmonary hypertension (PH) is a vasoconstrictive disease characterized by elevated mean pulmonary arterial pressure (mPAP) at rest. Idiopathic pulmonary arterial hypertension (iPAH) and chronic thromboembolic pulmonary hypertension (CTEPH) represent two distinct subtypes of PH. Persisting PH leads to right ventricular (RV) hypertrophy, heart failure, and death. RV performance predicts survival and surgical interventions re-establishing physiological mPAP reverse cardiac remodeling. Nonetheless, a considerable number of PH patients are deemed inoperable. The underlying mechanism(s) governing cardiac regeneration, however, remain largely elusive. Methods: In a longitudinal approach, we profiled the transcriptional landscapes of hypertrophic RVs and recovered hearts 3 months after surgery of iPAH and CTEPH patients. Results: Genes associated with cellular responses to inflammatory stimuli and metal ions were downregulated, and cardiac muscle tissue development was induced in iPAH after recovery. In CTEPH patients, genes related to muscle cell development were decreased, and genes governing cardiac conduction were upregulated in RVs following regeneration. Intriguingly, early growth response 1 (EGR1), a profibrotic regulator, was identified as a major transcription factor of hypertrophic RVs in iPAH and CTEPH. A histological assessment confirmed our biocomputational results, and suggested a pivotal role for EGR1 in RV vasculopathy. Conclusion: Our findings improved our understanding of the molecular events driving reverse cardiac remodeling following surgery. EGR1 might represent a promising candidate for targeted therapy of PH patients not eligible for surgical treatment. Full article

Genome size is one of the fundamental cytogenetic features of a species, which is critical for the design and initiation of any genome sequencing projects and can provide essential insights in studying taxonomy, cytogenetics, phylogenesis, and evolutionary studies. However, this key cytogenetic information is almost lacking in the endemic species Reseda pentagyna and the locally rare species Reseda lutea in Saudi Arabia. Therefore, genome size was analyzed by propidium iodide PI flow cytometry and compared to k-mer analysis methods. The standard method for genome size measures (flow cytometry) estimated the genome size of R. lutea and R. pentagyna with nuclei isolation MB01 buffer were found to be 1.91 ± 0.02 and 2.09 ± 0.03 pg/2 °C, respectively, which corresponded approximately to a haploid genome size of 934 and 1.022 Mbp, respectively. For validation, K-mer analysis was performed on both species’ Illumina paired-end sequencing data from both species. Five k-mer analysis approaches were examined for biocomputational estimation of genome size: A general formula and four well-known programs (CovEST, Kmergenie, FindGSE, and GenomeScope). The parameter preferences had a significant impact on GenomeScope and Kmergenie estimates. While the general formula estimations did not differ considerably, with an average genome size of 867.7 and 896. Mbp. The differences across flow cytometry and biocomputational predictions may be due to the high repeat content, particularly long repetitive regions in both genomes, 71% and 57%, which interfered with k-mer analysis. GenomeScope allowed quantification of high heterozygosity levels (1.04 and 1.37%) of R. lutea and R. pentagyna genomes, respectively. Based on our observations, R. lutea may have a tetraploid genome or higher. Our results revealed fundamental cytogenetic information for R. lutea and R. pentagyna, which should be used in future taxonomic studies and whole-genome sequencing. Full article

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same. Full article

DNA bio-computing is an emerging trend in modern science that is based on interactions among biomolecules. Special types of DNAs are aptamers that are capable of selectively forming complexes with target compounds. This review is devoted to a discussion of logic gates based on aptamers for the purposes of medicine and analytical chemistry. The review considers different approaches to the creation of logic gates and identifies the general algorithms of their creation, as well as describes the methods of obtaining an output signal which can be divided into optical and electrochemical. Aptameric logic gates based on DNA origami and DNA nanorobots are also shown. The information presented in this article can be useful when creating new logic gates using existing aptamers and aptamers that will be selected in the future. Full article

Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications. Full article

Plant genomes provide information on biosynthetic pathways involved in the production of industrially relevant compounds. Genome size estimates are essential for the initiation of genome projects. The genome size of rooibos (Aspalathus linearis species complex) was estimated using DAPI flow cytometry and k-mer analyses. For flow cytometry, a suitable nuclei isolation buffer, plant tissue and a transport medium for rooibos ecotype samples collected from distant locations were identified. When using radicles from commercial rooibos seedlings, Woody Plant Buffer and Vicia faba as an internal standard, the flow cytometry-estimated genome size of rooibos was 1.24 ± 0.01 Gbp. The estimates for eight wild rooibos growth types did not deviate significantly from this value. K-mer analysis was performed using Illumina paired-end sequencing data from one commercial rooibos genotype. For biocomputational estimation of the genome size, four k-mer analysis methods were investigated: A standard formula and three popular programs (BBNorm, GenomeScope, and FindGSE). GenomeScope estimates were strongly affected by parameter settings, specifically CovMax. When using the complete k-mer frequency histogram (up to 9 × 10⁵), the programs did not deviate significantly, estimating an average rooibos genome size of 1.03 ± 0.04 Gbp. Differences between the flow cytometry and biocomputational estimates are discussed. Full article

Nitrotyrosine is a product of tyrosine nitration mediated by reactive nitrogen species. As an indicator of cell damage and inflammation, protein nitrotyrosine serves to reveal biological change associated with various diseases or oxidative stress. Accurate identification of nitrotyrosine site provides the important foundation for further elucidating the mechanism of protein nitrotyrosination. However, experimental identification of nitrotyrosine sites through traditional methods are laborious and expensive. In silico prediction of nitrotyrosine sites based on protein sequence information are thus highly desired. Here, we report a novel predictor, NTyroSite, for accurate prediction of nitrotyrosine sites using sequence evolutionary information. The generated features were optimized using a Wilcoxon-rank sum test. A random forest classifier was then trained using these features to build the predictor. The final NTyroSite predictor achieved an area under a receiver operating characteristics curve (AUC) score of 0.904 in a 10-fold cross-validation test. It also significantly outperformed other existing implementations in an independent test. Meanwhile, for a better understanding of our prediction model, the predominant rules and informative features were extracted from the NTyroSite model to explain the prediction results. We expect that the NTyroSite predictor may serve as a useful computational resource for high-throughput nitrotyrosine site prediction. The online interface of the software is publicly available at https://biocomputer.bio.cuhk.edu.hk/NTyroSite/. Full article