scholarly journals Computational Methods Enabling Next-Generation Bioprocesses

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 214 ◽  
Author(s):  
Julio R. Banga ◽  
Filippo Menolascina
Keyword(s):  
Synthetic Biology ◽  
Computational Methods ◽  
Next Generation ◽  
Biomolecular Networks

Synthetic biology—the engineering of cells to rewire the biomolecular networks inside them—has witnessed phenomenal progress [...]

Development and Application of Computational Methods in Phage Display Technology

2020 ◽  
Vol 26 (42) ◽  
pp. 7672-7693 ◽  
Author(s):  
Bifang He ◽  
Anthony Mackitz Dzisoo ◽  
Ratmir Derda ◽  
Jian Huang
Keyword(s):  
Phage Display ◽  
Computational Methods ◽  
Peptide Ligands ◽  
Next Generation ◽  
Phage Display Technology ◽  
Next Generation Sequencing Technology ◽  
Screening Experiments ◽  
Display Technology ◽  
Comprehensive Search ◽  
Generation Sequencing

Background: Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display. Methods: We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses. Results: We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data. Conclusion: The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

scholarly journals Spatial localisation meets biomolecular networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Govind Menon ◽  
J. Krishnan
Keyword(s):  
Synthetic Biology ◽  
Pattern Formation ◽  
Circuit Design ◽  
Systematic Approach ◽  
Temporal Networks ◽  
Spatial Organisation ◽  
Biomolecular Networks ◽  
Systems Chemistry ◽  
Spatial Localisation ◽  
Systems Framework

AbstractSpatial organisation through localisation/compartmentalisation of species is a ubiquitous but poorly understood feature of cellular biomolecular networks. Current technologies in systems and synthetic biology (spatial proteomics, imaging, synthetic compartmentalisation) necessitate a systematic approach to elucidating the interplay of networks and spatial organisation. We develop a systems framework towards this end and focus on the effect of spatial localisation of network components revealing its multiple facets: (i) As a key distinct regulator of network behaviour, and an enabler of new network capabilities (ii) As a potent new regulator of pattern formation and self-organisation (iii) As an often hidden factor impacting inference of temporal networks from data (iv) As an engineering tool for rewiring networks and network/circuit design. These insights, transparently arising from the most basic considerations of networks and spatial organisation, have broad relevance in natural and engineered biology and in related areas such as cell-free systems, systems chemistry and bionanotechnology.

scholarly journals SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cheng Yan ◽  
Jianxin Wang ◽  
Wei Lan ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Computational Methods ◽  
Drug Target ◽  
Large Scale ◽  
High Efficiency ◽  
Drug Repositioning ◽  
External Validation ◽  
Complex Diseases ◽  
Biological Databases ◽  
Biomolecular Networks ◽  
New Chemical Entities

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

scholarly journals Metabolic Engineering for Production of Biorenewable Fuels and Chemicals: Contributions of Synthetic Biology

2010 ◽  
Vol 2010 ◽  
pp. 1-18 ◽  
Author(s):  
Laura R. Jarboe ◽  
Xueli Zhang ◽  
Xuan Wang ◽  
Jonathan C. Moore ◽  
K. T. Shanmugam ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Synthetic Biology ◽  
Plant Material ◽  
Microbial Fermentation ◽  
Next Generation ◽  
Fermentation Conditions ◽  
Fermentative Production ◽  
Chemical Inhibitors ◽  
Fuels And Chemicals

Production of fuels and chemicals through microbial fermentation of plant material is a desirable alternative to petrochemical-based production. Fermentative production of biorenewable fuels and chemicals requires the engineering of biocatalysts that can quickly and efficiently convert sugars to target products at a cost that is competitive with existing petrochemical-based processes. It is also important that biocatalysts be robust to extreme fermentation conditions, biomass-derived inhibitors, and their target products. Traditional metabolic engineering has made great advances in this area, but synthetic biology has contributed and will continue to contribute to this field, particularly with next-generation biofuels. This work reviews the use of metabolic engineering and synthetic biology in biocatalyst engineering for biorenewable fuels and chemicals production, such as ethanol, butanol, acetate, lactate, succinate, alanine, and xylitol. We also examine the existing challenges in this area and discuss strategies for improving biocatalyst tolerance to chemical inhibitors.

scholarly journals Editorial: Towards the Next Generation of Deep Brain Stimulation Therapies: Technological Advancements, Computational Methods, and New Targets

2021 ◽  
Vol 15 ◽  
Author(s):  
Sabato Santaniello ◽  
George C. McConnell ◽  
John T. Gale ◽  
Rose T. Faghih ◽  
Caleb Kemere ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Computational Methods ◽  
Brain Stimulation ◽  
Next Generation ◽  
New Targets ◽  
Deep Brain
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