A holistic approach for integration of biological systems and usage in drug discovery

Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

Systems biology–the transformative approach to integrate sciences across disciplines

Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

Chromophoric Dendrimer-Based Materials: An Overview of Holistic-Integrated Molecular Systems for Fluorescence Resonance Energy Transfer (FRET) Phenomenon

Dendrimers (from the Greek dendros → tree; meros → part) are macromolecules with well-defined three-dimensional and tree-like structures. Remarkably, this hyperbranched architecture is one of the most ubiquitous, prolific, and recognizable natural patterns observed in nature. The rational design and the synthesis of highly functionalized architectures have been motivated by the need to mimic synthetic and natural-light-induced energy processes. Dendrimers offer an attractive material scaffold to generate innovative, technological, and functional materials because they provide a high amount of peripherally functional groups and void nanoreservoirs. Therefore, dendrimers emerge as excellent candidates since they can play a highly relevant role as unimolecular reactors at the nanoscale, acting as versatile and sophisticated entities. In particular, they can play a key role in the properties of light-energy harvesting and non-radiative energy transfer, allowing them to function as a whole unit. Remarkably, it is possible to promote the occurrence of the FRET phenomenon to concentrate the absorbed energy in photoactive centers. Finally, we think an in-depth understanding of this mechanism allows for diverse and prolific technological applications, such as imaging, biomedical therapy, and the conversion and storage of light energy, among others.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Robust adaptation of PKCζ-IRS1 insulin signaling pathways through integral feedback control

Insulin signaling pathways in muscle tissue play a major role in maintaining glucose homeostasis. Dysregulation in these pathways results in the onset of serious metabolic disorders like type 2 diabetes. Robustness is an essential characteristic of insulin signaling pathways that ensures reliable signal transduction in the presence of perturbations as a result of several feedback mechanisms. Integral control, according to control engineering, provides reliable setpoint tracking and disturbance rejection. The presence of negative feedback and integrating process is crucial for biological processes to achieve integral control. The existence of an integral controller leads to the rejection of perturbations which resulted in the robust regulation of biochemical entities within acceptable levels. In the presentin silicoresearch work, the presence of integral control in the protein kinase Cζ- insulin receptor substrate-1 (PKCζ-IRS1) pathway is identified, verified mathematically and model is simulated in Cell Designer. The data is exported to Minitab software and robustness analysis is carried out statistically using the Mann-Whitney test. The p-value of the results obtained with given parameters perturbed by ±1% is greater than the significance level of 0.05 (0.2132 for 1% error in k7(rate constant of IRS1 phosphorylation), 0.2096 for -1% error in k7, 0.9037 for both ±1% error in insulin and 0.9037 for ±1% error in k1(association rate constant of the first molecule of insulin to bind the insulin receptor), indicated that our hypothesis is proved The results satisfactorily indicate that even when perturbations are present, glucose homeostasis in muscle tissue is robust due to the presence of integral regulation in the PKCζ-IRS1 insulin signaling pathways. In this paper, we have analysed the findings from the framework of robust control theory, which has allowed us to examine that how PKCζ-IRS1 insulin signaling pathways produces desired output in presence of perturbations.

Systems biology of seeds: decoding the secret of biochemical seed factories for nutritional security

Seeds serve as biochemical factories of nutrition, processing, bio-energy and storage related important bio-molecules and act as a delivery system to transmit the genetic information to the next generation. The research pertaining towards delineating the complex system of regulation of genes and pathways related to seed biology and nutrient partitioning is still under infancy. To understand these, it is important to know the genes and pathway(s) involved in the homeostasis of bio-molecules. In recent past with the advent and advancement of modern tools of genomics and genetic engineering, multi-layered 'omics' approaches and high-throughput platforms are being used to discern the genes and proteins involved in various metabolic, and signaling pathways and their regulations for understanding the molecular genetics of biosynthesis and homeostasis of bio-molecules. This can be possible by exploring systems biology approaches via the integration of omics data for understanding the intricacy of seed development and nutrient partitioning. These information can be exploited for the improvement of biologically important chemicals for large-scale production of nutrients and nutraceuticals through pathway engineering and biotechnology. This review article thus describes different omics tools and other branches that are merged to build the most attractive area of research towards establishing the seeds as biochemical factories for human health and nutrition.