Omics Studies and Systems Biology Perspective towards Abiotic Stress Response in Plants

Plant abiotic stress responses are vital yield-restricting aspect in agriculture. Recent technology in plant biology allows research of such stress responses at a molecular scale in plants. Network analysis provides in-depth knowledge regarding omics information visualisation as it reduces the intrinsic intricacy of such data. The use of integrated functional genomics helps to understand the relationship between the genomic profile and the phenotypic profile in different environmental conditions of an organism. Plants’ responses to abiotic stress are often considered as a complex process. Systems biology approaches allow visualising and understanding how plant life works to overcome abiotic stress. The combination of integrated functional genomics along with bioinformatics will put a hand in additional in-depth research knowledge on stress tolerance to plants by exploiting available genetic information and continuously improving techniques and strategies. Most of the omics technologies are high throughput with very rapid data generation rates and humongous outputs. These technologies have made noticeable contributions to the modern-day improvements in our knowledge of plant biology. So, in this review, omics studies and the system biology approach towards abiotic stress tolerance in plants are highlighted.

How Do Precision Medicine and System Biology Response to Human Body's Complex Adaptability？

In the field of life sciences, although system biology and ＂precision medicine＂ introduce some complex scientific methods and techniques, it is still based on the ＂analysis–reconstruction＂ of reductionist theory as a whole. Adaptability of complex system increase system behaviour uncertainty as well as the difficulties of precise identification and control. It also put systems biology research into trouble. To grasp the behaviour and characteristics of organism fundamentally, systems biology has to abandon the ＂analysis–reconstruction＂ concept. In accordance with the guidelines of complexity science, systems biology should build organism model from holistic level, just like the Chinese medicine did in dealing with human body and disease. When we study the living body from the holistic level, we will find the adaptability of complex system is not the obstacle that increases the difficulty of problem solving. It is the ＂exceptional＂, ＂right-hand man＂ that helping us to deal with the complexity of life more effectively.

Network biology:A promising approach for drug target identification against neurodevelopmental disorders

Biological entities are involved in complicated and complex connections;hence,discovering biological information using network biology ideas is critical.In the past few years,network biology has emerged as an integrative and systems-level approach for understanding and interpreting these complex interactions.Biological network analysis is one method for reducing enormous data sets to clinically useful knowledge for disease diagnosis,prognosis,and treatment.The network of biological entities can help us predict drug targets for several diseases.The drug targets identified through the systems biology approach help in targeting the essential biological pathways that contribute to the progression and development of the disease.The novel strategical approach of system biologyassisted pharmacology coupled with computer-aided drug discovery(CADD)can help drugs fight multifactorial diseases efficiently.In the present review,we have summarized the role and application of network biology for not only unfolding the mechanism of complex neurodevelopmental disorders but also identifying important drug targets for diseases like ADHD,Autism,Epilepsy,and Intellectual Disability.Systems biology has emerged as a promising approach to identifying drug targets and aiming for targeted drug discovery for the precise treatment of neurodevelopmental disorders.

Cellular and subcellular coherent dynamics,biological functional properties,and system-environment interaction

In this view point paper,we briefly summarize some of the clinical,biochemical and biophysical results obtained in our research on Relaxation Response.We also qualitatively describe the theoretical biophysical model that could link them.Our work points to a unified view of the human biological system activity,joining the dynamics ruling the interactions and correlations of the microscopic components to the knowledge of their specific individual properties in the effort of going beyond a purely atomistic approach.

A Patient with End-stage Respiratory Failure Gets Recovery from Mechanical Ventilation by Utilization of Chinese Traditional Medicine

Ventilator-assisted ventilation and lung transplantation are the final treatments for the patients with end-stage respiratory failure.A patient,who has been diagnosed as end-stage respiratory failure and received tracheal intubation with mechanical ventilation,received the treatment of Chinese herbal medicine on the principle of TCM and got some recovery gradually.After two months,the patient got rid of mechanical ventilation successfully.This case implies that TCM will be an alternative treatment for the patients with the similar conditions.

The systemic hallmarks of cancer

Cancer is not just a lump of cells that divide,invade,and spread randomly,but rather a multi-layered precisely tuned process that requires the participation of the whole organism.There is an urgent need to zoom-out from the cellular and the local stromal view and broaden our perspective by including the whole organism level.Geographically separated cancer tissues communicate between themselves,forming a system that interacts with the rest of the organism through cancer induced systemic pathogenic networks.In the present paper,I introduce six systemic hallmarks of cancer that emerge as a result of these interactions.I also describe several potential therapeutic approaches that can be developed using the cancer system concept.Overall,I argue that the tumoricentric paradigm should be replaced with a broader approach that brings into focus the“cancerized”organism.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts,as these stress states can lead to suboptimal growth and lower yields of target products.To enable the design of genetic circuits for reporting or mitigating the stress states,it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria.Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria,motivating computational methods that can identify robust biomarkers for experimental characterisation and verification.Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis,this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments.We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model,gene regulatory network and co-expression network.We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification(mean f1-score achieved at 0.99),as well as in a range of independent datasets(mean f1-score achieved at 0.98).We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B.subtilis gene regulatory network(GRN)and the number of associated modules in a B.subtilis co-expression network(CEN).GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes.We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

Pichia as yeast cell factory for production of industrially important bio-products:Current trends,challenges,and future prospects

Yeast has been used as a cell factory for thousands of years to produce a wide variety of com-plex biofuels,bioproducts,biochemicals,food ingredients,and pharmaceuticals.For a variety of biotechnological production hosts,a few specific genera of yeast have proven themselves.Rapid developments in metabolic engineering and synthetic biology provide a workable long-term supply solution for these substances.In this review,we have covered recent advances in the design of yeast cell factories for the synthesis of terpenoids,alkaloids,phenylpropanoids,and other natural chemicals,primarily focusing on Pichia species.Cutting-edge solutions involving genetic and process engineering have also been discussed.Overall,the review summarized recent advancements and challenges in synthetic and systems biology,as well as initiatives in metabolic engineering aimed at commercializing non-conventional yeasts like Pichia.The processes used in non-traditional yeasts to produce enzymes,therapeutic proteins,lipids,and metabolic products for industrial applications were thoroughly elaborated.

Strategies for Revitalization of Traditional Medicine

Traditional medicine (TM) plays an inevitable role in drug discovery and development. Most of the therapeutically useful molecules used in the present day are inspired from TM. Herbal drugs are the oldest forms of medicines used for the treatment of various ailments and the TM of every country has a long history of their usage. To develop more data on their quality, safety, and efficacy, so also to improve the consumer’s need of modern days several thrust areas of research are to be focused on the development of TM. Based on the above concept, a paradigm shift is required for the revitalization of TM. These facts along with the modern scientific approaches, molecular tools, and strategies make it necessary for TM to be revitalized. Confluencing several strategies with the technological and scientific developments including pharmacogenomics, nutrigenomics, system biology, and related approaches, the scientific potential of TM can be explored further with international coordination and collaborations.

Deciphering Ascorbic Acid Regulatory Pathways in Ripening Tomato Fruit Using a Weighted Gene Correlation Network Analysis Approach

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid （AsA）, which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato （Solanum lycopersicum L.） fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty- four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.

Stochastic Simulation of the Cell Cycle Model for Budding Yeast

We use the recently proposed Nested Stochastic Simulation Algorithm(Nested SSA)to simulate the cell cycle model for budding yeast.The results show that Nested SSA is able to significantly reduce the computational cost while capturing the essential dynamical features of the system.