Advances in Basic Theory Research of Mongolian Medicine and Prospects of Its Combination with Systems Biology Research

This paper sorted out the relevant literature on the basic theory of Mongolian medicine,explored the research methods and ideas of the basic theory of Mongolian medicine,and elaborated the idea of combining systems biology to study part of the basic theory of Mongolian medicine.Through searching classic works and research papers in academic journals,this paper sorted out and summarized the research progress of the basic theory of Mongolian medicine and the existing problems in the current research,analyzed its characteristics,combined with systems biology methods to systematically explain some content in the basic theory,reveal its scientific connotation,and provide a basis for further research.

Model informed precision medicine of Chinese herbal medicines formulas-A multi-scale mechanistic intelligent model

Recent trends suggest that Chinese herbal medicine formulas(CHM formulas)are promising treatments for complex diseases.To characterize the precise syndromes,precise diseases and precise targets of the precise targets between complex diseases and CHM formulas,we developed an artificial intelligence-based quantitative predictive algorithm(DeepTCM).DeepTCM has gone through multilevel model calibration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling,molecular and theoretical levels of traditional Chinese medicine(TCM).As an example,our model simulated the optimal CHM formulas for the treatment of coronary heart disease(CHD)with depression,and through model sensitivity analysis,we calculated the balanced scoring of the formulas.Furthermore,we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions.Finally,we experimentally confirmed the therapeutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice.This novel multiscale model opened up a new avenue to combine“disease syndrome”and“macro micro”system modeling to facilitate translational research in CHM formulas.

Neural plasticity and adult neurogenesis: the deep biology perspective

The recognition that neurogenesis does not stop with adolescence has spun off research towards the reduction of brain disorders by enhancing brain regeneration. Adult neurogenesis is one of the tougher problems of developmental biology as it requires the generation of complex intracellular and pericellular anatomies, amidst the danger of neuroinflammation. We here review how a multitude of regulatory pathways optimized for early neurogenesis has to be revamped into a new choreography of time dependencies. Distinct pathways need to be regulated, ranging from neural growth factor induced differentiation to mitochondrial bioenergetics, reactive oxygen metabolism, and apoptosis. Requiring much Gibbs energy consumption, brain depends on aerobic energy metabolism, hence on mitochondrial activity. Mitochondrial fission and fusion, movement and perhaps even mitoptosis, thereby come into play. All these network processes are interlinked and involve a plethora of molecules. We recommend a deep thinking approach to adult neurobiology.

Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.

Comparative systems biology between human and animal models based on next-generation sequencing methods

Animal models provide myriad benefits to both experimental and clinical research.Unfortunately,in many situations,they fall short of expected results or provide contradictory results.In part,this can be the result of traditional molecular biological approaches that are relatively inefficient in elucidating underlying molecular mechanism.To improve the efficacy of animal models,a technological breakthrough is required.The growing availability and application of the high-throughput methods make systematic comparisons between human and animal models easier to perform.In the present study,we introduce the concept of the comparative systems biology,which we define as“comparisons of biological systems in different states or species used to achieve an integrated understanding of life forms with all their characteristic complexity of interactions at multiple levels”.Furthermore,we discuss the applications of RNA-seq and ChIP-seq technologies to comparative systems biology between human and animal models and assess the potential applications for this approach in the future studies.

Comparative systems biology between human and animal models based on next-generation sequencing methods

Animal models provide myriad benefits to both experimental and clinical research. Unfortunately, in many situations, they fall short of expected results or provide contradictory results. In part, this can be the result of traditional molecular biological approaches that are relatively inefficient in elucidating underlying molecular mechanism. To improve the efficacy of animal models, a technological breakthrough is required. The growing availability and application of the high-throughput methods make systematic comparisons between human and animal models easier to perform. In the present study, we introduce the concept of the comparative systems biology, which we define as "comparisons of biological systems in different states or species used to achieve an integrated understanding of life forms with all their characteristic complexity of interactions at multiple levels". Furthermore, we discuss the applications of RNA-seq and ChIP-seq technologies to comparative systems biology between human and animal models and assess the potential applications for this approach in the future studies.

Developmental systems biology flourishing on new technologies

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems Biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

In-Silico Analysis &In-Vivo Results Concur on Glutathione Depletion in Glyphosate Resistant GMO Soy, Advancing a Systems Biology Framework for Safety Assessment of GMOs

This study advances previous efforts towards development of computational systems biology, in silico, methods for biosafety assessment of genetically modified organisms (GMOs). C1 metabolism is a critical molecular system in plants, fungi, and bacteria. In our previous research, critical molecular systems of C1 metabolism were identified and modeled using CytoSolve?, a platform for in silico analysis. In addition, multiple exogenous molecular systems affecting C1 metabolism such as oxidative stress, shikimic acid metabolism, glutathione biosynthesis, etc. were identified. Subsequent research expanded the C1 metabolism computational models to integrate oxidative stress, suggesting glutathione (GSH) depletion. Recent integration of data from the EPSPS genetic modification of Soy, also known as Roundup Ready Soy (RRS), with C1 metabolism predicts similar GSH depletion and HCHO accumulation in RRS. The research herein incorporates molecular systems of glutathione biosynthesis and glyphosate catabolism to expand the extant in silico models of C1 metabolism. The in silico results predict that Organic Soy will have a nearly 250% greater ratio of GSH and GSSG, a measure of glutathione levels, than in RRS that are glyphosate-treated glyphosate-resistant Soy versus the Organic Soy. These predictions also concur with in vivo greenhouse results. This concurrence suggests that these in silico models of C1 metabolism may provide a viable and validated platform for biosafety assessment of GMOs, and aid in selecting rational criteria for informing in vitro and in vivo efforts to more accurately decide in the problem formulation phase whose parameters need to be assessed so that conclusion on “substantial equivalence” or material difference of a GMO and its non-GMO counterpart can be drawn on a well-grounded basis.

An Effective Numerical Calculation Method for Multi-Time-Scale Mathematical Models in Systems Biology

The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In Systems Biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.

Stress Knowledge Map:A knowledge graph resource for systems biology analysis of plant stress responses

Stress Knowledge Map(SKM;https://skm.nib.si)is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical,signaling,and regulatory molecular interactions in plants:a highly curated model of plant stress signaling(PSS;543 reactions)and a large comprehensive knowledge network(488390 interactions).Both were constructed by domain experts through systematic curation of diverse literature and database resources.SKM provides a single entry point for investigations of plant stress response and related growth trade-offs,as well as interactive explorations of current knowledge.PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin.Here,we describe the features of SKM and show,through two case studies,how it can be used for complex analyses,including systematic hypothesis generation and design of validation experiments,or to gain new insights into experimental observations in plant biology.

Liver zonation: Novel aspects of its regulation and its impact on homeostasis

Liver zonation, the spatial separation of the immense spectrum of different metabolic pathways along the liver sinusoids, is fundamental for proper functioning of this organ. Recent progress in elucidating localization and interactions of different metabolic pathways by using “omics” techniques and novel approaches to couple them with refined spatial resolution and in characterizing novel master regulators of zonation by using transgenic mice has created the basis for a deeper understanding of core mechanisms of zonation and their impact on liver physiology, pathology and metabolic diseases. This review summarizes the fascinating technical achievements for investigating liver zonation and the elucidation of an emerging network of master regulators of zonation that keep the plethora of interrelated and sometimes opposing functions of the liver in balance with nutritional supply and specific requirements of the entire body. In addition, a brief overview is given on newly described zonated functions and novel details on how diverse the segmentation of metabolic zonation may be. From these facts and developments a few fundamental principles are inferred which seem to rule zonation of liver parenchyma. In addition, we identify important questions that still need to be answered as well as interesting fields of research such as the connection of zonation with circadian rhythm and gender dimorphism which need to be pushed further, in order to improve our understanding of metabolic zonation. Finally, an outlook is given on how disturbance of liver zonation and its regulation may impact on liver pathology and the development of metabolic diseases.

Systems pharmacology for traditional Chinese medicine with application to cardio-cerebrovascular diseases

Identified as a treasure of natural herbal products,traditional Chinese medicine(TCM)has attracted extensive attention for their moderate treatment effect and lower side effect.Cardio-cerebrovascular diseases(CCVD)are a leading cause of death.TCM is used in China to prevent and treat CCVD.However,the complexity of TCM poses challenges in understanding the mechanisms of herbs at a systems-level,thus hampering the modernization and globalization of TCM.A novel model,termed traditional Chinese medicine systems pharmacology(TCMSP)analysis platform,which relies on the theory of systems pharmacology and integrates absorption,distribution,metabolism,excretion and toxicity(ADME/T)evaluation,target prediction and network/pathway analysis,was proposed to address these problems.Here,we review the development of systems pharmacology,the TCMSP approach and its applications in the investigations of CCVD and compare it with other methods.TCMSP assists in uncovering the mechanisms of action of herbal formulas used in treating CCVD.It can also be applied in ascertaining the different syndrome patterns of coronary artery disease,decoding the multi-scale mechanisms of herbs,and in understanding the mechanisms of herbal synergism.

Cytokinome profile evaluation in patients with hepatitis C virus infection

The ‘‘omics sciences’’ (genomics, transcriptomics, proteomics) are often used to study living organisms as a whole system by evaluating the complex expression patterns of genes, miRNA, proteins, and metabolites. This study aimed, through bioinformatics and systems biology, to decipher the cytokinome profile in the evolution of inflammatory processes leading to cancer. The cytokinome was defined as the totality of cytokines and their interactions in and around biological cells. The system biology approach would provide a better understanding of the complex interaction network of cytokines, especially in cancer patients. Acquired knowledge would enable health providers with tools to evaluate disease onset through progression as well as identifying innovative therapeutic strategies. Understanding the role each cytokine plays in the metabolic network is of great importance. This paper reviews our group’s ‘‘omics’’ work. In particular, it addresses the role cytokines play in liver disease in six different scenarios. The first is the role the cytokines play in chronic inflammatory diseases and cancers. The second is the significance of the cytokinome profile. The third is the role of liver cirrhosis as an inflammatory disease. The fourth is the comparison of cytokine levels evaluated in patients with chronic hepatitis C virus (HCV) or with HCV-related cirrhosis. The fifth is the comparison of cytokine levels evaluated in patients with HCV-related cirrhosis in the presence and absence of type 2 diabetes. And lastly, we present a comparison of cytokine levels evaluated in patients with HCV-related cirrhosis in the presence and absence of hepatocellular carcinoma.

Design and construction of microbial cell factories based on systems biology

Environmental sustainability is an increasingly important issue in industry.As an environmentally friendly and sustainable way,constructing microbial cell factories to produce all kinds of valuable products has attracted more and more attention.In the process of constructing microbial cell factories,systems biology plays a crucial role.This review summarizes the recent applications of systems biology in the design and construction of microbial cell factories from four perspectives,including functional genes/enzymes discovery,bottleneck pathways identification,strains tolerance improvement and design and construction of synthetic microbial consortia.Systems biology tools can be employed to identify functional genes/enzymes involved in the biosynthetic pathways of products.These discovered genes are introduced into appropriate chassis strains to build engineering microorganisms capable of producing products.Subsequently,systems biology tools are used to identify bottleneck pathways,improve strains tolerance and guide design and construction of synthetic microbial consortia,resulting in increasing the yield of engineered strains and constructing microbial cell factories successfully.

Systems perspectives on mRNA processing

The application of genomic technologies to the study of mRNA processing is increasingly conducted in metazoan organisms in order to understand the complex events that occur during and after transcription. Large-scale systems analyses ofmRNA-protein interactions and mRNA dynamics have revealed specificity in mRNA transcription, splicing, transport, translation, and turnover, and have begun to make connections between the different layers ofmRNA processing. Here, we review global studies of post-transcriptional processes and discuss the challenges facing our understanding of mRNA regulation in metazoan organisms. In parallel, we examine genome-scale investigations that have expanded our knowledge of RNA-binding proteins and the networks of mRNAs that they regulate.

Exploring Innovation with Scientific Thinking

The mode of scientific thinking is undergoing rapid and profound changes.In the 21st century,macro and micro civilizations go parallel.A systematic and scientific methodology is required for the study of complex things.The thinking mode in modern medicine is gradually shifting from analytical,reductive thinking to holistic and systematic thinking.As such Western medicine and traditional Chinese medicine are gradually approaching the epistemology of health and disease state.The importance of scientific thinking in innovation has been expounded in this study.The development trends in medicine in the current era are analyzed,the importance of systems theory in the study of human bodies is discussed,and a new medical model named Novel Systems Medicine is proposed.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer(CRC)is the most commonly diagnosed fatal cancer in both women and men worldwide.CRC ranked second in mortality and third in incidence in 2020.It is difficult to diagnose CRC at an early stage as there are no clinical symptoms.Despite advances in molecular biology,only a limited number of biomarkers have been translated into routine clinical practice to predict risk,prognosis and response to treatment.In the last decades,systems biology approaches at the omics level have gained importance.Over the years,several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis.On the other hand,a few drugs are being developed and used in clinics for the treatment of CRC.However,the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than$1 billion.Therefore,drug repositioning(DR)could save time and money by establishing new indications for existing drugs.In this review,we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Single-cell systems neuroscience:A growing frontier in mental illness

The development of effective treatments for psychiatric disease has been disappointing in recent decades given the advancements in neuroscience.Moreover,rising rates of mental ilness such as addiction and depression compel scientists and physicians to discover novel and creative solutions.One such approach that has proven effective is systems neuroscience:A focus on networks as opposed to mechanism.Further,investigation at the single-cell and circuit level is likely to be fruitful in such endeavors as this resolution describes the functional psychopathology that allows for intervention.

Eigenvalues of Jacobian Matrices Report on Steps of Metabolic Reprogramming in a Complex Plant-Environment Interaction

Mathematical modeling of biochemical systems aims at improving the knowledge about complex regulatory networks. The experimental high-throughput measurement of levels of biochemical components, like metabolites and proteins, has become an integral part for characterization of biological systems. Yet, strategies of mathematical modeling to functionally integrate resulting data sets is still challenging. In plant biology, regulatory strategies that determine the metabolic output of metabolism as a response to changes in environmental conditions are hardly traceable by intuition. Mathematical modeling has been shown to be a promising approach to address such problems of plant-environment interaction promoting the comprehensive understanding of plant biochemistry and physiology. In this context, we recently published an inversely calculated solution for first-order partial derivatives, i.e. the Jacobian matrix, from experimental high-throughput data of a plant biochemical model system. Here, we present a biomathematical strategy, comprising 1) the inverse calculation of a biochemical Jacobian;2) the characterization of the associated eigenvalues and 3) the interpretation of the results with respect to biochemical regulation. Deriving the real parts of eigenvalues provides information about the stability of solutions of inverse calculations. We found that shifts of the eigenvalue real part distributions occur together with metabolic shifts induced by short-term and long-term exposure to low temperature. This indicates the suitability of mathematical Jacobian characterization for recognizing perturbations in the metabolic homeostasis of plant metabolism. Together with our previously published results on inverse Jacobian calculation this represents a comprehensive strategy of mathematical modeling for the analysis of complex biochemical systems and plant-environment interactions from the molecular to the ecosystems level.

Artificial intelligence for modeling uveal melanoma

Understanding of the cellular signaling pathways involved in cancer disease is of great importance.These complex biological mechanisms can be thoroughly revealed by their structure,dynamics,and control methods.Artificial intelligence offers rule-based models that favor the research of human signaling processes.In this paper,we give an overview of the advantages of the formalism of symbolic models in medical biology and cell biology of the uveal melanoma.A language is described that allows us:(1)To define the system states and elements with their alterations;(2)To model the dynamics of the cellular system;and(3)To perform inference-based analysis with the logical tools of the language.