Analysis of the Evolution and Structure of Systems Biology

2012 ◽  
Vol 488-489 ◽  
pp. 1006-1010
Author(s):  
Chao Liu ◽  
Lian Fen Liu ◽  
Fu Guo Li ◽  
Nai Hua Jiang ◽  
Wen Juan Guo ◽  
...  
Keyword(s):  
Systems Biology ◽  
Molecular Interactions ◽  
Close Association ◽  
Physiological Responses ◽  
Biological Molecules ◽  
System Level ◽  
Biological Research ◽  
Research Practice ◽  
Research Model ◽  
Fundamental Challenge

Systems biology is a term used to describe a number of trends in bioscience research, and a movement which draws on those trends. Systems biology aims to understand the biology from the system level. The fundamental challenge of systems biology is to establish a complete, detailed description of the link between biological molecules and to study molecular interactions and the close association between the physiological responses. Systems biology methods in the system under the guidance will enable us to break the shackles of the old research model to study life from the grasp of the whole phenomenon. We must effectively grasp and follow the systems biology approach to guide our biological research practice.

INDICATIONS OF PHYSIOLOGICAL AND PATHOPHYSIOLOGICAL RELEVANCE OF NOISE AND CHAOS

2004 ◽  
Vol 04 (01) ◽  
pp. L207-L217 ◽  
Author(s):  
HANS A. BRAUN ◽  
KARL VOIGT ◽  
J. CHRISTIAN KRIEG ◽  
MARTIN T. HUBER
Keyword(s):  
Systems Biology ◽  
Essential Role ◽  
Life Sciences ◽  
Biological Research ◽  
Sensory Receptors ◽  
Noise Effects ◽  
Physiological Relevance ◽  
Physically Based ◽  
Modelling Studies ◽  
Peripheral Sensory

In recent years biophysical approaches have had particular impact on the progress in physiological and biological research. In systems biology such progress is often associated with the terms "noise" and "chaos". The introduction of these physically based concepts into life sciences has essentially been promoted by the work of Frank Moss and his group. This paper provides evidence of the physiological relevance of such biophysically based approaches with examples from quite different physiological and pathophysiological functions like temperature transduction in peripheral sensory receptors and the progression of mood disorders. We will use modelling studies, based on experimental and clinical data, to illustrate that both systems can attain specific dynamical states where chaos and/or noise plays an essential role and we will try to describe under which conditions functionally relevant noise effects or chaotic behaviour can be expected.

Early Days of Biochemical NMR

1997 ◽  
Author(s):  
R.G. Shulman
Keyword(s):  
Magnetic Resonance ◽  
Biological Systems ◽  
Biological Molecules ◽  
Biological Research ◽  
Solid State Physics ◽  
Arts And Sciences ◽  
Birthday Celebration ◽  
Common Interests ◽  
The 1960S ◽  
The Individual

It was my pleasure to participate in Oleg’s 65th birthday celebration and to reminisce about the early days of Biochemical NMR. Oleg was always there. I remember in the summer in the early 1960s sitting on lawn chairs at a Gordon Conference and discussing the need for a meeting on biochemical NMR. This was to convene those with common interests, and out of this grew the 1964 meeting in Boston, which was the first International Conference on Magnetic Resonance in Biological Systems. In organizing the 1964 meeting Oleg was stalwart, in charge of the local arrangements at the old mansion, home of the American Academy of Arts and Sciences. The venue was much appreciated by the more than 100 attendees, and the smooth arrangements and elegant, although somewhat dowdy locale, contributed to the sense, generated by the meeting, that the field had a coherent scientific core and a meaningful future. In the early days of the 1960s the field of magnetic resonance in biological systems, brought together biannually by the society, had a coherence that was nurtured by the society. In those days the NMR and ESR methods were much less developed than they soon became, so that any reasonably competent spectroscopist could understand all the methods employed. Additionally, because the earlier studies concentrated upon the better understood biological molecules or processes, the breadth of the applications did not baffle a slightly informed biochemist. The rapid advances in definite understanding were thrilling to practitioners in the field, and individual efforts were motivated by a sense that the field was going to grow. By that time NMR was firmly established as a quantitative method in chemistry, solid state physics, and other material sciences so that with the results in hand it was logical to extrapolate to a future in which magnetic resonance could be central to biological research. These high hopes, however, required considerable confidence in extrapolation, because the individual findings were sometimes slight when compared to the exciting cutting edges of biological research.

scholarly journals Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
System Level ◽  
Expression Data ◽  
Differential Analysis ◽  
Computational Systems Biology ◽  
Microarray Gene Expression ◽  
High Throughput Data ◽  
Computational Systems

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

scholarly journals The Promises and the Challenges of Integrating Multi-Omics and Systems Biology in Comparative Stress Biology

2020 ◽  
Vol 60 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Tonia S Schwartz
Keyword(s):  
Systems Biology ◽  
Physiological Responses ◽  
Model Organisms ◽  
Omics Data ◽  
Holistic Understanding ◽  
Stress Biology

Abstract Comparative stress biology is inherently a systems biology approach with the goal of integrating the molecular, cellular, and physiological responses with fitness outcomes. In this way, the systems biology approach is expected to provide a holistic understanding of how different stressors result in different fitness outcomes, and how different individuals (or populations or species) respond to stressors differently. In this perceptive article, I focus on the use of multiple types of -omics data in stress biology. Targeting students and those researchers who are considering integrating -omics approaches in their comparative stress biology studies, I discuss the promise of the integration of these measures for furthering our holistic understanding of how organisms respond to different stressors. I also discuss the logistical and conceptual challenges encountered when working with -omics data and the current hurdles to fully utilize these data in studies of stress biology in non-model organisms.

scholarly journals Deciphering the regulation mechanism in biochemical networks by a systems-biology approach

2017 ◽  
Author(s):  
Bernardo A. Mello ◽  
Yuhai Tu
Keyword(s):  
Systems Biology ◽  
Molecular Mechanisms ◽  
Biochemical Networks ◽  
System Level ◽  
Regulation Mechanism ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Complex Processes ◽  
Regulation Mechanisms

To decipher molecular mechanisms in biological systems from system-level input-output data is challenging especially for complex processes that involve interactions among multiple components. Here, we study regulation of the multi-domain (P1-5) histidine kinase CheA by the MCP chemoreceptors. We develop a network model to describe dynamics of the system treating the receptor complex with CheW and P3P4P5 domains of CheA as a regulated enzyme with two substrates, P1 and ATP. The model enables us to search the hypothesis space systematically for the simplest possible regulation mechanism consistent with the available data. Our analysis reveals a novel dual regulation mechanism wherein besides regulating ATP binding the receptor activity has to regulate one other key reaction, either P1 binding or phosphotransfer between P1 and ATP. Furthermore, our study shows that the receptors only control kinetic rates of the enzyme without changing its equilibrium properties. Predictions are made for future experiments to distinguish the remaining two dual-regulation mechanisms. This systems-biology approach of combining modeling and a large input-output data-set should be applicable for studying other complex biological processes.

scholarly journals Exosomal miRNAs as New Players of Cancers: A Mini-review Study

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Roya Ashoory ◽  
Homa Mollaei
Keyword(s):  
Physiological Responses ◽  
The Body ◽  
Biological Molecules ◽  
Cancer Cell Proliferation ◽  
Diagnostic Biomarkers ◽  
Current Review ◽  
Exosomal Mirnas ◽  
Islamic Science ◽  
Review Study ◽  
Intercellular Communications

Context: Exosomes are nano-extracellular vesicles that carry biological molecules, including DNA, RNAs, and proteins, throughout the body. They can modulate intercellular communications and play key roles in signaling pathways and physiological responses. In the current review, we focused on exosomal miRNAs' roles in cancer development. Evidence Acquisition: This research was garnered from a large number of reports published in PubMed, ScienceDirect, Springer, Islamic Science Citation, and Magiran databases from 2000 to 2021. The keywords used were exosome, cancer, and microRNA. Results: The roles of exosomes in disease development and cancer have been investigated in several studies. It is revealed that exosome components such as microRNAs contribute to cancer-related processes, including cancer cell proliferation, metastasis, and angiogenesis. Conclusions: The findings showed that we could propose exosomes as cancer therapeutic targets and diagnostic biomarkers.

scholarly journals Systems biology informed deep learning for inferring parameters and hidden dynamics

2020 ◽  
Vol 16 (11) ◽  
pp. e1007575 ◽  
Author(s):  
Alireza Yazdani ◽  
Lu Lu ◽  
Maziar Raissi ◽  
George Em Karniadakis
Keyword(s):  
Deep Learning ◽  
Systems Biology ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Learning Algorithm ◽  
Optimization Procedure ◽  
System Level ◽  
Benchmark Problems ◽  
Model Parameters ◽  
Unknown Parameters

Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.

scholarly journals A Customizable Analysis Flow in Integrative Multi-Omics

Biomolecules ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1606
Author(s):  
Samuel M. Lancaster ◽  
Akshay Sanghi ◽  
Si Wu ◽  
Michael P. Snyder
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Systems Biology ◽  
Nucleic Acid ◽  
Best Practices ◽  
Data Integration ◽  
Study Design ◽  
Molecular Interactions ◽  
Analysis Workflow ◽  
Integration Techniques

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

scholarly journals SABIO-RK: A data warehouse for biochemical reactions and their kinetics

2007 ◽  
Vol 4 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Olga Krebs ◽  
Martin Golebiewski ◽  
Renate Kania ◽  
Saqib Mir ◽  
Jasmin Saric ◽  
...  
Keyword(s):  
Systems Biology ◽  
Kinetic Data ◽  
Biochemical Networks ◽  
System Level ◽  
Kinetic Properties ◽  
Biological Processes ◽  
Biochemical Reactions ◽  
Biochemical Pathways ◽  
Application Potential ◽  
Promising Application

Abstract Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.

scholarly journals Biomolecular electrostatics and solvation: a computational perspective

2012 ◽  
Vol 45 (4) ◽  
pp. 427-491 ◽  
Author(s):  
Pengyu Ren ◽  
Jaehun Chun ◽  
Dennis G. Thomas ◽  
Michael J. Schnieders ◽  
Marcelo Marucho ◽  
...  
Keyword(s):  
Molecular Interactions ◽  
Electrostatic Interactions ◽  
Membrane Lipids ◽  
Biological Molecules ◽  
Computational Aspect ◽  
Special Importance ◽  
Computational Biophysics ◽  
Aqueous Ions ◽  
Solvation Models ◽  
Insight Into

AbstractAn understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

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