@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix owl: <http://www.w3.org/2002/07/owl#> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix xml: <http://www.w3.org/XML/1998/namespace> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . @prefix dc: <http://purl.org/dc/elements/1.1/> . @prefix : <http://denigma.org/resource/> . :Systems_Biology rdfs:subClassOf :Biology ; :aims_to_develop :Hypothesis ; :based_on :Integrated_Data, :Modelled_Data ; :in_the :Future ; :definition "A research approach that seeks to describe the overall behavior of a biological system through detailed, quantitative experimentation combined with conceptual or computational modeling of the systems components and their interactions", "The study of biological function that derives from interactions" . :Top-Down_Modeling rdfs:subClassOf :Modeling ; :description "Top-down modeling is scale independent; one can utilize this approach for the whole organisms (which may be a single cell), tissue, cell or even an organelle." . :Top_Down_Approach rdfs:subClassOf :Approach ; :description """Top-down approach is the act of generating large-scale bias-free data, which is often "used for hypothesis generation under complex situations"""" . :Relationship_Extraction :subClassOf :Extraction . :Semantic_Systems_Biology rdfs:subClassOf :Systems_Biology ; :initialism "SSB" ; :description "Semantic Systems Biology (SSB) is a system biology approach that uses semantic description of knowledge about biological systems to facilitate data analysis." . :Semantic_Systems_Biology_Approach rdfs:subClassOf :Approach ; :that_combines :Semantic_Technology, :Systems_Biology ; :description """There are four key elements in Semantic Systems Biology approach: 1. Knowledge represenation 2. Data integration 3. Reasoning => hypthesis 4, Querying => hypothesis """ . :Scientific_Biomedicine_Communication :is_by_and_large_still :Text ; :subClassOf :Communication . :Scientific_Communication :in :Biomedicine ; :subClassOf :Communication . :Automated_Extraction :can_be_directly_used_for :Systems_Biology_Modelling ; :of :Useful_Biomedical_Information ; :of_useful :Biomedical_Unstructured_Text_Information ; :subClassOf :Extraction . :Biomedical_Unstructured_Text_Information :subClassOf :Information . :Biomedicine :is_by_and_large_still :Text . :Fundamental_Approach :subClassOf :Approach . :Further_Interdisciplinary_Development :of :Systems_Biology-Orientated_Ontology, :Training_Corpora ; :subClassOf :Development . :Importance :of :Named_Entity_Recognition, :Relationship_Extraction . :Named_Entity_Recognition :is_relevant_to :Systems_Biology ; :as :Fundamental_Approach . :Relationship_Extraction :is_relevant_to :Systems_Biology ; :as :Fundamental_Approach . :Entity_Recognition :subClassOf :Recognition . :Named_Entity_Recognition rdfs:subClassOf :Entity_Recognition . :Publicly_Organized_Scientific_Benchmarking_Challenge :are_important_in_moving_forward_the :Entire_Field ; :subClassOf :Challenge ; :that_reflect_the_current_status_of :Text-Mining_Technology . :Relationship_Extraction :subClassOf :Extraction . :Scientific_Benchmarking_Challenge :subClassOf :Challenge ; :that_reflect_the_current :Status; :are_important_in_moving :Entire_Field ; :of :Text-Mining_Technology . :Systems_Biology-Orientated_Ontology :subClassOf :Ontology . :Training_Corpus rdfs:subClassOf :Text_Corpus ; :for :Training . :Unstructured_Text :subClassOf :Text ; :is :unstructured . :Useful_Biomedical_Information :can_be_directly_used_for :Systems_Biology_Modelling ; :from :Unstructured_Text ; :subClassOf :Information . :Systems_Biology_Modelling :have_been_substantially_improved_over_the :Past_Few_Year ; :subClassOf :Modelling . :Text_Mining_Technology :for :Automated_Extraction ; :subClassOf :Technology . :Text-Mining_Technology :are_important_in_moving :Entire_Field ; :are_important_in_moving_the :Entire_Field ; :on :Systems_Biology ; :subClassOf :Technology . #:We :emphasize_the_role_of :Publicly_Organized_Scientific_Benchmarking_Challenge ; # :expect_a_steadily_increasing_impact_of :Text-Mining_Technology ; # :publicly_organized :Scientific_Benchmarking_Challenge ; # :underline :Importance . :Entire_Field :given :Further_Interdisciplinary_Development ; :subClassOf :Field . # Resources: :django-sysbio a :Django_Project ; :definition "systems biology web tools developed in django" ; :website <http://code.google.com/p/django-sysbio/> ; :for :Systems_Biology . ## Some papers on Systems Biology of aging: :21115531 a :Review_Article ; :full_text_link <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001307/> . :23633915 a :Review_Article ; dc:title "Systems biology in aging: linking the old and the young" . # http://pcwww.liv.ac.uk/~aging/research.html :Semantic_Systems_Biology_Portal a :Portal ; :is_about :Semantic_Systems_Biology ; :website <http://www.semantic-systems-biology.org/> .
Systems biology is an approach in which experimental biology is closely integrated with mathematical or computational modelling in a synergistic way to answer biological questions that would not be possible by empirical approaches alone. One of the goals of systems biology is to discover new emergent properties that may arise from studying the system as a whole, leading to more rapid and deeper understanding of how the system is controlled and how it responds to external stimuli. This level of understanding will greatly facilitate the future of biological systems.
Systems biology aims to develop hypotheses based on integrated or modelled data. A systems biology project may be performed at a number of different biological scales or it may integrate across scales. The systems biology cycle is composed of modelling and experimentation. Models should be both descriptive and predictive [Antezana+Al:2013]. Systems biology provides an understanding of biology from a system perspective, moving from components and interactions into groups or sets of components that are associated with a function and how these subcellular functions give rise to cell- and tissue-level functions.
There has been a vast amount of knowledge that was gained. This knowledge allows now to gain a perspective where one can start at the level of genes, and go to the level of the cell, tissue and organismal level and understand how the information in genes is decoded to form proteins and how proteins interact with the subsets of lipids and sugars and so on. All of these together give rise to cellular tissue and organismal function. It is this kind of integrated study that is called systems biology.
The word system itself is an amorphous term. It can mean systems at various levels of biological organization. Some might have a system at the level of a cell or the level of a tissue or an organ or the level of the whole organisms, or going back to the other end, it may be subcellular levels, either mitochondria or the nucleus can also be called systems. So there is not one fixed definition of what systems biology will be and different people can have different perspectives of how the field is growing and focused on.
Focusing largely on systems biology at the cellular level, and mostly mammalian cells, provides a natural entry into how cells become tissues and organs, and allow systems approaches to the study in medicine, pharmacology, and therapeutics. Systems biology requires a sense of how quantitative reasoning can be used to deal with large datasets and an understanding of what kind of mathematical representation is appropriate for different kinds of biological questions and systems. Mathematical analysis can provide a deep understanding of how behaviours occur and emerge, and how one can get predictive value from computational analysis.