6. Simulators

Systems Biology: A Very Short Introduction ◽

10.1093/actrade/9780198828372.003.0006 ◽

2020 ◽

pp. 79-107

Author(s):

Eberhard O. Voit

Keyword(s):

Metabolic Engineering ◽

Systems Biology ◽

Organic Compounds ◽

Computational Models ◽

Computational Systems Biology ◽

Bulk Materials ◽

Growth Simulation ◽

Practical Applications ◽

Crop Development ◽

Computational Systems

Computational models can serve many purposes, but a particularly powerful application of a model is its use as a system simulator. An emerging branch of computational systems biology strives to develop simulators for complex systems in biology and medicine, the premier example being a disease simulator. ‘Simulators’ discusses the nascent efforts towards the development of simulators for practical applications. Disease simulators will deepen our understanding of the physiology of human diseases and their treatment. Simulators in metabolic engineering have the goal of improving the microbial production of bulk materials and of valuable organic compounds. Relatively simple simulators for the production of biofuels and for crop development, such as the Soybean Growth Simulation Model (SoySim), are already in use.

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Standards and ontologies in computational systems biology

Essays in Biochemistry ◽

10.1042/bse0450211 ◽

2008 ◽

Vol 45 ◽

pp. 211-222 ◽

Cited By ~ 21

Author(s):

Herbert M. Sauro ◽

Frank T. Bergmann

Keyword(s):

Systems Biology ◽

Computational Models ◽

Software Tools ◽

Computational Systems Biology ◽

Modelling Languages ◽

Systems Biology Markup Language ◽

Visualization Tools ◽

Computational Systems ◽

Model Interchange ◽

Model Annotation

With the growing importance of computational models in systems biology there has been much interest in recent years to develop standard model interchange languages that permit biologists to easily exchange models between different software tools. In the present chapter two chief model exchange standards, SBML (Systems Biology Markup Language) and CellML are described. In addition, other related features including visual layout initiatives, ontologies and best practices for model annotation are discussed. Software tools such as developer libraries and basic editing tools are also introduced, together with a discussion on the future of modelling languages and visualization tools in systems biology.

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Faculty Opinions recommendation of Computational systems biology and in silico modeling of the human microbiome.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717697961.793153071 ◽

2012 ◽

Author(s):

Nicola Mulder

Keyword(s):

Systems Biology ◽

In Silico ◽

Human Microbiome ◽

Computational Systems Biology ◽

In Silico Modeling ◽

Computational Systems

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Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Cancers ◽

10.3390/cancers13010035 ◽

2020 ◽

Vol 13 (1) ◽

pp. 35

Author(s):

Sahar Aghakhani ◽

Naouel Zerrouk ◽

Anna Niarakis

Keyword(s):

Rheumatoid Arthritis ◽

Extracellular Matrix ◽

Systems Biology ◽

Healing Process ◽

Metabolic Reprogramming ◽

Critical Event ◽

Wound Healing Process ◽

Computational Systems Biology ◽

Structural Framework ◽

Computational Systems

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

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