scholarly journals Systems biology of angiogenesis signaling: Computational models and omics

2021 ◽  
Author(s):  
Yu Zhang ◽  
Hanwen Wang ◽  
Rebeca Hannah M. Oliveira ◽  
Chen Zhao ◽  
Aleksander S. Popel
Keyword(s):  
Systems Biology ◽  
Computational Models

Systems biology markup language: Level 2 and beyond

2003 ◽  
Vol 31 (6) ◽  
pp. 1472-1473 ◽  
Author(s):  
A. Finney ◽  
M. Hucka
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Chemical Species ◽  
Biochemical Networks ◽  
Markup Language ◽  
Exchange Format ◽  
Systems Biology Markup Language ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.

Agent-Based Modelling in Multicellular Systems Biology

2020 ◽  
pp. 369-389
Author(s):  
Sara Montagna ◽  
Andrea Omicini
Keyword(s):  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Systems Biology ◽  
Computational Models ◽  
The Self ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Multi Agent

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.

scholarly journals The glutaredoxin mono- and di-thiol mechanisms for deglutathionylation are functionally equivalent: implications for redox systems biology

2015 ◽  
Vol 35 (1) ◽  
Author(s):  
Lefentse N. Mashamaite ◽  
Johann M. Rohwer ◽  
Ché S. Pillay
Keyword(s):  
Systems Biology ◽  
Active Site ◽  
Redox Regulation ◽  
Computational Models ◽  
Kinetic Modelling ◽  
Side Reaction ◽  
Kinetic Properties ◽  
Computational Systems Biology ◽  
Redox Systems

Glutathionylation plays a central role in cellular redox regulation and anti-oxidative defence. Grx (Glutaredoxins) are primarily responsible for reversing glutathionylation and their activity therefore affects a range of cellular processes, making them prime candidates for computational systems biology studies. However, two distinct kinetic mechanisms involving either one (monothiol) or both (dithiol) active-site cysteines have been proposed for their deglutathionylation activity and initial studies predicted that computational models based on either of these mechanisms will have different structural and kinetic properties. Further, a number of other discrepancies including the relative activity of active-site mutants and contrasting reciprocal plot kinetics have also been reported for these redoxins. Using kinetic modelling, we show that the dithiol and monothiol mechanisms are identical and, we were also able to explain much of the discrepant data found within the literature on Grx activity and kinetics. Moreover, our results have revealed how an apparently futile side-reaction in the monothiol mechanism may play a significant role in regulating Grx activity in vivo.

scholarly journals Erratum: Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models

2014 ◽  
Vol 6 (4) ◽  
pp. 344-344
Author(s):  
Denise E. Kirschner ◽  
C. Anthony Hunt ◽  
Simeone Marino ◽  
Mohammad Fallahi-Sichani ◽  
Jennifer J. Linderman
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Multi Scale

scholarly journals The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Michael Hucka ◽  
Frank T. Bergmann ◽  
Andreas Dräger ◽  
Stefan Hoops ◽  
Sarah M. Keating ◽  
...  
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Software Systems ◽  
Markup Language ◽  
Starting Point ◽  
Extensible Markup ◽  
Systems Biology Markup Language ◽  
Level 3 ◽  
Translation Errors ◽  
Language Specification

AbstractComputational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.

scholarly journals A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures

2017 ◽  
Author(s):  
Brian T. Hawkins ◽  
Sonia Grego
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Three Dimensional ◽  
Toxicity Testing ◽  
Biological Data ◽  
Physiological Relevance ◽  
Toxicity Risk ◽  
Therapeutic Development ◽  
Single Cell Type

Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way—for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, “systems biology” approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using computational tools that are sophisticated enough to handle the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, three-dimensional, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment. This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field.

scholarly journals Advances in the integration of transcriptional regulatory information into genome-scale metabolic models

2016 ◽  
Author(s):  
R.P. Vivek-Ananth ◽  
Areejit Samal
Keyword(s):  
Systems Biology ◽  
Metabolic Networks ◽  
Computational Models ◽  
Cellular Metabolism ◽  
Transcriptional Regulatory ◽  
Metabolic Systems ◽  
Regulatory Information ◽  
Domains Of Life ◽  
Genome Scale ◽  
Constraint Based Models

AbstractA major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

scholarly journals Mathematical and Computational Modeling in Complex Biological Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhiwei Ji ◽  
Ke Yan ◽  
Wenyang Li ◽  
Haigen Hu ◽  
Xiaoliang Zhu
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Cancer Progression ◽  
High Throughput ◽  
Computational Models ◽  
Molecular Mechanisms ◽  
Biological Process ◽  
Biological Systems ◽  
Complex Diseases ◽  
Modeling Approaches

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

6. Simulators

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.

scholarly journals Multiscale computational models in physical systems biology of intracellular trafficking

2014 ◽  
Vol 8 (5) ◽  
pp. 198-213 ◽  
Author(s):  
Richard W. Tourdot ◽  
Ryan P. Bradley ◽  
Natesan Ramakrishnan ◽  
Ravi Radhakrishnan
Keyword(s):  
Systems Biology ◽  
Intracellular Trafficking ◽  
Computational Models ◽  
Physical Systems
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