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 Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models

2014 ◽  
Vol 6 (4) ◽  
pp. 289-309 ◽  
Author(s):  
Denise E. Kirschner ◽  
C. Anthony Hunt ◽  
Simeone Marino ◽  
Mohammad Fallahi-Sichani ◽  
Jennifer J. Linderman
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Multi Scale

Computational models for multi-scale coupled dynamic problems

2004 ◽  
Vol 20 (3) ◽  
pp. 453-464 ◽  
Author(s):  
R.V.N. Melnik ◽  
A.H. Roberts
Keyword(s):  
Computational Models ◽  
Dynamic Problems ◽  
Multi Scale

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.

scholarly journals Treatment resistant depression: A multi-scale, systems biology approach

2018 ◽  
Vol 84 ◽  
pp. 272-288 ◽  
Author(s):  
Huda Akil ◽  
Joshua Gordon ◽  
Rene Hen ◽  
Jonathan Javitch ◽  
Helen Mayberg ◽  
...  
Keyword(s):  
Systems Biology ◽  
Treatment Resistant Depression ◽  
Treatment Resistant ◽  
Multi Scale ◽  
Resistant Depression

scholarly journals Multi-scale dynamic mean field model (MDMF) relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

2021 ◽  
pp. 1-55
Author(s):  
Amit Naskar ◽  
Anirudh Vattikonda ◽  
Gustavo Deco ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Resting State ◽  
Computational Models ◽  
Field Model ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Mean Field ◽  
Clustering Coefficient ◽  
Brain Dynamics ◽  
Mean Field Model ◽  
Multi Scale

Abstract Previous computational models have related spontaneous resting-state brain activity with local excitatory−inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E-I balance governs resting state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions relate to functional brain activity is of critical importance. We propose a multi-scale dynamic mean field model (MDMF) – a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modelled as population of MDMF and are connected by realistic connection topologies estimated from Diffusion Tensor Imaging data. First, MDMF successfully predicts resting-state functionalconnectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from MDMF model.

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 Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

2016 ◽  
Vol 44 (9) ◽  
pp. 2611-2625 ◽  
Author(s):  
Ghassan S. Kassab ◽  
Gary An ◽  
Edward A. Sander ◽  
Michael I. Miga ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Multi Scale ◽  
Scale Modeling ◽  
Multi Scale Modeling ◽  
Multidisciplinary Perspective

scholarly journals Multi-scale modelling to synergise Plant Systems Biology and Crop Science

2017 ◽  
Vol 202 ◽  
pp. 77-83 ◽  
Author(s):  
Yin Hoon Chew ◽  
Daniel D. Seaton ◽  
Andrew J. Millar
Keyword(s):  
Systems Biology ◽  
Multi Scale ◽  
Crop Science ◽  
Scale Modelling ◽  
Plant Systems Biology

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.

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