Pattern-oriented Agent-based Monte Carlo simulation of Cellular Redox Environment
Bibliographic record
Abstract
Research suggests that cellular redox environment could affect the phenotype and function of cells through a complex reaction network[1]. In cells, redox status is mainly regulated by several redox couples, such as Glutathione/glutathione disulfide (GSH/GSSG), Cysteine/ Cystine (CYS/CYSS) and mitochondrial redox couples. Evidence suggests that both intracellular and extracellular redox can affect overall cell redox state. How redox is communicated between extracellular and intracellular environments is still a matter of debate. Some researchers conclude based on experimental data, that there is a connection between extracellular and intracellular redox [2], whereas others oppose this view [3]. In general however, these experiments lack insight into the dynamics, complex network of reactions and transportation through cell membrane of redox. Therefore, current experimental results reveal but a snapshot, or average of true dynamics. What is more, it can be more complex if the dynamics of redox in different intracellular compartments is included [4]. Furthermore, heterogeneous spatial and temporal distribution of reactants and enzymes, diffusion rate and import direction of chemical source [5] could be very important factors. In our project, an agent-based Monte Carlo modeling [6] is offered to study the dynamic relationship between extracellular and intracellular redox and complex networks of redox reactions. In the model, pivotal redox-related reactions will be included, and the reactants will be the agents [7]. Additionally, the spatial distribution of enzymes and reactants, and diffusion of reactants will be considered as a contributing factor. To initially simplify the modeling, the redox change of intracellular compartments will be ignored or only the export and import of redox will be modeled. Because complex networks and dynamics of redox still is not completely understood , results of existing experiments will be used to validate the modeling according to ideas in pattern-oriented agent-based modeling[8]. The simulation of this model is computational intensive, thus an application 'FLAME' that can be run in parallel with MPI on computer cluster, will be used to implement modeling [9]. In the future, studies will be performed simulating how cellular redox state could affect phenotype of a population of cells, and hereby the tissue and organ if dynamics between intracellular and extracellular redox is well understand. Reference: 1. Moriarty-Craige, S.E. and D.P. Jones, Extracellular thiols and thiol/disulfide redox in metabolism. Annu Rev Nutr, 2004. 24: p. 481-509. 2. Banerjee, R., Redox outside the box: linking extracellular redox remodeling with intracellular redox metabolism. J Biol Chem, 2012. 287(7): p. 4397-402. 3. Anderson, C.L., et al., Control of extracellular cysteine/cystine redox state by HT-29 cells is independent of cellular glutathione. Am J Physiol Regul Integr Comp Physiol, 2007. 293(3): p. R1069-75. 4. Go, Y.M. and D.P. Jones, Redox compartmentalization in eukaryotic cells. Biochimica Et Biophysica Acta-General Subjects, 2008. 1780(11): p. 1271-1290. 5. Jones, D.P., Redox sensing: orthogonal control in cell cycle and apoptosis signalling. J Intern Med, 2010. 268(5): p. 432-48. 6. Pogson, M., et al., Formal agent-based modelling of intracellular chemical interactions. Biosystems, 2006. 85(1): p. 37-45. 7. Stern, J.R., et al., Integration of TGF-beta- and EGFR-based signaling pathways using an agent-based model of epithelial restitution. Wound Repair Regen, 2012. 20(6): p. 862-71. 8. Grimm, V., et al., Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science, 2005. 310(5750): p. 987-91. 9. Kiran, M., et al., FLAME: simulating large populations of agents on parallel hardware architectures, in Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 12010, International Foundation for Autonomous Agents and Multiagent Systems: Toronto, Canada. p. 1633-1636.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".