Evidence map›Paper›PMID 18516226›Full record

ReviewPLoS computational biology2008

Why are computational neuroscience and systems biology so separate?

Erik De Schutter

Abstract readReview
In one paragraph

Review in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed.

  1. Review
  2. Article
  3. Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025
    Article
  4. Review
  5. Article
  6. Article
  7. Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022
    Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Computational Models for Calcium-Mediated Astrocyte Functions.Frontiers in computational neuroscience · 2018
    Review
  17. Article
  18. Article
  19. Article
  20. Is realistic neuronal modeling realistic?Journal of neurophysiology · 2016
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Erik De SchutterComputational Neuroscience Unit, Okinawa Institute of Science and Technology, Japan. erik@oist.jp

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future.

Indexed as

Models, NeurologicalComputer SimulationNeurosciencesSystems Biology

Identifiers

PMID18516226
PMCPMC2367448

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.