| Title |
Multiscale Analysis of Biological Systems
|
|---|---|
| Published in |
Acta Biotheoretica, January 2013
|
| DOI | 10.1007/s10441-013-9170-z |
| Pubmed ID | |
| Authors |
Annick Lesne |
| Abstract |
It is argued that multiscale approaches are necessary for an explanatory modeling of biological systems. A first step, besides common to the multiscale modeling of physical and living systems, is a bottom-up integration based on the notions of effective parameters and minimal models. Top-down effects can be accounted for in terms of effective constraints and inputs. Biological systems are essentially characterized by an entanglement of bottom-up and top-down influences following from their evolutionary history. A self-consistent multiscale scheme is proposed to capture the ensuing circular causality. Its differences with standard mean-field self-consistent equations and slow-fast decompositions are discussed. As such, this scheme offers a way to unravel the multilevel architecture of living systems and their regulation. Two examples, genome functions and biofilms, are detailed. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 3% |
| Italy | 1 | 3% |
| Unknown | 27 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 31% |
| Researcher | 8 | 28% |
| Student > Doctoral Student | 2 | 7% |
| Other | 2 | 7% |
| Professor | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 17% |
| Engineering | 5 | 17% |
| Philosophy | 3 | 10% |
| Computer Science | 3 | 10% |
| Business, Management and Accounting | 1 | 3% |
| Other | 8 | 28% |
| Unknown | 4 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 January 2015.
All research outputs
#19,942,887
of 25,371,288 outputs
Outputs from Acta Biotheoretica
#157
of 213 outputs
Outputs of similar age
#223,633
of 292,728 outputs
Outputs of similar age from Acta Biotheoretica
#3
of 4 outputs
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