Back

A graph-based framework for multi-scale modeling of physiological transport

Maheshvare, D.; Raha, S.; Pal, D.

2021-09-16 systems biology
10.1101/2021.09.14.460337 bioRxiv
Show abstract

Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches where it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective-dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro - in vivo studies and vascular topology from imaging studies for examining the structure-function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/460337v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1b872b4org.highwire.dtl.DTLVardef@72b98borg.highwire.dtl.DTLVardef@1f35f29org.highwire.dtl.DTLVardef@ecba45_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS Computational Biology
1863 papers in training set
Top 0.6%
26.6%
2
npj Systems Biology and Applications
125 papers in training set
Top 0.2%
7.9%
3
Scientific Reports
3612 papers in training set
Top 11%
6.7%
4
Bulletin of Mathematical Biology
92 papers in training set
Top 0.3%
4.9%
5
Biophysical Journal
631 papers in training set
Top 2%
4.9%
50% of probability mass above
6
Journal of Theoretical Biology
162 papers in training set
Top 0.8%
3.5%
7
PLOS ONE
5266 papers in training set
Top 38%
3.2%
8
iScience
1154 papers in training set
Top 6%
3.2%
9
Bioinformatics
1204 papers in training set
Top 5%
3.2%
10
Interface Focus
14 papers in training set
Top 0.1%
2.6%
11
eLife
5828 papers in training set
Top 40%
2.4%
12
Integrative Biology
14 papers in training set
Top 0.1%
2.1%
13
Computers in Biology and Medicine
128 papers in training set
Top 2%
1.7%
14
BMC Bioinformatics
457 papers in training set
Top 4%
1.7%
15
Frontiers in Physiology
106 papers in training set
Top 2%
1.3%
16
Science Advances
1243 papers in training set
Top 25%
1.1%
17
PNAS Nexus
159 papers in training set
Top 2%
1.1%
18
Fluids and Barriers of the CNS
28 papers in training set
Top 0.4%
1.0%
19
Frontiers in Immunology
638 papers in training set
Top 8%
1.0%
20
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 38%
1.0%
21
Computational and Structural Biotechnology Journal
242 papers in training set
Top 6%
1.0%
22
Physical Review E
112 papers in training set
Top 1%
0.9%
23
Journal of The Royal Society Interface
235 papers in training set
Top 4%
0.8%
24
Communications Biology
993 papers in training set
Top 35%
0.6%