如何从其他格式导出 sbml 文件,例如 graphml?
How to export sbml file from other formats, such as graphml?
因此,如前所述,我正在尝试找到一种将 graphml 文件(或其他格式,例如 xgmml、csv、edgelist)从 networkx 或 igraph(python 或 R)转换为此文件的方法SBML格式。
我相信应该有一种简单的方法,但是...我找不到。有什么想法吗?
编辑:有一些 other fomats 可以用来最终登陆 SBML 星球,但我仍然不知道如何导出到它们中的任何一个。
编辑 II:here 我发布了一个与 SBML 和 Cytoscape 相关的问题,所以...可能对其他对该主题感兴趣的人有用。
SBML 主要是编码过程或基于反应的模型。此类模型对应的网络图是二分图,即图中有两个 类 个节点(反应和物种),只有物种和反应节点之间的边,但物种-物种或物种之间没有边-反应。
SBML 中的一个重要概念是反应中的化学计量,它基本上是定义物质如何在相应反应中发生的边缘属性。
因此,对于可转换为 SBML 的图,它们必须遵循一定的结构,即它们必须是二分有向图,其中反应和物种节点在边上具有化学计量信息。
可以使用例如 libsbml 或 JSBML(这两个库都用于处理 SBML)轻松将此类图形转换为 SBML。
我在下面附上了一个示例,其中包含来自 libsbml 的 python 绑定。
"""
Converts simple bipartite species-reaction graph to SBML
using python bindings in libsbml
requirements:
pip install python-libsbml networkx
"""
import networkx as nx
import libsbml
'''
Create bipartite networkx graph consisting of species and reaction nodes.
Edges require stoichiometry (or set to 1 otherwise).
'''
G = nx.DiGraph()
# add species nodes
G.add_node("S1", ntype="specie")
G.add_node("S2", ntype="specie")
G.add_node("S3", ntype="specie")
G.add_node("S4", ntype="specie")
G.add_node("S5", ntype="specie")
G.add_node("S6", ntype="specie")
# add reaction nodes (and reaction edges)
G.add_node("r1", ntype="reaction") # 2 S1 -> S2
G.add_edges_from([
("S1", "r1", {'stoichiometry': 2}),
("r1", "S2", {'stoichiometry': 1})])
G.add_node("r2", ntype="reaction") # S2 -> S3
G.add_edges_from([
("S2", "r2", {'stoichiometry': 1}),
("r2", "S3", {'stoichiometry': 1})])
G.add_node("r3", ntype="reaction") # S3 + S4 -> S5 + S6
G.add_edges_from([
("S3", "r3", {'stoichiometry': 1}),
("S4", "r3", {'stoichiometry': 1}),
("r3", "S5", {'stoichiometry': 1}),
("r3", "S6", {'stoichiometry': 1})
])
print(G)
for sid, n in G.nodes.items():
print(sid, n)
for sid, e in G.edges.items():
print(sid, e)
'''
Create SBML model from the graph
'''
doc = libsbml.SBMLDocument() # type: libsbml.SBMLDocument
model = doc.createModel() # type: libsbml.Model
model.setId("graph_model")
# create species
for sid, n in G.nodes.items():
print(sid, n)
if n['ntype'] == "specie":
s = model.createSpecies() # type: libsbml.Species
s.setId(sid)
# create reactions
for sid, n in G.nodes.items():
if n['ntype'] == "reaction":
r = model.createReaction() # type: libsbml.Reaction
r.setId(sid)
for reactant_id in G.predecessors(sid):
stoichiometry = G.edges[reactant_id, sid]['stoichiometry']
reactant = model.getSpecies(reactant_id)
r.addReactant(reactant, stoichiometry)
for product_id in G.successors(sid):
product = model.getSpecies(product_id)
stoichiometry = G.edges[sid, product_id]['stoichiometry']
r.addProduct(product, stoichiometry)
# serialization
sbml_str = libsbml.writeSBMLToString(doc)
print("-" * 80)
print(sbml_str)
libsbml.writeSBMLToFile(doc, "graph2sbml.xml")
与输出
S1 {'ntype': 'specie'}
S2 {'ntype': 'specie'}
S3 {'ntype': 'specie'}
S4 {'ntype': 'specie'}
S5 {'ntype': 'specie'}
S6 {'ntype': 'specie'}
r1 {'ntype': 'reaction'}
r2 {'ntype': 'reaction'}
r3 {'ntype': 'reaction'}
('S1', 'r1') {'stoichiometry': 2}
('S2', 'r2') {'stoichiometry': 1}
('S3', 'r3') {'stoichiometry': 1}
('S4', 'r3') {'stoichiometry': 1}
('r1', 'S2') {'stoichiometry': 1}
('r2', 'S3') {'stoichiometry': 1}
('r3', 'S5') {'stoichiometry': 1}
('r3', 'S6') {'stoichiometry': 1}
S1 {'ntype': 'specie'}
S2 {'ntype': 'specie'}
S3 {'ntype': 'specie'}
S4 {'ntype': 'specie'}
S5 {'ntype': 'specie'}
S6 {'ntype': 'specie'}
r1 {'ntype': 'reaction'}
r2 {'ntype': 'reaction'}
r3 {'ntype': 'reaction'}
--------------------------------------------------------------------------------
<?xml version="1.0" encoding="UTF-8"?>
<sbml xmlns="http://www.sbml.org/sbml/level3/version2/core" level="3" version="2">
<model id="graph_model">
<listOfSpecies>
<species id="S1"/>
<species id="S2"/>
<species id="S3"/>
<species id="S4"/>
<species id="S5"/>
<species id="S6"/>
</listOfSpecies>
<listOfReactions>
<reaction id="r1">
<listOfReactants>
<speciesReference species="S1" stoichiometry="2" constant="true"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="S2" stoichiometry="1" constant="true"/>
</listOfProducts>
</reaction>
<reaction id="r2">
<listOfReactants>
<speciesReference species="S2" stoichiometry="1" constant="true"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="S3" stoichiometry="1" constant="true"/>
</listOfProducts>
</reaction>
<reaction id="r3">
<listOfReactants>
<speciesReference species="S3" stoichiometry="1" constant="true"/>
<speciesReference species="S4" stoichiometry="1" constant="true"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="S5" stoichiometry="1" constant="true"/>
<speciesReference species="S6" stoichiometry="1" constant="true"/>
</listOfProducts>
</reaction>
</listOfReactions>
</model>
</sbml>
然后可以使用 Cytoscape 中的 cy3sbml 等工具可视化 SBML
因此,如前所述,我正在尝试找到一种将 graphml 文件(或其他格式,例如 xgmml、csv、edgelist)从 networkx 或 igraph(python 或 R)转换为此文件的方法SBML格式。
我相信应该有一种简单的方法,但是...我找不到。有什么想法吗?
编辑:有一些 other fomats 可以用来最终登陆 SBML 星球,但我仍然不知道如何导出到它们中的任何一个。
编辑 II:here 我发布了一个与 SBML 和 Cytoscape 相关的问题,所以...可能对其他对该主题感兴趣的人有用。
SBML 主要是编码过程或基于反应的模型。此类模型对应的网络图是二分图,即图中有两个 类 个节点(反应和物种),只有物种和反应节点之间的边,但物种-物种或物种之间没有边-反应。 SBML 中的一个重要概念是反应中的化学计量,它基本上是定义物质如何在相应反应中发生的边缘属性。
因此,对于可转换为 SBML 的图,它们必须遵循一定的结构,即它们必须是二分有向图,其中反应和物种节点在边上具有化学计量信息。
可以使用例如 libsbml 或 JSBML(这两个库都用于处理 SBML)轻松将此类图形转换为 SBML。 我在下面附上了一个示例,其中包含来自 libsbml 的 python 绑定。
"""
Converts simple bipartite species-reaction graph to SBML
using python bindings in libsbml
requirements:
pip install python-libsbml networkx
"""
import networkx as nx
import libsbml
'''
Create bipartite networkx graph consisting of species and reaction nodes.
Edges require stoichiometry (or set to 1 otherwise).
'''
G = nx.DiGraph()
# add species nodes
G.add_node("S1", ntype="specie")
G.add_node("S2", ntype="specie")
G.add_node("S3", ntype="specie")
G.add_node("S4", ntype="specie")
G.add_node("S5", ntype="specie")
G.add_node("S6", ntype="specie")
# add reaction nodes (and reaction edges)
G.add_node("r1", ntype="reaction") # 2 S1 -> S2
G.add_edges_from([
("S1", "r1", {'stoichiometry': 2}),
("r1", "S2", {'stoichiometry': 1})])
G.add_node("r2", ntype="reaction") # S2 -> S3
G.add_edges_from([
("S2", "r2", {'stoichiometry': 1}),
("r2", "S3", {'stoichiometry': 1})])
G.add_node("r3", ntype="reaction") # S3 + S4 -> S5 + S6
G.add_edges_from([
("S3", "r3", {'stoichiometry': 1}),
("S4", "r3", {'stoichiometry': 1}),
("r3", "S5", {'stoichiometry': 1}),
("r3", "S6", {'stoichiometry': 1})
])
print(G)
for sid, n in G.nodes.items():
print(sid, n)
for sid, e in G.edges.items():
print(sid, e)
'''
Create SBML model from the graph
'''
doc = libsbml.SBMLDocument() # type: libsbml.SBMLDocument
model = doc.createModel() # type: libsbml.Model
model.setId("graph_model")
# create species
for sid, n in G.nodes.items():
print(sid, n)
if n['ntype'] == "specie":
s = model.createSpecies() # type: libsbml.Species
s.setId(sid)
# create reactions
for sid, n in G.nodes.items():
if n['ntype'] == "reaction":
r = model.createReaction() # type: libsbml.Reaction
r.setId(sid)
for reactant_id in G.predecessors(sid):
stoichiometry = G.edges[reactant_id, sid]['stoichiometry']
reactant = model.getSpecies(reactant_id)
r.addReactant(reactant, stoichiometry)
for product_id in G.successors(sid):
product = model.getSpecies(product_id)
stoichiometry = G.edges[sid, product_id]['stoichiometry']
r.addProduct(product, stoichiometry)
# serialization
sbml_str = libsbml.writeSBMLToString(doc)
print("-" * 80)
print(sbml_str)
libsbml.writeSBMLToFile(doc, "graph2sbml.xml")
与输出
S1 {'ntype': 'specie'}
S2 {'ntype': 'specie'}
S3 {'ntype': 'specie'}
S4 {'ntype': 'specie'}
S5 {'ntype': 'specie'}
S6 {'ntype': 'specie'}
r1 {'ntype': 'reaction'}
r2 {'ntype': 'reaction'}
r3 {'ntype': 'reaction'}
('S1', 'r1') {'stoichiometry': 2}
('S2', 'r2') {'stoichiometry': 1}
('S3', 'r3') {'stoichiometry': 1}
('S4', 'r3') {'stoichiometry': 1}
('r1', 'S2') {'stoichiometry': 1}
('r2', 'S3') {'stoichiometry': 1}
('r3', 'S5') {'stoichiometry': 1}
('r3', 'S6') {'stoichiometry': 1}
S1 {'ntype': 'specie'}
S2 {'ntype': 'specie'}
S3 {'ntype': 'specie'}
S4 {'ntype': 'specie'}
S5 {'ntype': 'specie'}
S6 {'ntype': 'specie'}
r1 {'ntype': 'reaction'}
r2 {'ntype': 'reaction'}
r3 {'ntype': 'reaction'}
--------------------------------------------------------------------------------
<?xml version="1.0" encoding="UTF-8"?>
<sbml xmlns="http://www.sbml.org/sbml/level3/version2/core" level="3" version="2">
<model id="graph_model">
<listOfSpecies>
<species id="S1"/>
<species id="S2"/>
<species id="S3"/>
<species id="S4"/>
<species id="S5"/>
<species id="S6"/>
</listOfSpecies>
<listOfReactions>
<reaction id="r1">
<listOfReactants>
<speciesReference species="S1" stoichiometry="2" constant="true"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="S2" stoichiometry="1" constant="true"/>
</listOfProducts>
</reaction>
<reaction id="r2">
<listOfReactants>
<speciesReference species="S2" stoichiometry="1" constant="true"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="S3" stoichiometry="1" constant="true"/>
</listOfProducts>
</reaction>
<reaction id="r3">
<listOfReactants>
<speciesReference species="S3" stoichiometry="1" constant="true"/>
<speciesReference species="S4" stoichiometry="1" constant="true"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="S5" stoichiometry="1" constant="true"/>
<speciesReference species="S6" stoichiometry="1" constant="true"/>
</listOfProducts>
</reaction>
</listOfReactions>
</model>
</sbml>
然后可以使用 Cytoscape 中的 cy3sbml 等工具可视化 SBML