Minimal Path Enumeration to Predict Gene
Knockout Effects in Microbial Communities
Steven
1School
1
Pastor ,
Yemin
1
Lan ,
Gail L
2
Rosen
of Biomedical Engineering, Science, and Health Systems, Drexel University, 3141 Chestnut St Philadelphia, PA 19104, USA
2Department
of Electrical and Computer Engineering, Drexel University, 3141 Chestnut St Philadelphia, PA 19104, USA
Introduction
Supraorganism Topological Model
In complex microbial communities, it is a challenge to trace activities
performed by certain organisms.
Applied the path finding pipeline to the metabolic interactions of 3
fungi: 1. Trichoderma reesei (Tr), 2. Phanerochaete
chrysosporium (Pc) 3. Rhodotorula graminis (Rg).
To address this, we simulate effects of gene knockouts in a
community using a supraorganism topological model.
140
Trichoderma reesei Focused Model (Wild Type)
127
T. reesei individual
biosynthesis
of Trehalose
Boolean Satisfiability
Over the years, SAT solvers have been used for many applications
and are renown for their speed in solving complex formulas.
Consumed/produced by Tr
Trehalose
59
3
1
3
2
3
0
4
5
6
7
8
9
10
11
12
Gene Knockout Effects on Minimal Paths in Trehalose Biosynthesis
10
Pc and Rg
8
Minimal Paths
7
Procedure
Trichoderma reesei Focused Model, Knockout Example - 2.3.1.Condition
Encode CNF
T. reesei individual
biosynthesis
of Trehalose
Legend
Not consumed by Tr
Consumed by Tr & produced by:
Tr+Pc+Rg, Tr+Pc, Tr+Rg, Tr
Pc and Rg
http://en.wikipedia.org/wiki/Boolean_satisfiability_problem
SAT Solving
63
Condition Number
Consumed/produced by Tr
Condition Number Total Solutions Minimal Solutions
Tr Wild Type
1
3
3
Supraorganism Wild Type
2
127
7
KO: 2.3.1.-
3
3
3
KO: 3.2.1.28 (Tr)
4
63
4
KO: lpqX
5
127
7
KO: 3.1.3.12
6
123
4
KO: 3.6.3.-
7
123
4
KO: 2.3.1.122
8
120
6
KO: 2.4.1.15 (Tr)
9
126
6
KO: 2.3.1.- & 3.2.1.28 (Tr)
10
0
0
KO: lpqX & 3.2.1.28 (Tr)
11
63
4
KO: 3.1.3.12 & 3.2.1.28 (Tr)
12
59
4
Trehalose
Color-Coded Rows
Correspond to
Model Color-Codes
and Denote Knockout
Conditions
7
6
6
5
4
3
Minimal Paths
120
35
For this reason, and the short-comings exhibited by other metabolic
models, it is of interest to encode a complex metabolic network into
a propositional formula to determine the minimal routes from a
source metabolite to target metabolite.
Automatically encode the
metabolic pathway into
boolean logical formula1,2
126
63
Consumed by Tr & produced by:
To determine satisfiable solutions, one must encode their system of
interest into a boolean propositional logic formula.
KEGG, MetaCyc, Unipathway:
Substrates
Products
Enzymes
123
Not consumed by Tr
Tr+Pc+Rg, Tr+Pc, Tr+Rg, Tr
Databases
123
70
0
Legend
127
105
Total Paths
In the presence of the other 2 organisms, trehalose production is
both complemented and augmented, expanding metabolism to 7
minimal paths.
Previous iterations of this technique only addressed single
organism/single pathway systems.
SAT solvers automatically calculate the satisfiability of a
propositional logic formula3.
Gene Knockout Effects on Total Paths in Trehalose Biosynthesis
By itself, TR synthesizes trehalose via 3 minimal paths.
Using this model and aided by a boolean satisfiability (SAT) solver,
we can enumerate minimal paths through metabolic networks.
Our model allows for insights into how a knockout perturbs
metabolic networks within a community.
Community-Level Knockout Effects
0
3
4
4
4
4
3
0
1
2
3
4
5
6
7
8
Condition Number
9
10
11
12
Conclusions
Expanded upon single organism methods to create an automated
pipeline that enumerates the total and minimal paths in microbial
community metabolism.
This multi-organism approach serves as a hypothesis-generating
tool for more granular experimental microbial ecological analyses.
Applying the minimization process allowed for pinpointing of
essential genes required for key metabolic functions.
Future iterations of this process will extend the minimization via
stoichiometric coefficient matrices.
- - - - - Denotes a Double Knockout
Automated process to
find shortest route
from source metabolite(s) to
target metabolite(s).
FInds Total Paths:
Source Metabolite(s)
Target Metabolite(s)
References
1. Soh T, et al. Evaluation of the prediction of gene knockout effects by minimal pathway enumeration.
International Journal On Advances in Life Sciences, 2012. 4,3 and 4,154-165.
2. Peres S, Morterol M, and Simon L. SAT-Based Metabolics Pathways Analysis Without Compilation.
12th Conference on Computational Methods in Systems Biology (CMSB'14), 2014. 20-31.
3. Ohrimenko O, et al. Propagation = Lazy Clause Generation, Principles and Practice of Constraint
Programming. Lecture Notes in Computer Science, 2007. 4741, pp. 544–558