Documentation for NetControl4BioMed

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Pipeline

The pipeline generates a biochemical network basing on the user's query, and searches for a smallest combination of driven nodes via which one should be able to control a user-selcted set of target nodes there.

The pipeline consists of the following three parts:

  1. DATA IMPORT: Integrate the user’s defined input into the pipeline. Either generate the network with Moksiskan, basing on the user’s defined input or get the user-defined network in GRAPHML format.
  2. NETWORK CONTROLLABILITY: Compute the minimal set of driven nodes for the given target genes in the network generated at the previous step.
  3. POSTPROCESSING AND OUTPUT: Highlight those driven nodes that can be targeted by FDA approved drugs. Generate the network file (GRAPHML, Cytoscape and PDF) from the original network and by adding additional annotations to the nodes representing selected driven genes/proteins, drug-targetable driven genes/proteins, if any, and target genes. Generate CSV tables with the information about the driven genes/proteins, and the list of target genes and their control paths from the driven nodes.

Input

The pipeline currently accepts the following inputs from the user:

  1. List of Genes/Proteins to Generate the Network: List of genes/proteins that will be used as seed nodes by Moksiskaan to generate the network. This input can be any gene/protein name from Homo sapiens genome.
  2. Custom network: The user has an option to use a custom network in the pipeline instead of the Moksiskaan network.
  3. Cancer Cell Lines: A cancer cell line whose set of essential proteins will be used as target nodes for the network controllability algorithm. These nodes can act also as seed nodes if the user decides so.
  4. List of Additional Target Genes: A set of target nodes defined in addition to those in the “Cancer Cell Lines". These nodes can act also as seed nodes if the user decides so.
  5. Gap Between Genes: The gap parameter used by Moksiskaan to generate the network.
  6. Target By Drug: Defines if the pipeline includes also the drug target information for the driven nodes. If so, then the driven nodes for which there exist FDA approved drugs will be specifically highlighted in the output of the pipeline.
  7. User defined drug target genes to be included in the analysis: User has an option to include also set of custom drug target proteins. If the "Target By Drug" field is chosen, the user-defined custom drug targets will be considered along with the FDA-aproved drugs targets.

Output

Sample Case Studies:

Case study 1: Breast cancer

INPUT:
List of Genes/Proteins to Generate the Network List of Additional Target Genes Gap Between Genes
AKT1
AKT3
NRG1
MTOR
ERBB3
One

OUTPUT:
(Please note that NetControl4BioMed is a stochastic tool and for the same input it may give one of several different outputs. The following is just one of possible outputs for the input above.)
driven.csv extra.csv details.txt
AKT1 NRG1 NRG1
AKT1
ERBB3 <- NRG1
MTOR <- AKT1

Network sample 1





Case study 2: Type 2 diabetic

INPUT
List of Genes/Proteins to Generate the Network List of Additional Target Genes Gap Between Genes
KCNQ1
NOTCH2
TCF7L2
HNF1A
HNF4A
CDKN2B Two

OUTPUT:
(Please note that NetControl4BioMed is a stochastic tool and for the same input it may give one of several different outputs. The following is just one of possible outputs for the input above.)
driven.csv extra.csv details.txt
MYC CDKN2B <- MYC

Network sample 2





Case study 3: Alzheimer's disease

INPUT
List of Genes/Proteins to Generate the Network List of Additional Target Genes Gap Between Genes
BDNF
CHRNA6
CHRNA7
CHRNB4
CR1
EFNA5
EGFR
EPHA1
EPHA4
GAB2
IRS1
NOS3
NTRK2
PIK3R1
PPP1R3A
PPP3CB
PTGS2
SOS2
NOS3 Two

OUTPUT:
(Please note that NetControl4BioMed is a stochastic tool and for the same input it may give one of several different outputs. The following is just one of possible outputs for the input above.)
driven.csv extra.csv details.txt
MTOR CR1 NOS3 <- AKT1 <- MTOR
Network sample 3