In silico analyses of the p53 models

6.2.1 Functional network analysis using CellNetAnalyzer CellNetAnalyzer (CNA) is discussed in section 4 of this report.

All p53 models (PMH260, PMH300 and Meso – PMH61) were constructed via retrieval of protein – protein interaction information derived from STRING and imported into CNA (v.2013.1). All Confirmed interaction records were input into tab delimited text. file and processed to a node and reaction transcript readable by CNA as described by Klamt et al. (2007). A new signal flow network project was declared and data files of species/nodes and their edges/interactions were uploaded. Two approaches facilitated by CNA were used for various in silico knock out tests and comparative in silico

simulations; dependency matrix calculations and logical steady state analysis (LSSA) described below (6.2.1.2).

6.2.1.1 Application of dependency matrices to the p53 models

CNA facilitates construction and calculation of dependency matrices within a given network. The dependency matrix represents the effects between node pairs by

presence of feedback loops within the system. Two nodes may be represented, i and j with six dependency effects observed in a matrix: Strong activator, weak activator, strong inhibitor, weak inhibitor, ambivalent factor and no effect defined by:

1. If no negative or positive path exists between node i and node j, then I will have no effect on node j

2. If both a positive and negative path exists between node i to node j, then i is an ambivalent factor of node j

3. If only negative paths exist between node i to node j, with only negative feedback loops present in these paths, then node i is a weak inhibitor of node j 4. If only positive paths exist between node i to node j with negative feedback

loops present in these positive paths, then node i is a weak activator of node j 5. If only negative paths exist between node i to node j with negative feedback

loops absent in these paths, then node i is a strong inhibitor of node j 6. If only positive paths exist between node i to node j with negative feedback

loops absent in positive paths, then node i is a strong activator of node j.

The connectivity of all nodes was determined using Cytoscape (v. 3.1) and the most highly connected nodes of the network deleted to generate an in silico knockout simulation. The particular nodes deleted were dependant on model being analysed. For all models, ASCII files were generated and imported into CNA in accordance with the format described in (Klamt et al.2007). For the deleted node, default values of ‘NaN’ given as’ #’ in species files were altered to ‘0’ in the network composer. Shortest paths and species dependencies were calculated using approximate algorithm for each knockout (KO) scenario by declaration of exclusion of any given species/node defined as ‘0’. Dependency matrices were subsequently calculated where the 6 possible relationships may be derived as defined above. Result files of data corresponding to the values of 1 – 6 defining the above relationships were exported as raw data in tab

delimited format and subsequently converted into a readable format accepted by excel for analysis by comparison to the default model.

6.2.1.2 Application of logical steady state analysis to the p53 models

LSSA is a method for predictive input and output relationships in signalling networks. Interaction hypergraphs supported in CNA are used to represent signalling events that allow for a synchronous interaction targeting the same node(s) to be combined, referring to the relationship operators of AND, OR, NOT (Klamt et al. 2007). The logical behaviour of node states within a Boolean network can be analysed by LSSA in

response to various in silico perturbations of different input and knock out tests.

In LSSA, each scenario is defined by declaration of the value of input signals. For example, declaring the values below where:

‘0’ represents inactivated ‘1’ represents activated

‘NaN’ represents undetermined

The state of some nodes may remain undetermined if several logical steady states are possible (NaN). Proceeding declaration of each scenario from the given values, CNA calculates the steady state of each node and its interactions within a network by logical operation (above). Resultant node and interaction states for each scenario are

returned by CNA which can be compared to default model for investigation of network perturbations.

ASCII files of interactions were generated for all models in accordance with (Klamt et al.2007). Four different scenarios were constructed for application of LSSA which can represent or mimic in vivo processes such as loss of p53 function arising from mutation (p53 deletion from the network) in the presence or absence of DNA damage or

PMH300 and Meso- PMH61 models and table 6.1 for hypoxia and p53 simulations (PMH302 and Meso – PMH61 models).

Table 6 Four in silico scenarios generated for LSSA of DNA damage ON/OFF in the presence / absence of p53

Scenario No. Input signal Model background

1 DNA damage ON p53 knockout

2 DNA damage OFF p53 knockout

3 DNA damage ON p53 wildtype

4 DNA damage OFF p53 wildtype

Table 6.1 Four in silico scenarios generated for LSSA of hypoxia ON/OFF in the presence / absence of p53

Scenario No. Input signal Model background

1 Hypoxia ON p53 knockout

2 Hypoxia OFF p53 knockout

3 Hypoxia ON p53 wildtype

4 Hypoxia OFF p53 wildtype

To mimic loss of p53, deletion of p53 from the network was via total node/ species deletion from the network composer and all associated edges were removed to

generate a p53 knockout scenario. For construction of a p53 wild type background, the p53 node was switched to 1 in CNA network composer. The DNA damage or hypoxia input was switched to ‘0’ referring to hypoxia or DNA damage OFF or ‘1’ referring to hypoxia or DNA damage ON for the various scenarios as described above in table 6 and table 6.1.

Each scenario was subsequently imported and computed separately by CNA and compared against the default model for investigation of the network response to p53, DNA damage or hypoxia presence or absence. Results obtained from CNA in tab delimited format were subsequently converted and imported into excel files for analysis.

Four comparative scenarios of different DNA damage, hypoxia and p53 statuses were additionally constructed to investigate network perturbations in response to the different in silico stresses. In particular node state changes of angiogenic and apoptotic nodes were explored. The various in silico scenarios simulated are summarised in tables 6.2 and 6.3 for DNA damage and hypoxia respectively.

Table 6.2 List of four different scenario comparisons for in silico simulations using LSSA for DNA damage

In silico simulation Scenario No.

1 p53 wildtype DNA damage ON vs. p53 knockout DNA damage OFF 2 p53 wildtype DNA damage OFF vs. p53 knockout DNA damage OFF 3 p53 wildtype DNA damage OFF vs. p53 wildtype DNA damage ON 4 p53 knockout DNA damage OFF vs. p53 knockout DNA damage ON

Table 6.3 List of four different scenario comparisons for in silico simulations using LSSA for hypoxia

In silico simulation Scenario No.

1 p53 wildtype hypoxia ON vs. p53 knockout hypoxia OFF 2 p53 wildtype hypoxia OFF vs. p53 knockout hypoxia OFF 3 p53 wildtype hypoxia OFF vs. p53 wildtype hypoxia ON 4 p53 knockout hypoxia OFF vs. p53 knockout hypoxia ON