A Boolean network model of cytotoxic T cell signaling that reproduces the known experimental results of these T cells in the context of T-LGL leukemia was previously constructed by Zhang et al.

This network model consists of 60 nodes and 142 regulatory edges, with the nodes representing genes, proteins, receptors, small molecules, external signals (e.g.

This result agrees with previous studies of T-LGL leukemia, in which it was found that blocking SIP signaling induced apoptosis in leukemic T-LGL cells [28, 47] , a result reproduced by the network model when the state of SIP was set to OFF [41, 46].

In this work we proposed a network control approach that combines the structural and functional information of a discrete (logical) dynamic network model to identify control targets.

We illustrated our methods potential to find intervention targets for cancer treatment and cell differentiation by applying it to network models of T-LGL leukemia and helper T cell differentiation.

Logical dynamic network models [31—38] consist of a set of binary variables {0,}, i = 1,2,. .

None of the networks we discuss in this work nor any intracellular network models we are aware of fall in this category; for more details see 81 Text, 82 Text, and ref [41].)

Intervention targets for each control strategy in the T-LGL leukemia network model .

To address this, we translate the studied Boolean network models into ordinary differential equation (ODE) models using the method described by Wittmann et al.

We present and develop the theory of 3-way networks, a type of hypergraph in which each edge models relationships between triplets of objects as opposed to pairs of objects as done by standard network models .

We explore approaches of how to prune these 3-way networks, illustrate their utility in comparative genomics and demonstrate how they find relationships which would be missed by standard 2—way network models using a phylogenomic dataset of 211 bacterial genomes.

In order to address this, we have developed a new three-way similarity metric and constructed three-way networks modelling the relationships between 211 bacterial genomes.

For both the Sorensen Index and the Czekanowski Index, the union of the 3-way best-edge network and the 2-way MST was calculated, resulting in a combined network model .

These networks, when used to model the phylogenomic relationships between 211 bacterial species revealed relationships between the species which were not found when using standard 2-way network models .

Network models are a useful reductionist approach for modelling complex systems.

Thus networks model a system in a pairwise manner, breaking a system down into individual parts (nodes), modelling relationships between pairs of these individual parts (edges) and then reconstructing the system as a network [1].

We then apply a 3-way network model to a set of 211 bacterial genomes, modelling the similarities between the bacteria on a whole genome scale, (based on gene family content), and compare the resulting 3-way networks to those obtained using standard 2-way network models .

With the aim of modelling higher order relationships than simply pairwise relationships, we define 3-way networks as network models of ternary relationships, i.e.

Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs.

The effect of GPe on FSIs was modelled in our network model as a constant amount of inhibition on FSI firing rates.

These simulations were only performed for the spiking neural network model since modelling correlations in a mean field model is nontrivial, especially when post-synaptic neurons are recurrently connected.

Here we describe the effect of the heterogenous connectivity of D1 and D2 neurons on their mutual interactions using both a reduced firing rate model and numerical simulations of a spiking striatal network model .

Apart from the obvious simplicity of our network model , we think there might be an additional reason for this.

The striatal network model was based on the spiking network model of the striatum as described in [12] , except that the network connectivity was not considered to be homogeneous as in [12].

The parameter values for both MSNs and F813 in our network model are summarized in Table 3.

Our analysis demonstrated that: (1) the theoretical predictions made from computational human brain models regarding the relationship between node degree and dPLI are supported by patterns observed in empirical EEG networks recorded from waking and unconscious states (in Fig 5A and 5B); (2) The functional brain network of the whole frequency band (0.5—55HZ) is highly correlated with the node degree distribution found in the anatomical brain network model .

simulated how network structure affects the phase lead/lag relationship between brain regions in a realistic brain network model [19].

showed in a network model that if two nodes are symmetrically located within a given network topology, the dynamics of the nodes will be fully synchronized even at a significant distance [53].

This relationship, derived from simple oscillator models, was applied successfully to complex brain network models generated computationally or reconstructed empirically.

We proceeded by constructing a simple coupled oscillatory network model , using a Stuart-Landau model oscillator to represent the neural mass population activity at each node of the network (see Materials and Methods, and 81 Text for details).

The directionality of interactions between nodes was studied through the modulated phase lead/lag relationship of coupled oscillators in general network models, large-scale anatomical brain network models and empirically-reconstructed networks from high-density human EEG across different states of consciousness (Fig 1).

Neural network model .

Such disentangled representations of objects have been identified in animal brains [63] and are common at intermediate layers of neural network models [64].

Neural Network Model Details

We utilize a standard network model common in previous studies of the evolution of modularity [23, 57], extended with neuromodulatory neurons to add reinforcement learning dynamics [25, 69].

Here, by considering a simple feedback network model of spiking neurons, we investigated the algorithm inherent to STDP in neural circuits containing feedback.

We first examined that point in a simple network model with two independent external sources (Fig 2A).

We constructed a network model with three feedforward layers as shown in Fig 1A (see Neural dynamics in Methods for details).

Based on the previous study [7] , we constructed a network model with one external layer and three layers of neurons (Fig 1A).

We find the evolution of narrow waists in a wide range of evolutionary parameters, in both linear and nonlinear multilayered network models .

Finally, we asked whether the present mechanism would apply in a nonlinear network model .

Simulations of multilayered network models evolving towards input-output goals

The second (hidden) layer of the network models the association cortex, and contains regular units (circles in Fig.

Earlier neural networks models used “backpropagation-through-time”, but its mechanisms are biologically implausible [77].

Several models addressed how neural network models can store F1 and compare it to F2 [46—48].