Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as ‘not of function’ and ‘function of not’, are introduced. In the former, the inputs are assumed to be correctly interpreted but the output of the update rule is perturbed; and in the latter, each input is perturbed but the correct update rule is applied. These and previously used perturbation methods were applied to two existing Boolean models, namely the human melanogenesis signalling network and the fly segment polarity network. Through mathematical simulations, it was found that these methods successfully identified nodes earlier found to be sensitive using other methods, and were also able to identify sensitive nodes which were previously unreported.

Cells communicate with their environment via proteins, located at the plasma membrane separating the interior of a cell from its surroundings. The spatial distribution of these proteins in the plasma membrane under different physiological conditions is of importance, since this may influence their signal transmission properties. In this study, the authors compare different methods such as hierarchical clustering, extensible Markov models and the gammics method for analysing such a spatial distribution. The methods are examined in a simulation study to determine their optimal use. Afterwards, they analyse experimental imaging data and extend these methods to simulate dual colour data.

In this study, the authors propose a methodology for the estimation of glucose masses in stomach (both in solid and liquid forms), intestine, plasma and tissue; insulin masses in portal vein, liver, plasma and interstitial fluid using only plasma glucose measurement. The proposed methodology fuses glucose–insulin homoeostasis model (in the presence of meal intake) and plasma glucose measurement with a Bayesian non-linear filter. Uncertainty of the model over individual variations has been incorporated by adding process noise to the homoeostasis model. The estimation is carried out over 24 h for the healthy people as well as a type II diabetes mellitus patients. In simulation, the estimator follows the truth accurately for both the cases. Moreover, the performances of two non-linear filters, namely the unscented Kalman filter (KF) and cubature quadrature KF are compared in terms of root mean square error. The proposed methodology will be helpful in future to: (i) observe a patient's insulin–glucose profile, (ii) calculate drug dose for any hyperglycaemic patients and (iii) develop a closed-loop controller for automated insulin delivery system.

This study proposes a two-dimensional (2D) oscillator model of p53 network, which is derived via reducing the multidimensional two-phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables, and studies the impact of p53-regulators on cell fate decision. First, the authors identify a 6D core oscillator module, then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator. This oscillator provides a mechanism that leads diverse modes underpinning cell fate, each corresponding to a cell state. To investigate the effects of p53 inhibitors and the intrinsic time delay of Wip1 on the characteristics of oscillations, they introduce also a delay differential equation version of the 2D oscillator. They observe that the suppression of p53 inhibitors decreases the amplitudes of p53 oscillation, though the suppression increases the sustained level of p53. They identify Wip1 and P53DINP1 as possible targets for cancer therapies considering their impact on the oscillator, supported by biological findings. They model some mutations as critical changes of the phase space characteristics. Possible cancer therapeutic strategies are then proposed for preventing these mutations’ effects using the phase space approach.

The knowledge on the biological molecular mechanisms underlying cancer is important for the precise diagnosis and treatment of cancer patients. Detecting dysregulated pathways in cancer can provide insights into the mechanism of cancer and help to detect novel drug targets. Based on the wide existing mutual exclusivity among mutated genes and the interrelationship between gene mutations and expression changes, this study presents a network-based method to detect the dysregulated pathways from gene mutations and expression data of the glioblastoma cancer. First, the authors construct a gene network based on mutual exclusivity between each pair of genes and the interaction between gene mutations and expression changes. Then they detect all complete subgraphs using CFinder clustering algorithm in the constructed gene network. Next, the two gene sets whose overlapping scores are above a specific threshold are merged. Finally, they obtain two dysregulated pathways in which there are glioblastoma-related multiple genes which are closely related to the two subtypes of glioblastoma. The results show that one dysregulated pathway revolving around epidermal growth factor receptor is likely to be associated with the primary subtype of glioblastoma, and the other dysregulated pathway revolving around TP53 is likely to be associated with the secondary subtype of glioblastoma.