Boolean networks are popular models for gene regulation, where genes are regarded as binary units, that can be either expressed or not, each updated at regular time intervals according to a random Boolean function of its neighbouring genes. Stable gene expression profiles, corresponding to cell types, are regarded as attractors of the network dynamics. However, the random character of gene updates does not allow to link explicitly the existence of attractors to the biological mechanism with which gene interact. We propose a bipartite Boolean network approach which integrates genes and regulatory proteins (i.e. transcription factors) into a single network, where interactions incorporate two fundamental aspects of cellular biology, i.e. gene expression and gene regulation, and the resulting dynamics is highly non-linear. Since any finite stochastic system is ergodic, the emergence of an attractor structure, stable under noisy conditions, requires a giant component in the bipartite graph. By adapting graph percolation techniques to directed bipartite graphs, we are able to calculate exactly the region, in the network parameters space, where a cell can sustain steady-state gene expression profiles, in the absence of inhibitors, and we quantify numerically the effect of inhibitors. Results show that for cells to sustain a steady-state gene expression profile, transcription factors should typically be small protein complexes that regulate many genes. This condition is crucial for cell reprogramming and remarkably well in line with biological facts.

Cells respond to their environments and self-organise into multicellular assemblies with dedicated functions. The migratory and homing response of cells to soluble ligands can be studied by using different techniques, but for real time studies of complex multicellular self-organisation, novel and simpler systems are required. We fabricated a flexible open access microsystem and tested the design by studying cell recruitment from an immune cell reservoir towards an infectious compartment. The two compartments were connected by a network of bifurcated microchannels allowing diffusion of signalling molecules and migration of cells. Bacterial filters were incorporated in the design to prevent bacteria and activated cells from entering the network, permitting migration only from the recruitment reservoir. The fabricated microsystem allows real-time continuous monitoring of cellular decision-making based on biologically produced gradients of cytokines and chemokines. It is a valuable tool for studying cellular migration and self-organisation in relation to infections, autoimmunity, cancer, stem cell homing, and tissue and wound repair.

Networks of gene regulation determine cell identity and regulate cell function, but little is known about which logics are biologically favored. We show that, remarkably, the number of logical dependencies that a gene can have on others is severely restricted. This is because genes interact via transcription factors but only gene-gene interactions are observed. We enumerate the number of biologically permitted logics by mapping the problem onto the composition of Boolean functions, and confirm our predictions computationally. This is a key insight into how information is processed at the genetic level.

We construct a model of cell reprogramming (the conversion of fully differentiated cells to a state of pluripotency, known as induced pluripotent stem cells, or iPSCs) which builds on key elements of cell biology viz. cell cycles and cell lineages. Although reprogramming has been demonstrated experimentally, much of the underlying processes governing cell fate decisions remain unknown. This work aims to bridge this gap by modelling cell types as a set of hierarchically related dynamical attractors representing cell cycles. Stages of the cell cycle are characterised by the configuration of gene expression levels, and reprogramming corresponds to triggering transitions between such configurations. Two mechanisms were found for reprogramming in a two level hierarchy: cycle specific perturbations and a noise induced switching. The former corresponds to a directed perturbation that induces a transition into a cycle-state of a different cell type in the potency hierarchy (mainly a stem cell) whilst the latter is a priori undirected and could be induced, e.g., by a (stochastic) change in the cellular environment. These reprogramming protocols were found to be effective in large regimes of the parameter space and make specific predictions concerning reprogramming dynamics which are broadly in line with experimental findings.