Cellular types of multicellular organisms are the stable results of complex intertwined processes that occur in biological cells. Among the many others, chromatin dynamics significantly contributes—by modulating access to genes—to differential gene expression, and ultimately to determine cell types. Here, we propose a dynamical model of differentiation based on a simplified bio-inspired methylation mechanism in Boolean models of GRNs. Preliminary results show that, as the number of methylated nodes increases, there is a decrease in attractor number and networks tend to assume dynamical behaviours typical of ordered ensembles. At the same time, results show that this mechanism does not affect the possibility of generating path dependent differentiation: cell types determined by the specific sequence of methylated genes.

Slime mould (Physarum) may not have brains, but they are capable of solving many significant and challenging problems. Existing models for studying the intelligent behaviour of Physarum have overlooked its foraging behaviour under competitive settings. In this research, we propose a new model based on Cellular Automata (CA) and Reaction Diffusion (RD) system, where multiple Physarum interact with each other and with their environment. The novelty of our model is that the Physarum has six neighbours at equidistant (hexagonal CA), furthermore, we have extended the model to 3D and multi-dimensional CA grid. The growth of Physarum is determined by the balance between attraction force towards food resources (determined by mass and quality) and repulsion forces between competing Physarum according to their power (mass) and hunger motivation. To validate this model, numerical experiments were conducted. Physarum with more mass succeeded in engulfing a larger number of food resources with high quality in shorter time (number of iteration). It also occupied larger area of the grid (territory) and excluded its competitors. We also conducted empirical analysis to compare the time complexity between the hexagonal and Moore neighbourhood, and it showed that hexagonal neighbourhood is more efficient than Moore in terms of computational cost. To the best of our knowledge, we are the first to present Physarum in competition mathematical model and the algorithms inspired from such a model has demonstrated its promising performance in solving several real world problems such as mobile wireless sensor networks, and discrete multi objective optimization problems.

Colorectal cancer (CRC) is the second most common tumour in the world (Bray, 2018). It has been proposed that morbidity and mortality could be mitigated by screening methods that identify key genetic mutations in the DNA of a patient’s biosample (Traverso, 2002). However, for this to work, a theoretical understanding of the most likely mutations that initiate malignant transformation, and how they affect subsequent microevolution, is needed. Specifically, we hypothesise that there is a CRC-proliferative mutation that is more likely to be initially fixated in the crypt . To investigate this, we developed an agent-based model of cells in the colon crypt that shows emergent biological homeostasis at the tissue level from the cellular and molecular interactions. We equipped each of the cells with a molecular gene network which, in their wildtype state, regulates homeostasis in the crypt and recapitulates known behaviour. We identified and modelled key genes implicated in CRC which, when mutated, alter the rate of death and division of cells. We used this model to study the biological first principles of the fixation of mutations, offering key spatial and temporal understanding of this process. We discuss the impact and clinical relevance of proliferative genetic mutations in isolation, pointing to the KRAS gene as a likely mutation to be initially fixed in the crypt.

In this contribution, we propose a system-level compartmental population dynamics model of tumour cells that interact with the patient (innate) immune system under the impact of radiation therapy (RT). The resulting in silico - model enables us to analyse the system-level impact of radiation on the tumour ecosystem. The Tumour Control Probability (TCP) was calculated for varying conditions concerning therapy fractionation schemes, radio-sensitivity of tumour sub-clones, tumour population doubling time, repair speed and immunological elimination parameters. The simulations exhibit a therapeutic benefit when applying the initial 3 fractions in an interval of 2 days instead of daily delivered fractions. This effect disappears for fast-growing tumours and in the case of incomplete repair. The results suggest some optimisation potential for combined hyperthermia-radiotherapy. Regarding the sensitivity of the proposed model, cellular repair of radiation-induced damages is a key factor for tumour control. In contrast to this, the radio-sensitivity of immune cells does not influence the TCP as long as the radio-sensitivity is higher than those for tumour cells. The influence of the tumour sub-clone structure is small (if no competition is included). This work demonstrates the usefulness of in silico – modelling for identifying optimisation potentials.

Espinosa-Soto and Wagner (2010) introduced a domain with weak assumptions on biology and environment, where modular structures emerge under simple evolutionary processes. We found a number of anomalous behaviours: modularity emerged in this domain, but could not dominate populations as observed in biology. Highly fit, modular solutions exist in the search space, can be readily found by a simple deterministic procedure (and presumably could dominate populations if found), but evolutionary search never found them, despite mutation biases that appear to favour those solutions. Moreover, emergence of modularity was promoted by stochastic dynamicity in the fitness function: a stochastic but fixed fitness function generated much less modular solutions.

Avian Influenza Viruses (AIV), specifically H5N1, are highly adaptive and mutate continuously throughout their life-cycle. The accumulation of constant mutations causes antigenic drift, leading to the spread of epidemics which result in billions of dollars in socioeconomic losses each year. Consequently, the containment of AIV epidemics is of vital importance. Computational approaches to the study of epidemiology, such as phylodynamic simulations, enhance in vivo analysis by examining the impact of ecological parameters and evolutionary traits, as well as forecasting the rise of future variants. We propose an improvement on existing phylodynamic simulation models through the introduction of: ❶ actual Hemagglutinin (HA) protein sequences, ❷ simulating mutations, ❸ and implementing an amino-acid level antigenic analysis algorithm to model natural selection pressure. In contrast to prior approaches that use abstract antigenic models, our method uses and yields actual HA strains enabling robust validation and direct application of results to inform vaccine design. We assess the validity of our method against the currentWorld Health Organization (WHO) H5N1 nomenclature phylogram for 3 countries. Our calibration and validation experiments use > 10,000 simulations with 1,000s of different parameter settings requiring over 2,500 hours of computing time. Our results show that our calibrated models yield the expected evolutionary characteristics but with a compromise of ∼10× longer simulation times.

Collective dynamics is a behavior of living systems that can improve their survivability in harsh and complex environments. Towards improving the vulnerability of engineering systems against power-source limitations, we focused on an oscillatory-growth dynamics of Bacillus subtilis biofilms. We developed a minimal reaction-diffusion model that captures the essence of the bacterial growth, nutrient consumption and electrical signalling. Numerical simulation of the model successfully recapitulated the oscillatory dynamics of bacterial biofilms. Thus, our model provides a first step forward towards designing biofilm-inspired engineering systems such as swarm robots and power supply networks.