The olfactory system in insects has evolved to process the dynamic changes in the concentration of food odors or sex pheromones to localize the nutrients or conspecific mating partners. Experimental studies have suggested that projection neurons (PNs) in insects encode not only the stimulus intensity but also its rate-of-change (input gradient). In this study, we aim to develop a simple computational model for a PN to understand the mechanism underlying the coding of the rate-of-change information. We show that the spike frequency adaptation is a potential key mechanism for reproducing the phasic response pattern of the PN in Drosophila. We also demonstrate that this adaptation mechanism enables the PN to encode the rate-of-change of the input firing rate. Finally, our model predicts that the PN exhibits the intensity-invariant response for the pulse and ramp odor stimulus. These results suggest that the developed model is useful for investigating the coding principle underlying olfactory information processing in insects.

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

One of the fundamental problems of contemporary history is to understand the processes governing the rise and fall of polities. The universality of boom-and-bust dynamics associated with the life-cycle of polities tempts to treat the problem mathematically and thus brings it to the framework of cliodynamics. Here we introduce a mathematical model of evolving polity under assumption that its evolution is associated with interactions of certain groups of people, forming the polity and differing by their psycho-ethic characteristics. The model is given in terms of ordinary differential equations and the bust dynamics associated with the rise and fall of polities is modelled as an excitation process, which is the non-linear phenomenon, well known in mathematical biology. We consider the deterministic as well as the stochastic version of the model which we fit to the time-scale of civilization’s lifespan. We also expand the model to study interaction between two evolving polities. Investigation is performed using analytical methods as well as numerical integration (i.e. MATLAB simulation).