The number N of detectable (i.e. communicating) extraterrestrial civilizations in the Milky Way galaxy is usually calculated by using the Drake equation. This equation was established in 1961 by Frank Drake and was the first step to quantifying the Search for ExtraTerrestrial Intelligence (SETI) field. Practically, this equation is rather a simple algebraic expression and its simplistic nature leaves it open to frequent re-expression. An additional problem of the Drake equation is the time-independence of its terms, which for example excludes the effects of the physico-chemical history of the galaxy. Recently, it has been demonstrated that the main shortcoming of the Drake equation is its lack of temporal structure, i.e., it fails to take into account various evolutionary processes. In particular, the Drake equation does not provides any error estimation about the measured quantity. Here, we propose a first treatment of these evolutionary aspects by constructing a simple stochastic process that will be able to provide both a temporal structure to the Drake equation (i.e. introduce time in the Drake formula in order to obtain something like N(t)) and a first standard error measure.

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While the control of cell migration by biochemical and biophysical factors is largely documented, a precise quantification of cell migration parameters in different experimental contexts is still questionable. Indeed, these phenomenological parameters can be evaluated from data obtained either at the cell population level or at the individual cell level. However, the range within which both characterizations of cell migration are equivalent remains unclear. We analyse here to which extent both sources of data could be integrated within a unified description of cell migration by considering the motility of the endothelial cell line EAhy926. Using time-lapse video-microscopy and associated analysis of digital image time series, we quantified EAhy926 random motility coefficient, migration speed and trajectory persistence time in two different migration assays: the in vitro wound healing assay, and the cell-populated agarose drop assay. In order to analyse the agreement between independent quantifications of cell motility based either on individual cell analysis or cell population dynamic analysis, a theoretical multi-agents cellular model was developed and discussed as a possible theoretical framework able to unify these multi-scale data. Model simulations especially reveal the potential bias induced by cell proliferation and cell-cell adhesion when cell migration parameters are estimated from the extensively used in vitro wound healing assay.

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The Cellular Potts Model (CPM) is a cellular automaton (CA), developed by Glazier and Graner in 1992, to model the morphogenesis. In this model, the entities are the cells. It has already been improved in many ways; however, a key point in biological systems, not defined in CPM, is energetic exchange between entities. We integrate this energetic concept inside the CPM. We simulate a cell differentiation inside a growing cell tissue. The results are the emergence of dynamic patterns coming from the consumption and production of energy. A model described by CA is less scalable than one described by a multi-agent system (MAS). We have developed a MAS based on the CPM, where a cell agent is implemented from the cell of CPM together with several behaviours, in particular the consumption and production of energy from the consumption of molecules.

The relevance of biological materials and processes to computing—aliasbioputing—has been explored for decades. These materials include DNA, RNA and proteins, while the processes include transcription, translation, signal transduction and regulation. Recently, the use of bacteria themselves as living computers has been explored but this use generally falls within the classical paradigm of computing. Computer scientists, however, have a variety of problems to which they seek solutions, while microbiologists are having new insights into the problems bacteria are solving and how they are solving them. Here, we envisage that bacteria might be used for new sorts of computing. These could be based on the capacity of bacteria to grow, move and adapt to a myriad different fickle environments both as individuals and as populations of bacteria plus bacteriophage. New principles might be based on the way that bacteria explore phenotype space via hyperstructure dynamics and the fundamental nature of the cell cycle. This computing might even extend to developing a high level language appropriate to using populations of bacteria and bacteriophage. Here, we offer a speculative tour of what we term bactoputing, namely the use of the natural behaviour of bacteria for calculating.

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