Our work introduces the Neural Lindenmayer system, an innovative approach to learning Lindenmayer rules from string representations in the presence of noise. It demonstrates its effectiveness in the example of the dragon curve. By integrating a selection network with multiple rule networks, our system effectively captures the variability in rule lengths. This offers a promising direction for analyzing and generating complex patterns observed in nature.

This research paper presents a chemical compiler developed to find optimal configurations of a platform for synthesizing specific branched oligomers in an artificial chemistry, along with exemplary compiler output and benchmarks where the platform configuration suggested by the compiler is compared to other configurations in simulation. The compiler operates as a pipeline with two stages: labelling and optimization. The report explains the structure of the compiler target and its interpretation, followed by a code walk-through of the compiler stages with code snippets and examples. The compiler can be used as a code generator for reactions in a chemical simulator and to derive loading schemes for multilevel droplets. The results obtained in simulations suggest that the container system can efficiently optimize the yield of coupled reaction networks and that multi-level droplets can lead to significant improvements.

The computer simulation of tumor – host ecosystems interacting with an adaptive immune system may serve as a tool for anti-cancer treatment optimization, but requires appropriate mathematical models. Regarding the tasks of the adaptive immune system (antigen pattern recognition and classification), a perceptron can be used as a conceptual structure representing corresponding biological structures for antigen pattern recognition and classification such as Antigen Presenting Cells (APC’s) and their interaction with effector cells in lymph nodes. Regarding the topology of the lymph vessel network, the adaptive immune system may be represented by several perceptrons receiving information about antigen patterns from different tissue compartments. In this study, two scenarios of lymph node arrangement have been investigated. In both scenarios, a tumor-host tissue compartment is treated with ionizing radiation and a second compartment with host tissue and a tumor metastasis is not irradiated. The results exhibit a dependence of the immune response onto the lymph node arrangement, indicating that the topology of the lymph node network is important for an optimal adaptive immune response. The presented simplistic model structure does not allow for a perfect classification between tumor and host tissue. Instead of a single perceptron which is related to the interaction of immune cells in a corresponding lymph node as suggested in this study, networks of locally interacting units may be considered as layers building a deep (convolutional) neural network - like structure.

Spatial resolution is relevant for many processes in population dynamics because it may give rise to heterogeneity. Simulating the effect of space in two or three dimensions is computationally costly. Furthermore, in Euclidean space, the notion of heterogeneity is complemented by neighbourhood correlations. In this paper, we use an infinite-dimensional simplex as a minimal model of space in which heterogeneity is realized, but neighbourhood is trivial and study the coexistence of viral traits in a SIRS - model. As a function of the migration parameter, multiple regimes are observed. We further discuss the relevance of minimal models for decision support.

As part of the European Horizon 2020 project ACDC, a chemical compiler is being developed that allows the self-assembly of artificial, three-dimensional, vesicular structures to be first simulated and then translated into reality. This work reports on simulations that shed light on an important aspect: How to disentangle inter-vesicular connections?

In this work, we propose a framework that derives the configuration of an artificial, compartmentalized, cell-like structure in order to maximize the yield of a desired output reactant given a formal description of the chemistry. The configuration of the structure is then used to compile G-code for 3D printing of a microfluidic platform able to manufacture the aforementioned structure. Furthermore, the compiler output includes a set of pressure profiles to actuate the valves at the input of the microfluidic platform. The work includes an outline of the steps involved in the compilation process and a discussion of the algorithms needed for each step. Finally, we provide formal, declarative languages for the input and output interfaces of each of these steps.

The latest report from the Intergovernmental Panel on Climate Change (IPCC) estimated that humanity has a time window of about 12 years in order to prevent anthropogenic climate change of catastrophic magnitude. Green house gas emission from air travel, which is currently rising, is possibly one of the factors that can be most readily reduced. Within this context, we advocate for the re-design of academic conferences in order to decrease their environmental footprint. Today, virtual technologies hold the promise to substitute many forms of physical interactions and increasingly make their way into conferences to reduce the number of travelling delegates. Here, we present the results of a survey in which we gathered the opinion on this topic of academics worldwide. Results suggest there is ample room for challenging the (dangerous) business-as-usual inertia of scientific lifestyle.

We present a model for the spread, transmission and competition of skills with an emphasis on the role of spatial mobility of individuals. From a methodological point of view, we seek mathematical and computational simplicity in the sense of a minimal model. This minimalism lets us use a infinite dimensional simplex space and not a Euclidean space as underlying structure. Such a simplex captures the essentials of spatial heterogeneity without the mathematical difficulties of neighborhood structures. In the presented model, individuals may have no skill or either skill A or B. Individuals are born unskilled and may acquire skills by learning from a skilled individual. Skill A results in a small reproductive advantage and is easy to transmit (teaching happens at high rate), whereas skill B is harder to teach but results in a high benefit. The model exhibits a rich behavior; after an initial transient, the system settles to a fix point (constant distribution of skills), whereby the distribution of skills depends on a mobility parameter m. We observe different regimes, and as the main result, we conclude that for some settings of the system parameters, the spread of the (harder to learn but more beneficial) skill B is only possible within a specific range of the mobility parameter. From a technical point of view, this paper presents the application of the PRESS–method (probability reduced evolution of spatially resolved species) that enables the study of spatial effects in a very efficient manner. We analyze the consequences of spatial organization and argue that we can study aspects of social dynamics in an infinite dimensional simplex space. In spite of this maybe daunting name, the dynamics on such a structure is comparably easy to implement. The model we present is far from reflecting all the details of human interaction. On the contrary, we deliberately tailored the model to be as simple as possible from a mathematical point of view (but still reflecting central properties of spatial organization). This approach is guided by physics, where seemingly simple models which obviously don’t reflect the true physical behavior of a system (such as the Ising model) are nevertheless suited to reveal fundamental aspects and limiting cases of the real world.