In computational biology, in silico simulators are vital for exploring and understanding the behavior of complex biological systems. Hybrid multi-level simulators, such as PhysiCell and PhysiBoSS 2.0, integrate multiple layers of biological complexity, providing deeper insights into emergent patterns. However, one key limitation of these simulators is the inability to adjust simulation parameters once the simulation has started, which hinders the interactive exploration and adaptation of dynamic protocols ranging from biofabrication to in vitro pharmacological testing. To address this challenge, we introduce the Start & Stop add-on for PhysiCell and PhysiBoSS 2.0. This add-on offers multi-level state preservation and multi-modal stop control, triggered by simulation time or cell conditions, enabling users to pause a simulation, adjust parameters, and then resume from the exact halted state. We validate Start & Stop using two well-established PhysiBoSS 2.0 use cases, a tumor spheroid 3T3 mouse fibroblasts use case under tumor necrosis factor (TNF) stimulation, and a lung cancer cell line invasion simulation, demonstrating that it preserves the simulator's original behavior while enabling interactive configuration changes that facilitate the exploration of diverse and adaptive treatment strategies. By enhancing flexibility and user interaction, Start & Stop makes PhysiCell and PhysiBoSS 2.0 more akin to real in vitro scenarios, thus expanding the range of potential simulations and advancing more effective protocol development in a variety of applications.

Multi-level, hybrid models and simulations, among other methods, are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast compared with the simulator. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes.

In the contemporary security landscape, the incorporation of photonics has emerged as a transformative force, unlocking a spectrum of possibilities to enhance the resilience and effectiveness of security primitives. This integration represents more than a mere technological augmentation; it signifies a paradigm shift towards innovative approaches capable of delivering security primitives with key properties for low-power systems. This not only augments the robustness of security frameworks, but also paves the way for novel strategies that adapt to the evolving challenges of the digital age. This paper discusses the security layers and related services that will be developed, modeled, and evaluated within the Horizon Europe NEUROPULS project. These layers will exploit novel implementations for security primitives based on physical un-clonable functions (PUFs) using integrated photonics technology. Their objective is to provide a series of services to support the secure operation of a neuromorphic photonic accelerator for edge comnuting applications.

BACKGROUND: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS: BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

Single-cell assays for transposase-accessible chromatin sequencing data represent a potent tool for exploring the epigenetic heterogeneity within cell populations. Despite their power, understanding the chromatin accessibility landscape poses challenges. This study introduces Gene Regulation Accessibility Integrating GeneHancer (GRAIGH), a novel approach to interpreting genome accessibility by integrating information from the GeneHancer database, detailing genome-wide enhancer-to-gene associations. Initially, we outline the methods for integrating GeneHancer with scATAC-seq data. This involves creating a new matrix where GeneHancer element IDs replace traditional accessibility peaks as features. Subsequently, the paper assesses the method’s ability to analyze data and detect cellular heterogeneity. Notably, our findings demonstrate the selective accessibility of GeneHancer elements for distinct cell types, with connected genes serving as precise marker genes. Furthermore, we explore the specificity of GeneHancer element accessibility, highlighting their high selectivity against gene activity. This investigation underscores the potential of Gene Regulation Accessibility Integrating GeneHancer in unraveling the complexities of chromatin accessibility, offering insights into the nuanced relationship between accessibility and cellular heterogeneity.

Single-cell multimodal technologies are becoming the hot topic of single-cell heterogeneity and function studies, promising to unravel the hidden relationship and functionalities of different aspects of the cells. Among the plethora of single-cell technologies, interesting is the patch-seq technology, which simultaneously performs Patch clamp measures and scRNA-seq on the same cells. However, given the experimental limitations of throughput of Patch clamp, the scRNA-seq analysis is challenging because it requires more samples to investigate cellular heterogeneity. Usually, the solution is associating the cells with the cell types in an existing scRNA-seq dataset. However, doing so loses part of the single cell resolution of the multimodal technique. Therefore, this work proposes a procedure leveraging the Seurat Integration process to find from a reference dataset t he most similar cells to the ones from the patch-seq. The similarity is how much gene expression profiles are identical, and to evaluate that, this work defines various etrics based on R and Index. In this way, one obtains a selection of suitable Reference cells to enrich the number of cells on which to perform multimodal investigation.

The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study.

The spread of antibiotic resistance is a growing problem known to be caused by antibiotic usage itself. This problem can be analyzed at different levels. Antibiotic administration policies and practices affect the societal system, which is made by human individuals and by their relations. Individuals developing resistance interact with each other and with the environment while receiving antibiotic treatments moving the problem at a different level of analysis. Each individual can be further see as a meta-organism together with his associated microbiotas, which prove to have a prominent role in the resistance spreading dynamics. Eventually, in each microbiota, population dynamics and vertical or horizontal transfer events implement cellular and molecular mechanisms for resistance spreading and possibly for its prevention. Using the Nets-within-nets formalism, in this work we model the relation between different antibiotic administration protocols and resistance spread dynamics both at the human population and at the single microbiota level.