A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns

Bacterial viability on chemically modified silicon nanowire arrays

The global threat of antimicrobial resistance is driving an urgent need for novel antimicrobial strategies. Functional surfaces are essential to prevent spreading of infection and reduce surface contamination. In this study we have fabricated and characterized multiscale-functional nanotopographies with three levels of functionalization: (1) nanostructure topography in the form of silicon nanowires, (2) covalent chemical modification with (3-aminopropyl)triethoxysilane, and (3) incorporation of chlorhexidine digluconate. Cell viability assays were carried out on two model microorganisms E. coli and S. aureus over these nanotopographic surfaces. Using SEM we have identified two growth modes producing distinctive multicellular structures, i.e. in plane growth for E. coli and out of plane growth for S. aureus. We have also shown that these chemically modified SiNWs arrays are effective in reducing the number of planktonic and surface-attached microorganisms

Bacterial Footprints in Elastic Pillared Microstructures

Soft substrates decorated with micropillar arrays are known to be sensitive to deflection due to capillary action. In this work, we demonstrate that micropillared epoxy surfaces are sensitive to single drops of bacterial suspensions. The micropillars can show significant deformations upon evaporation, just as capillary action does in soft substrates. The phenomenon has been studied with five bacterial strains: S. epidermidis, L. sakei, P. aeruginosa, E. coli, and B. subtilis. The results reveal that only droplets containing motile microbes with flagella stimulate micropillar bending, which leads to significant distortions and pillar aggregations forming dimers, trimers, and higher order clusters. Such deformation is manifested in characteristic patterns that are left on the microarrayed surface following evaporation and can be easily identified even by the naked eye. Our findings could lay the ground for the design and fabrication of mechanically responsive substrates, sensitive to specific types of microorganisms

Modular approach for bimodal antibacterial surfaces combining photo-switchable activity and sustained biocidal release

Photo-responsive antibacterial surfaces combining both on-demand photo-switchable activity and sustained biocidal release were prepared using sequential chemical grafting of nano-objects with different geometries and functions. The multi-layered coating developed incorporates a monolayer of near-infrared active silica-coated gold nanostars (GNS) decorated by silver nanoparticles (AgNP). This modular approach also enables us to unravel static and photo-activated contributions to the overall antibacterial performance of the surfaces, demonstrating a remarkable synergy between these two mechanisms. Complementary microbiological and imaging evaluations on both planktonic and surface-attached bacteria provided new insights on these distinct but cooperative effects

Modified Mesoporous Silica Nanoparticles with a Dual Synergetic Antibacterial Effect

Application of mesoporous silica nanoparticles (MSNs) as antifouling/antibacterial carriers is limited and specifically with a dual synergetic effect. In the present work, MSNs modified with quaternary ammonium salts (QASs) and loaded with the biocide Parmetol S15 were synthesized as functional fillers for antifouling/antibacterial coatings. From the family of the MSNs, MCM-48 was selected as a carrier because of its cubic pore structure, high surface area, and high specific pore volume. The QASs used for the surface modification of MCM-48 were dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammonium chloride and dimethyltetradecyl[3-(triethoxysilyl)propyl]ammonium chloride. The QAS-modified MCM-48 reveals strong covalent bonds between the QAS and the surface of the nanoparticles. The surface functionalization was confirmed by Fourier transform infrared spectroscopy, thermogravimetric analysis, elemental analysis, and ζ-potential measurements. Additional loading of the QAS-modified MCM-48 with a commercially available biocide (Parmetol S15) resulted in a synergetic dual antibacterial/antifouling effect. Either loaded or unloaded QAS-modified MSNs exhibited high antibacterial performance confirming their dual activity. The QAS-modified MCM-48 loaded with the biocide Parmetol S15 killed all exposed bacteria after 3 h of incubation and presented 100% reduction at the antibacterial tests against Gram-negative and Gram-positive bacteria. Furthermore, the QAS-modified MCM-48 without Parmetol S15 presented 77–89% reduction against the exposed Gram-negative bacteria and 78–94% reduction against the exposed Gram-positive bacteria. In addition, the modified MCM-48 was mixed with coating formulations, and its antifouling performance was assessed in a field test trial in northern Red Sea. All synthesized paints presented significant antifouling properties after 5 months of exposure in real seawater conditions, and the dual antifouling effect of the nanoparticles was confirmed

A novel self-assembly strategy for the fabrication of nano-hybrid satellite materials with plasmonically enhanced catalytic activity

The generation of hydrogen from water using light is currently one of the most promising alternative energy sources for humankind but faces significant barriers for large-scale applications due to the low efficiency of existing photo-catalysts. In this work we propose a new route to fabricate nano-hybrid materials able to deliver enhanced photo-catalytic hydrogen evolution, combining within the same nanostructure, a plasmonic antenna nanoparticle and semiconductor quantum dots (QDs). For each stage of our fabrication process we probed the chemical composition of the materials with nanometric spatial resolution, allowing us to demonstrate that the final product is composed of a silver nanoparticle (AgNP) plasmonic core, surrounded by satellite Pt decorated CdS QDs (CdS@Pt), separated by a spacer layer of SiO2 with well-controlled thickness. This new type of photoactive nanomaterial is capable of generating hydrogen when irradiated with visible light, displaying efficiencies 300% higher than the constituting photo-active components. This work may open new avenues for the development of cleaner and more efficient energy sources based on photo-activated hydrogen generation

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids

Summary: How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns