Competence for neural crest induction is controlled by hydrostatic pressure through Yap.

Embryonic induction is a key mechanism in development that corresponds to an interaction between a signalling and a responding tissue, causing a change in the direction of differentiation by the responding tissue. Considerable progress has been achieved in identifying inductive signals, yet how tissues control their responsiveness to these signals, known as competence, remains poorly understood. While the role of molecular signals in competence has been studied, how tissue mechanics influence competence remains unexplored. Here we investigate the role of hydrostatic pressure in controlling competence in neural crest cells, an embryonic cell population. We show that neural crest competence decreases concomitantly with an increase in the hydrostatic pressure of the blastocoel, an embryonic cavity in contact with the prospective neural crest. By manipulating hydrostatic pressure in vivo, we show that this increase leads to the inhibition of Yap signalling and impairs Wnt activation in the responding tissue, which would be required for neural crest induction. We further show that hydrostatic pressure controls neural crest induction in amphibian and mouse embryos and in human cells, suggesting a conserved mechanism across vertebrates. Our work sets out how tissue mechanics can interplay with signalling pathways to regulate embryonic competence.

Generative models of morphogenesis in developmental biology.

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.

Measuring Nanoparticle Penetration Through Bio-Mimetic Gels.

BACKGROUND: In cancer nanomedicine, drugs are transported by nanocarriers through a biological system to produce a therapeutic effect. The efficacy of the treatment is affected by the ability of the nanocarriers to overcome biological transport barriers to reach their target. In this work, we focus on the process of nanocarrier penetration through tumour tissue after extravasation. Visualising the dynamics of nanocarriers in tissue is difficult in vivo, and in vitro assays often do not capture the spatial and physical constraints relevant to model tissue penetration. METHODS: We propose a new simple, low-cost method to observe the transport dynamics of nanoparticles through a tissue-mimetic microfluidic chip. After loading a chip with triplicate conditions of gel type and loading with microparticles, microscopic analysis allows for tracking of fluorescent nanoparticles as they move through hydrogels (Matrigel and Collagen I) with and without cell-sized microparticles. A bespoke image-processing codebase written in MATLAB allows for statistical analysis of this tracking, and time-dependent dynamics can be determined. RESULTS: To demonstrate the method, we show size-dependence of transport mechanics can be observed, with diffusion of fluorescein dye throughout the channel in 8 h, while 20 nm carboxylate FluoSphere diffusion was hindered through both Collagen I and Matrigel™. Statistical measurements of the results are generated through the software package and show the significance of both size and presence of microparticles on penetration depth. CONCLUSION: This provides an easy-to-understand output for the end user to measure nanoparticle tissue penetration, enabling the first steps towards future automated experimentation of transport dynamics for rational nanocarrier design.

Harnessing adaptive novelty for automated generation of cancer treatments.

Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However, it also leads to a rich design space which is difficult to investigate and optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate the design of potential therapies for such complex cases, we propose a computational model for fast, novelty-based machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable, open-ended agent-based model, where the exploration of an initially small portion of the given state space can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a heterogeneous population of specialized groups of individuals optimized for tracking and killing different phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be used in real-world research and clinical practice.

Toward Engineering Biosystems With Emergent Collective Functions.

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.