Crosstalk between dendritic cells (DCs) and T cells plays a crucial role in modulating immune responses in natural and pathological conditions. DC-T cell crosstalk is achieved through contact-dependent (i.e., immunological synapse) and contact-independent mechanisms (i.e., cytokines). Activated DCs upregulate co-stimulatory signals and secrete proinflammatory cytokines to orchestrate T cell activation and differentiation. Conversely, activated T helper cells "license" DCs towards maturation, while regulatory T cells (Tregs) silence DCs to elicit tolerogenic immunity. Strategies to efficiently modulate the DC-T cell crosstalk can be harnessed to promote immune activation for cancer immunotherapy or immune tolerance for the treatment of autoimmune diseases. Here, we review the natural crosstalk mechanisms between DC and T cells. We highlight bioengineering approaches to modulate DC-T cell crosstalk, including conventional vaccines, synthetic vaccines, and DC-mimics, and key seminal studies leveraging these approaches to steer immune response for the treatment of cancer and autoimmune diseases.
How do cellular regulatory networks solve the challenges of life? This article presents computer software to study that question, focusing on how transcription factor networks transform internal and external inputs into cellular response outputs. The example challenge concerns maintaining a circadian rhythm of molecular concentrations. The system must buffer intrinsic stochastic fluctuations in molecular concentrations and entrain to an external circadian signal that appears and disappears randomly. The software optimizes a stochastic differential equation of transcription factor protein dynamics and the associated mRNAs that produce those transcription factors. The cellular network takes as inputs the concentrations of the transcription factors and produces as outputs the transcription rates of the mRNAs that make the transcription factors. An artificial neural network encodes the cellular input-output function, allowing efficient search for solutions to the complex stochastic challenge. Several good solutions are discovered, measured by the probability distribution for the tracking deviation between the stochastic cellular circadian trajectory and the deterministic external circadian pattern. The solutions differ significantly from each other, showing that overparameterized cellular networks may solve a given challenge in a variety of ways. The computation method provides a major advance in its ability to find transcription factor network dynamics that can solve environmental challenges. The article concludes by drawing an analogy between overparameterized cellular networks and the dense and deeply connected overparameterized artificial neural networks that have succeeded so well in deep learning. Understanding how overparameterized networks solve challenges may provide insight into the evolutionary design of cellular regulation.
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