Learn how Mitsubishi Electric used Dyad and SciML to build a high-fidelity HVAC model that predicts refrigerant mass with less than 2% error.

YouTube video by JuliaHub

YouTube video by JuliaHub

High-Performance Scientific Modeling with Julia and SciML - SciML/Julia_Modeling_Workshop

Open Source Software for Scientific Machine Learning

We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and sto…

If you work in #controls, you know the struggle—real-time systems need C, right? Not anymore! With #JuliaLang v1.12, you can compile high-level #code directly…

Наукове машинне навчання (SciML) — це нова дисципліна, що об'єднує машинне навчання, науку про дані та обчислювальне моделювання. SciML використовує потужні алгоритми для прискорення та покращення нау...

Learn how to model and simulate hydraulic actuators in Julia using ModelingToolkit.jl—covering steady-state and dynamic system behavior in one session.

Are you interested in bringing the latest data-driven approaches to interdisciplinary topics like Robotics, Neuroscience, Healthcare, and beyond? We’ve got six…

A smarter way to model complex systems! #ModelingToolkit.jl merges symbolic & numeric computing for faster, scalable simulations. From #EV design to #biotech…

Addressing the challenge of achieving a global circular bioeconomy requires efficient and robust bio-based processes operating at different scales. These processes should also be competitive replacements for the production of chemicals currently obtained from fossil resources, as well as for the production of new-to-nature compounds. To that end, genetic circuits can be used to control cellular behavior and are instrumental in developing efficient cell factories. Whole-cell biosensors harbor circuits that can be based on allosteric transcription factors (TFs) to detect and elicit a response depending on the target molecule concentrations. By modifying regulatory elements and testing various genetic components, the responsive behavior of genetic biosensors can be finely tuned and engineered. While previous models have described and characterized the behavior of naringenin biosensors, additional data and resources are required to predict their dynamic response and performance in different contexts, such as under various gene expression regulatory elements, media, carbon sources, or media supplements. Tuning these conditions is pivotal in optimizing biosensor design for applications operating in varying conditions, such as fermentation processes. In this study, we assembled a library of FdeR biosensors, characterized their performance under different conditions, and developed a mechanistic model to describe their dynamic behavior under reference conditions, which guided a machine learning-based predictive model that accounts for context-dependent dynamic parameters. Such a Design-Build-Test-Learn (DBTL) pipeline allowed us to determine optimal condition combinations for the desired biosensor specifications, both for automated screening and dynamic regulation. The findings of this work contribute to a deeper understanding of whole-cell biosensors and their potential for precise measurement, screening, and dynamic regulation of engineered production pathways for valuable molecules.

YouTube video by The Julia Programming Language