Biological Engineering: What does it mean? Where does it (need to) go?

TL;DR

This paper explores the multifaceted field of biological engineering, discussing its current state, challenges, opportunities, and future directions, emphasizing data-driven methods and education in AI and mathematics.

Contribution

It provides a comprehensive overview of biological engineering, categorizes its main approaches, and highlights the role of AI and education in advancing the field.

Findings

Data-driven discovery can address modeling gaps.

AI integration enhances biological engineering design.

Interdisciplinary education is crucial for future biological engineers.

Abstract

Biological engineering, the convergence between engineering and biology, is at the forefront of significant advances in healthcare, agriculture, and environmental sustainability, making it highly relevant to current scientific and societal challenges. We take a comprehensive look at this broad and interdisciplinary domain, structure it into three main areas - bioinspired, biological and biohybrid approaches - and dissect inherent and fundamental challenges along with opportunities, highlighting specific examples. We describe how data-driven discovery and design, in conjunction with artificial intelligence, can mitigate the absence of reductionist models in these areas. Additionally, we address the education of a new generation of biological engineers, emphasizing mathematical, technical, and artificial intelligence frameworks.