Design principles of multi-map variation in biological systems
Complexity in biology is often described using a multi-map architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes that drive evolution. Moreover, natural selection, along with other neutral forces, can modify these maps. At each hierarchical level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and their evolutionary dynamics.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The hardware is the software
Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the microscopic physics of artificial-intelligence hardware and of human biological "hardware" is distinct, …
Neuromorphic Control
Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biological organization of neural systems to develop novel technology for computing, sensing, and actuating. The unique proper…
Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations
While deep neural networks (DNNs) have achieved remarkable performance in tasks such as image recognition, they often struggle with generalization, learning from few examples, and continuous adaptation - abilities inhere…
Few-Shot LearningIntegrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden by automating insulin delivery through advanced control algorithms. Howe…
Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence
The pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to r…