The applicability of computational models to the biological world is an
active topic of debate. We argue that a useful path forward results from
abandoning hard boundaries between categories and adopting an
observer-dependent, pragmatic view. Such a view dissolves the contingent
dichotomies driven by human cognitive biases (e.g., tendency to oversimplify)
and prior technological limitations in favor of a more continuous, gradualist
view necessitated by the study of evolution, developmental biology, and
intelligent machines. Efforts to re-shape living systems for biomedical or
bioengineering purposes require prediction and control of their function at
multiple scales. This is challenging for many reasons, one of which is that
living systems perform multiple functions in the same place at the same time.
We refer to this as "polycomputing" - the ability of the same substrate to
simultaneously compute different things. This ability is an important way in
which living things are a kind of computer, but not the familiar, linear,
deterministic kind; rather, living things are computers in the broad sense of
computational materials as reported in the rapidly-growing physical computing
literature. We argue that an observer-centered framework for the computations
performed by evolved and designed systems will improve the understanding of
meso-scale events, as it has already done at quantum and relativistic scales.
Here, we review examples of biological and technological polycomputing, and
develop the idea that overloading of different functions on the same hardware
is an important design principle that helps understand and build both evolved
and designed systems. Learning to hack existing polycomputing substrates, as
well as evolve and design new ones, will have massive impacts on regenerative
medicine, robotics, and computer engineering.

我们认为将硬分类界限视为暂时的、观察者相关的、实用主义的观点是有用的，这种观点有助于我们理解生物与技术中的多计算、多功能原则，并有助于我们理解和设计演化和设计系统的方式。

这里有很多空间：生物系统作为进化过载的多尺度机器

There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines

Proteins are macromolecules that perform essential functions in all living
organisms. Designing novel proteins with specific structures and desired
functions has been a long-standing challenge in the field of bioengineering.
Existing approaches generate both protein sequence and structure using either
autoregressive models or diffusion models, both of which suffer from high
inference costs. In this paper, we propose a new approach capable of protein
sequence and structure co-design, which iteratively translates both protein
sequence and structure into the desired state from random initialization, based
on context features given a priori. Our model consists of a trigonometry-aware
encoder that reasons geometrical constraints and interactions from context
features, and a roto-translation equivariant decoder that translates protein
sequence and structure interdependently. Notably, all protein amino acids are
updated in one shot in each translation step, which significantly accelerates
the inference process. Experimental results across multiple tasks show that our
model outperforms previous state-of-the-art baselines by a large margin, and is
able to design proteins of high fidelity as regards both sequence and
structure, with running time orders of magnitude less than sampling-based
methods.

本文提出了一种基于上下文特征的三角函数编码器和旋转平移等变解码器，能够在随机初始化的基础上迭代地将蛋白质序列和结构转化为所需状态，从而设计高保真度的蛋白质的新方法。实验结果表明，该方法在多项任务上均优于现有基线算法，而且比基于抽样的方法更快且可靠。

使用等变平移技术进行蛋白质序列和结构联合设计

Protein Sequence and Structure Co-Design with Equivariant Translation

Proteins are macromolecules that mediate a significant fraction of the
cellular processes that underlie life. An important task in bioengineering is
designing proteins with specific 3D structures and chemical properties which
enable targeted functions. To this end, we introduce a generative model of both
protein structure and sequence that can operate at significantly larger scales
than previous molecular generative modeling approaches. The model is learned
entirely from experimental data and conditions its generation on a compact
specification of protein topology to produce a full-atom backbone configuration
as well as sequence and side-chain predictions. We demonstrate the quality of
the model via qualitative and quantitative analysis of its samples. Videos of
sampling trajectories are available at this https URL .

本文引入了一种生成模型，旨在设计具有特定 3D 结构和化学性质的蛋白质，以实现特定的功能。通过实验数据全面学习，该模型可生成全原子骨架构象以及序列和侧链预测，实现了分子生成建模方法的显著扩大。