Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels

• URL: http://arxiv.org/abs/2412.06993v1
• Date: Mon, 09 Dec 2024 20:59:59 GMT
• Title: Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels
• Authors: Le Song, Eran Segal, Eric Xing,
• Abstract summary: We present an approach of using AI to model and simulate biology and life. An AI-Driven Digital Organism (AIDO) is a system of integrated multiscale foundation models. We envision that an AIDO is poised to trigger a new wave of better-guided wet-lab experimentation.
• Score: 37.64318681235737
• License:
• Abstract: We present an approach of using AI to model and simulate biology and life. Why is it important? Because at the core of medicine, pharmacy, public health, longevity, agriculture and food security, environmental protection, and clean energy, it is biology at work. Biology in the physical world is too complex to manipulate and always expensive and risky to tamper with. In this perspective, we layout an engineering viable approach to address this challenge by constructing an AI-Driven Digital Organism (AIDO), a system of integrated multiscale foundation models, in a modular, connectable, and holistic fashion to reflect biological scales, connectedness, and complexities. An AIDO opens up a safe, affordable and high-throughput alternative platform for predicting, simulating and programming biology at all levels from molecules to cells to individuals. We envision that an AIDO is poised to trigger a new wave of better-guided wet-lab experimentation and better-informed first-principle reasoning, which can eventually help us better decode and improve life.

Related papers

• Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence [0.0]
  The pursuit of creating artificial intelligence mirrors our longstanding fascination with understanding our own intelligence. Recent advances in AI hold promise, but singular approaches often fall short in capturing the essence of intelligence. This paper explores how fundamental principles from biological computation can guide the design of truly intelligent systems.
  arXiv  Detail & Related papers  (2024-11-22T02:55:39Z)
• How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities [46.671834972945874]
  We propose a vision of leveraging advances in AI to construct virtual cells. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration.
  arXiv  Detail & Related papers  (2024-09-18T02:41:50Z)
• Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [129.08019405056262]
  Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial Intelligence (AGI) MLMs andWMs have attracted significant attention due to their remarkable perception, interaction, and reasoning capabilities. In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
  arXiv  Detail & Related papers  (2024-07-09T14:14:47Z)
• Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency [3.9233394969004713]
  Fusion Intelligence (FI) is a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms are integrated with the computational power of Artificial Intelligence (AI) We demonstrate FI's potential to enhance agricultural IoT system performance through a simulated case study on improving insect pollination efficacy (entomophily)
  arXiv  Detail & Related papers  (2024-05-16T02:10:30Z)
• Empowering Biomedical Discovery with AI Agents [15.125735219811268]
  We envision "AI scientists" as systems capable of skeptical learning and reasoning. Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
  arXiv  Detail & Related papers  (2024-04-03T16:08:01Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines [0.0]
  We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events.
  arXiv  Detail & Related papers  (2022-12-20T22:26:40Z)
• The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents [51.94554095091305]
  We argue for an introspective agent, which considers its own abilities in the context of its environment. Just as in nature, we hope to reframe strategy as one tool, among many, to succeed in an environment.
  arXiv  Detail & Related papers  (2022-01-02T20:14:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.