Tyler O'Neal, Staff Editor LATEST

From DNA to digital twins: New MDNA framework brings AI supercomputing closer to whole-cell simulation

The next major breakthrough in computational biology may not come from a new supercomputer, but from the software that allows scientists to harness one.
 
Researchers have introduced MDNA, a new open-source molecular modeling framework designed to generate, manipulate, and analyze complex DNA structures with unprecedented flexibility. While the software itself is a biological modeling tool, its broader significance lies in how it could accelerate large-scale molecular simulations, AI-driven biological discovery, and ultimately the long-standing ambition of constructing digital twins of living systems.
 
At a time when exaflops computing is transforming fields ranging from climate science to astrophysics, biology is increasingly becoming one of the most computationally demanding scientific disciplines. DNA is no longer viewed simply as a sequence of genetic letters. It is a dynamic three-dimensional structure whose geometry, interactions, and modifications influence everything from gene expression to disease progression.
 
Understanding those behaviors requires simulation at an extraordinary scale.

Building better starting points for supercomputing

Modern molecular dynamics simulations can model systems containing millions, or even billions, of atoms. Researchers have already demonstrated billion-atom DNA simulations on leadership-class supercomputers, revealing how genes fold, interact, and regulate biological activity.
 
However, one persistent challenge has been constructing biologically realistic DNA configurations suitable for large-scale simulation.
 
MDNA addresses that bottleneck.
 
The framework enables researchers to generate DNA structures with arbitrary shapes using spline-based modeling techniques, while also supporting biologically important modifications such as DNA methylation, Hoogsteen base-pair transitions, and non-canonical nucleotide configurations. By integrating structure generation and structural analysis within a single Python-based workflow, the software streamlines the creation of simulation-ready DNA systems.
 
The result is a platform that reduces the time required to translate a biological hypothesis into a computational experiment.

Bridging AI and molecular simulation

One of the most compelling aspects of MDNA is its compatibility with many computational tools already used across the molecular simulation community.
 
The software integrates with established platforms such as OpenMM, MDAnalysis, MDTraj, oxDNA, Bio3D, HTMD, and PLUMED, making it easier to connect AI-generated molecular designs with high-performance simulation workflows. According to the authors, the goal is not merely to construct DNA structures, but to enable a complete computational ecosystem for studying DNA-protein interactions and molecular dynamics.
 
This arrives at a pivotal moment for computational biology.
 
Artificial intelligence is increasingly being used to design biological molecules, predict molecular structures, and explore vast biochemical design spaces. Recent advances have demonstrated AI-driven approaches to genetic circuit design and biomolecular engineering, generating datasets and candidate structures at scales impossible for human researchers alone.
 
Yet AI predictions are only the beginning.
 
Before a new biological design can be trusted, it often must be validated through detailed molecular simulations that capture physical behavior at atomic resolution. These simulations frequently require the resources of modern supercomputing facilities.
 
MDNA provides a bridge between those two worlds.

Toward digital twins of biology

The implications extend well beyond DNA modeling.
 
Scientists increasingly envision a future in which entire biological systems can be represented as computational “digital twins,” virtual counterparts capable of predicting molecular behavior, disease progression, or therapeutic outcomes before laboratory experiments are performed.
 
Recent projects have mapped the four-dimensional organization of the human genome with unprecedented detail, identifying hundreds of thousands of genomic interactions across time and space.
 
At the same time, researchers are developing computational frameworks capable of simulating cellular processes, cancer evolution, and molecular communication networks.
 
Such ambitions depend on accurate structural models as foundational inputs.
 
MDNA represents one piece of that larger puzzle: a software layer capable of generating realistic DNA architectures that can be incorporated into increasingly sophisticated simulations.

The road to whole-cell simulation

Perhaps the most inspiring aspect of the work is what it suggests about the future.
 
For decades, biologists have dreamed of creating computational models capable of simulating entire living cells. Achieving that goal requires integrating DNA, proteins, RNA, membranes, molecular machinery, and environmental interactions into unified computational frameworks.
 
Exaflops supercomputers are beginning to provide the raw computational horsepower needed for such efforts. Yet hardware alone is insufficient. Researchers also require software capable of building, organizing, and analyzing the immense biological structures that those machines will simulate.
 
MDNA helps fill that gap.
 
By simplifying the construction of highly detailed DNA systems and integrating them with modern simulation ecosystems, the framework contributes to the growing software infrastructure underpinning next-generation computational biology.

A new era for computational life sciences

While the history of supercomputing is often defined by raw hardware power, scientific progress increasingly relies on the sophisticated software frameworks that translate that capacity into actionable insight.
 
MDNA exemplifies this shift: although it may not be the largest or most intensive platform, its value lies in its ability to bridge the gap between AI-driven discovery and large-scale molecular simulation.
 
By simplifying the complexity of DNA modeling, MDNA provides a vital tool for the long-term goal of building biological digital twins.
 
As we enter the exaflops era, such software will be indispensable, proving that while the future of life sciences is written in DNA, it will be mapped through the power of advanced computational modeling.
Microscope image of a semiconductor-integrated spintronic test chip developed by researchers at Tohoku University and NIST. The device demonstrates the first silicon-integrated probabilistic bit (p-bit), a key building block for future large-scale probabilistic computers designed for AI and optimization workloads.
Microscope image of a semiconductor-integrated spintronic test chip developed by researchers at Tohoku University and NIST. The device demonstrates the first silicon-integrated probabilistic bit (p-bit), a key building block for future large-scale probabilistic computers designed for AI and optimization workloads.

Silicon spintronics brings the P-computer closer to reality

Deck LATEST

Tohoku University and NIST demonstrate the world’s first semiconductor-integrated spintronic p-bit, opening a path toward million-bit probabilistic computers for AI and optimization

Efforts to develop computing architectures that surpass conventional CMOS and modern AI accelerators have just advanced significantly. A team from Tohoku University and the National Institute of Standards and Technology (NIST) has created the world’s first semiconductor-integrated spintronic probabilistic bit (p-bit), fabricated directly on a silicon chip using standard semiconductor manufacturing methods. This breakthrough tackles one of the main challenges in probabilistic computing: scalability.
 
For the high-performance computing community, this result is especially compelling. It marks a shift from laboratory prototypes using discrete components to integrated architectures that could ultimately support large-scale AI and optimization workloads.

A different path beyond Moore’s Law

As AI models continue to grow, traditional processors increasingly struggle with the energy costs associated with searching enormous solution spaces. While quantum computing remains a promising long-term approach, researchers worldwide are also exploring alternative architectures that can handle probabilistic calculations more efficiently.
 
One such architecture is the probabilistic computer, or p-computer.
 
Unlike conventional bits that are fixed at either 0 or 1, p-bits fluctuate stochastically between the two states. Their probability distributions can be controlled and correlated with neighboring p-bits, allowing entire networks to explore many possible solutions simultaneously. This makes p-computers particularly attractive for combinatorial optimization, sampling, machine learning, and inference problems.
 
Previous demonstrations of spintronic p-computers relied on separate spin devices connected to external control electronics through cables. Those systems successfully validated the concept but were limited to roughly 100-bit-scale experiments and offered little opportunity for the kind of integration required for practical computing systems.

Integrating spintronics directly into silicon

The new work changes that equation.
 
The research team fabricated a p-bit circuit directly on a silicon substrate by combining advanced semiconductor manufacturing techniques in the United States with spintronic device fabrication performed at Tohoku University. The resulting device integrates CMOS circuitry with a superparamagnetic tunnel junction whose magnetic state fluctuates naturally due to thermal effects.
 
The prototype was fabricated using a 130-nanometer CMOS process and experimentally verified to exhibit the expected probabilistic input-output behavior required for p-bit operation. The work was reported in IEEE Electron Device Letters https://ieeexplore.ieee.org/document/11535457/.
 
While a single p-bit may appear modest compared with modern processors containing billions of transistors, the accomplishment is significant because it demonstrates a manufacturing pathway compatible with semiconductor-scale integration.
 
According to the researchers, this foundational technology could eventually enable systems containing on the order of one million p-bits, representing a dramatic leap beyond current demonstrations.

Why supercomputing researchers should pay attention

For the HPC community, probabilistic computing occupies an increasingly interesting niche between traditional computing and quantum computing.
 
Many computational science problems involve searching extremely large solution spaces:
  • Protein folding and molecular sampling
  • Logistics and routing optimization
  • Bayesian inference
  • Machine learning training and inference
  • Statistical physics simulations
  • Financial risk modeling
These workloads often consume enormous amounts of compute time on today’s GPU-powered supercomputers.
 
P-computers are not quantum computers and do not rely on fragile quantum coherence. Instead, they exploit naturally occurring randomness in physical devices, operate at room temperature, and use mature semiconductor manufacturing techniques.
 
If large-scale p-bit arrays become practical, they could emerge as specialized accelerators analogous to GPUs or AI tensor processors, targeting classes of problems where stochastic search and probabilistic inference dominate computational cost.

The spintronics connection

The work also highlights the growing importance of spintronics as a post-CMOS technology platform.
 
Spintronic devices use both the charge and the spin of electrons, enabling information processing mechanisms that differ fundamentally from traditional transistor logic. Technologies such as MRAM have already demonstrated that spintronic devices can be manufactured within semiconductor production flows.
 
The ability to combine CMOS circuitry and stochastic magnetic tunnel junctions on the same silicon substrate suggests that future p-computers could leverage existing semiconductor infrastructure rather than requiring entirely new fabrication ecosystems.
 
That compatibility may prove decisive in determining whether probabilistic computing remains a research curiosity or evolves into a deployable computing technology.

A new computing landscape

The history of computing is filled with architectures that appeared promising but never escaped the laboratory. What makes this announcement noteworthy is not merely the demonstration of another novel device, but the demonstration of a manufacturable pathway.
 
The researchers have effectively shown that spintronic p-bits can be integrated into semiconductor processes rather than attached as external experimental components. That shift transforms probabilistic computing from a proof-of-concept architecture into a technology with a plausible scaling roadmap.
 
For supercomputing researchers watching the search for post-Moore computing platforms, the question is no longer whether spintronic p-bits can operate on silicon. That has now been demonstrated.
 
The more interesting question is what happens when hundreds of thousands, or even millions, of them begin working together.

AI breaks conservation barriers: Australia’s Wildlife Observatory leverages supercomputing to protect biodiversity

O’NEAL LATEST
Australia’s biodiversity crisis has evolved into a data challenge as much as an ecological one.
 
Across the continent, thousands of wildlife monitoring cameras quietly capture millions of images and videos each year, documenting everything from endangered marsupials to invasive predators. 
 
While these camera traps have transformed ecological research, they have also created an unexpected problem: researchers are drowning in data.
 
Now, scientists at the University of Queensland have unveiled a solution that combines artificial intelligence, cloud computing, and large-scale data infrastructure to transform how wildlife monitoring is conducted across Australia.
 
The newly launched Wildlife Observatory of Australia (WildObs) uses AI-powered computer vision systems to analyze millions of camera-trap images, enabling conservationists to identify species, detect ecological changes, and respond to threats far faster than traditional methods allow. The platform represents a significant step toward data-driven conservation at the national scale.

Turning millions of images into actionable science

Affordable camera traps have become ubiquitous tools for ecological research. Mounted to trees and left in the field for months at a time, they continuously record wildlife activity across remote forests, deserts, wetlands, and conservation reserves.
 
The result is unprecedented visibility into Australia’s ecosystems, but also an unprecedented analytical burden.
 
According to Associate Professor Matthew Luskin from the University of Queensland’s School of the Environment, researchers have been collecting enormous quantities of ecological data without an efficient means of processing it. WildObs was developed specifically to address this challenge by bringing AI, cloud infrastructure, and collaborative data management together in a single platform.
 
The platform can identify hundreds of Australian species from camera-trap imagery and performs classification tasks approximately ten times faster than human analysts, dramatically reducing the time required to convert raw imagery into usable ecological information.

AI-powered conservation

At the core of WildObs are specialized computer vision models trained on Australian wildlife and environmental conditions.
 
The platform hosts multiple AI classifiers developed by research institutions and conservation organizations, including:
  • WildObs-QCIF image classification models
  • Google’s SpeciesNet platform
  • Australian Wildlife Conservancy’s AWC135 model
  • University of Tasmania species-recognition systems
  • AddaxAI’s Victorian Species Recognition Model
Together, these models create a shared national ecosystem for AI-driven wildlife monitoring.
 
Researchers can upload imagery, run classification workflows in the cloud, and access results through interactive dashboards without requiring advanced machine learning expertise.
 
The result is a practical example of how artificial intelligence is moving beyond laboratory demonstrations and becoming operational infrastructure for environmental science.

Computing infrastructure behind the platform

Although the public focus is often on AI algorithms, the real innovation lies equally in the computing infrastructure supporting them.
 
WildObs is hosted on the ARDC Nectar Research Cloud, providing the storage, processing, and scalability necessary to manage millions of wildlife observations. The platform was developed through collaboration among the University of Queensland, QCIF Digital Research, the Australian Research Data Commons (ARDC), the Terrestrial Ecosystem Research Network (TERN), and international partners including Agouti, Wageningen University, and INBO.
 
This cloud-based architecture allows conservation organizations, universities, government agencies, and non-governmental organizations to access advanced AI capabilities without maintaining their own high-performance computing infrastructure.
 
Instead of downloading software, configuring machine-learning pipelines, and provisioning storage systems, researchers can simply upload images and allow the platform’s computing resources to perform the analysis.

From observation to conservation action

The implications extend well beyond image classification.
 
WildObs enables conservation teams to:
  • Detect rare and elusive species more rapidly.
  • Identify declines in native populations earlier.
  • Evaluate invasive-species management programs.
  • Track changes in biodiversity across large geographic regions.
  • Prioritize conservation resources based on real-time ecological evidence.
In conservation biology, timing can be critical. Species declines often become apparent only after significant population losses have already occurred. By accelerating data processing and analysis,
 
AI systems may provide earlier warning signals and support faster intervention strategies.

A new model for ecological research

One of the most significant aspects of WildObs is its emphasis on collaboration.
 
Historically, wildlife monitoring datasets have been fragmented across institutions, stored in incompatible formats, and difficult to share at scale. WildObs addresses this challenge by creating a common computational environment where data, AI models, and analytical workflows can be accessed by a broad community of researchers and conservation practitioners.
 
The platform also allows external developers to host new species-recognition models, creating an expandable ecosystem that can evolve as AI capabilities improve.
 
This approach mirrors broader trends in scientific computing, where cloud-native research environments increasingly replace isolated data silos.

The growing role of AI in environmental science

WildObs illustrates how artificial intelligence is becoming a foundational tool for ecological research.
 
As environmental monitoring technologies continue to generate larger datasets, from camera traps and acoustic sensors to drones and satellite imagery, the limiting factor is no longer data collection.
 
It is data interpretation.
 
AI systems are uniquely suited to address this challenge because they can process vast quantities of information consistently and at speeds impossible for human researchers alone.
 
For Australia, where biodiversity faces mounting pressure from habitat loss, invasive species, climate change, and environmental fragmentation, the ability to transform data into timely decisions may prove increasingly valuable.

Computing for conservation

The launch of WildObs highlights a broader shift occurring across scientific disciplines: modern discovery increasingly depends on the integration of AI, cloud computing, and large-scale data infrastructure.
 
In this case, advanced computing is not being used to model galaxies or train large language models. It is being deployed to help scientists understand, monitor, and protect living ecosystems.
 
By combining artificial intelligence with national research infrastructure, WildObs demonstrates how computational innovation can directly support conservation outcomes.
 
The challenge facing Australian wildlife is immense. But with AI-powered platforms capable of turning millions of images into actionable ecological intelligence, researchers are gaining a powerful new ally in the effort to preserve biodiversity for future generations.