The in silico digitalization of real-world scenarios through digital twins and advanced simulations makes it possible to understand the internal dynamics of any system under study and predict its behaviour over time. These tools enable the evaluation of how a system evolves under natural conditions or how it responds to any induced intervention, going beyond the limitations of conventional predictive models. This facilitates the optimization of experiments, resource savings and the generation of predictive knowledge in many areas of life sciences and beyond.
To build the simulators that recreate the digital twin of a given case, we use different technologies, with a special focus on Membrane Computing —a natural computing paradigm inspired by the organisation and functioning of biological systems. This approach allows the modelling of complex processes through dynamics with nested levels of complexity (molecular, cellular, tissue, individual, population, etc.), where phenomena occurring at one level can impact higher-level structures.
These approaches make it possible to explore highly diverse scenarios, evaluate responses to interventions, optimise complex systems in controlled and safe environments and generate detailed predictions that are impossible to achieve with traditional methods.
Data from sensors, bioassays, omics experiments or laboratory measurements are collected, creating a solid foundation for the digital representation of the real system.
The data are integrated into computational models (in the case of Membrane Computing, known as P systems) or algorithms that reproduce the processes to be simulated.
Using artificial intelligence and machine learning, the digital twin identifies patterns, predicts outcomes and helps make faster and more accurate decisions in research, development or production.
Anticipate experimental results, genetic variations or changes in complex biological processes through high-precision simulations.
Reduce time and experimental costs by virtually testing conditions, parameters or treatments before implementing them in the lab or in production.
Enable rapid hypothesis iteration, generation of predictive knowledge and shorter scientific or biotechnological development cycles.
Connect predictive models and advanced analytics to support strategic decisions in research, healthcare, agriculture or bioindustry.
Combine machine learning, molecular modelling and omics data simulation to build connected digital biological ecosystems.
The systems we can model include: microbial resistance dynamics, genetic diseases, metabolic and immune responses to infections, drugs or vaccines, natural and social ecosystems, industrial processes, extreme weather phenomena (DANAs, earthquakes, wildfires), pandemics and epidemics, strategic defence scenarios and risk models, among many others.
The digital twin makes it possible to virtually and highly accurately recreate the human body, generating a model that mimics both its physical structure and its biological functions. Through this simulation, it becomes possible to analyse, predict and visualise how the organism would respond to the administration of a vaccine before it is applied in reality.
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The digital twin makes it possible to digitally recreate the human body to analyse and predict how it will respond to a vaccine before it is actually administered.
The digital twin simulates the dynamics of resistance genes, plasmids and bacteria across different levels of the ecosystem (hosts, reservoirs and environments), facilitating risk assessment and the design of control strategies without the need for direct experiments in the real world.
If you are interested in getting more details about Digital Twins, please contact us at biotechvana@biotechvana.com so we can design a tailor-made solution for your project.
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