We are building frontier virtual modeling and simulation technologies that are loved by biologists of all kinds, from classical to computational.
Decoding biology and understanding how it works is one of the future frontiers in scientific research. The potential is enormous, from curing disease to climate resistant crops and manufacturing superior biological materials.
Yet, we are still far away from illuminating the inner workings of biological cells and their interactions. This is because biology is a dynamic, non-linear system of proteins, enzymes, metabolites, molecules and cells interacting with each other. This system is constantly evolving and responding to perturbations, changes and external factors.
For example, an average of 86% of drugs fail in clinical trials and it costs USD 2 billion to bring a drug to the market. The key reasons for this high failure rate is a lack of understanding of the underlying (disease) biology and the mechanism of action of the drug on a patient level.
We believe that classical and computational biologists need to work hand-in-hand to truly advance our understanding of biology. This is why we design intuitive, web-based user interfaces for scientists to build and collaborate on models and to simulate and experiment with them.
For simulating models, we are building the world’s most powerful and versatile simulation engine called Endogenics. We developed from first principles a framework to integrate different biological mechanisms and laws and evolve them over time. This framework is able to handle highly non-linear dynamics and extreme variation in rates, often observed in biology between different mechanisms. It is further designed to be suitable for modern high performance computing architectures to deal with large scale models and simulations.
Currently, accessible tools and frameworks are missing to build large scale mechanism based models. To do so, three fundamental challenges need to be addressed:
We have solved points 1 and 2 with a framework that is ready to integrate key mechanisms including:
Combined with an intuitive user interface to organize, modify and investigate models and simulations, we can start accelerating the journey towards solving the third challenge: filling the knowledge gaps.
Efforts are currently underway to build the AI Virtual Cell. Despite the tremendous capability of machine learning to identify patterns, signals and trends, it lacks causal explainability and is constrained by the data it is trained on. In particular, when applied to temporal evolution, like disease trajectories, these models cannot predict future states, except statistically.
A complementary, hybrid approach is necessary, leveraging the capabilities of machine learning to identify patterns and trends from large amounts of data and combining it with mechanism based models for causal explainability to generate new biological knowledge.
With implementing the framework and infrastructure for large scale mechanism based and AI enhanced models, building the virtual whole cell comes a large step closer to reality.
Now you can let your imagination run wild with what we can do with a whole cell model, and even a bunch of them interacting with each other, that we can explore and experiment with …