Biological Pathway Modeling

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The team at Amber Biology has a great deal of experience in the development of agent and rule-based stochastic models of biological pathways, and also in the development of the computational tools needed to create them and analyze their results.  We have built and calibrated stochastic models of a pathway involved in sepsis, and are currently working with a commercial client to integrate it into their R&D program.

We see modeling and simulation as an invaluable adjunct to any kind of laboratory-based, life science R&D program. Beyond a model’s ability to make predictions, having a model of the biological pathway you are studying, significantly enhances your ability to generate and test hypotheses and design new laboratory experiments. The model itself can also serve as a vehicle for communication and the discussion of ideas.

R&D programs typically generate a great deal of data, but these data seldom constitute actionable ”knowledge" in an R&D context. Organized within the conceptual framework of a model however, these observations can become a foundation for reasoning about the data from your studied system at a higher, biological level - moving the conversation from the microscopic domain of for example, molecules, kinetic rates, affinities and so on - to considerations of cellular responses, biological dysfunction and disease states.

Significant insights can be gained from having a working, dynamic model of the biological pathways that you are studying. With the rule-based, stochastic models we have already developed for our clients, we have been able to demonstrate key features of the biological systems that they were studying as part of their R&D programs. These have included for example, the detailed chain of cause-and-effect by which a clinically-observed deficiency in a particular regulatory molecule can lead to a disease state, as well as a biomarker for that disease state that was derived from the relative levels of production of certain metabolites generated by the biological pathway.

Our models have also enabled our clients to do some very interesting, virtual "what-if" experiments. What if the activity of this regulatory protein was only at 50% of its level in normal, healthy cells? How would the response of this pathway be different in a cell where a particular gene was deficient or knocked out?

Traditional deterministic models built using ordinary differential equations (ODEs), are relatively opaque with respect to the biology that they represent, as well as being very difficult to edit, debug or maintain. The combinatorial complexity of biological systems also severely limits the application of ODE models to biology, forcing a choice between scope or resolution in the models that can be built using them. For these reasons, we prefer to model biological systems using rule and agent-based approaches, that also allow for stochastic simulations of the syytem being studied.