Merrimack uses Biochemical Network Modeling to gain a thorough, quantitative understanding of cell signaling pathways. Our objective is to quantitatively understand the importance of the individual targets within the pathway, including redundancy, cross-talk and heterogeneity that might alter the effectiveness of a therapeutic.

When considering which target to pursue and how to pursue it, several issues must be considered, including:

• Which is the best target?
• Is there built-in redundancy that would affect the activity of the drug?
• Is there signal amplification or attenuation in the system that may affect the potency of the drug?
• How might heterogeneity, such as differences between cells in a tumor or between different patients, affect the activity of the drug?

We create detailed biochemical models that are ideally suited for answering these questions.

Computational models rely on experimental data. Merrimack differs from many companies in that it has produced the tools to generate quantitative, precise experimental data to populate its models. These tools include our proprietary antibody microarrays that facilitate the generation of very large, homogeneous datasets on protein concentrations and activation. These datasets are crucial for the construction and validation of accurate, quantitative models.

Once a network model is constructed and populated, Merrimack conducts a variety of in silico analyses to identify the critical “nodes” in the system–those targets that require the least inhibition to achieve the desired effect. Biochemical Network Models are also employed to consider important issues such as the effect of network heterogeneity for example, the overexpression of one or more kinases, and incorporate them into the process of validating a therapeutic target.

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