Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms

In a situation where the overall behavior of the system depends on many different components, horizontal integration synthesizes a composite model from several components having the same spatial and/or temporal granularity. In multi-cellular organisms, many cell and tissue behaviors are the consequences of interactions among local components. Integrated system models are expected to be able to uncover (i) precisely how components (at multiple levels) interact, (ii) the magnitudes of emergent properties (multiscale phenomena not explained by the sum total of local mechanisms), and how this emergent behavior can be modeled, (iii) the extent to which different biological circuits conform to common principles of “design”, and (iv) optimal ways to alter cellular and organ phenotypes using pharmacological agents. Discussion Systems integration is the act of assembling a composite system—computer models in QSP—from previously autonomous components. Horizontal integration synthesizes a composite from components having the same spatial and/or temporal granularity. An example might be a cell pathway interaction model composed of various networks within and across different cells in various cell systems, but not including tissue or molecular dynamics. A clear statement of current and future uses to which an integrated system will be put is a precondition of systems integration for scientific research (this represents the requirement of fit-to-purpose for models). A use statement typically begins with the current capabilities of the individual components followed by listing the expected capabilities of the integrated system. It seems unlikely that biological component interactions will be exclusively either horizontal or vertical. We must therefore anticipate that issues of horizontal and vertical component integration will overlap and even merge into a single problem of multi-scale integration. Impact What are the uses of horizontally integrated system models? Such systems will be used as stand-alone software components to study specific networks, and they will also be used as components in larger horizontally and vertically integrated systems. Such uses depend on the driving biological problems. If the integrated model is intended to represent an organism's pharmacological response, for example, then the duration of the response cycle, the number of cycles considered, and required response granularity each become determining aspects. With that in mind, model and component reuse, flexibility, and adaptability, become important and that feeds back into model and component design. Therefore, it should be relatively easy to reconfigure components to represent different mechanistic hypotheses or different aspects of a key attribute under different experimental conditions. It should also be relatively simple to accommodate additional aspects at the current level of granularity or alter usage and assumptions, without requiring significant component or system reengineering. Components should be constructed so that they can be adapted easily to function as components in different, integrated models. By way of example, the ability to target signaling and transcriptional pathways that drive diseases such as cancer will be enhanced by a more global understanding of how these pathways interconnect to create, through feedback and cross-talk mechanisms, the full signaling network that integrates all signals into a net outcome or phenotype (e.g. oncogenic transformations). The broader biomedical research community is searching for the underlying rules that govern signaling, while cancer researchers are simultaneously addressing through technology development the need to measure variations between tumor types, between tumors from different patients, and even within tumors through single cell measurements (e.g. integrated microfluidic-based assays). Using cancer models such as Bcr-Abl driven leukemic transformation, we are already collecting high dimensionality phosphoprofiling signaling data focused specifically on subnetworks that involve the cross-talk between a small numbers of signaling modules. Simplification of the problem through subnetwork analysis, allows us to first focus on a more tractable scale, while retaining clinical relevance, with the hope that