The gene expression matrix: Towards the extraction of genetic network architectures

Abstract

Understanding of complex biological processes requires knowledge of the component molecularelements, as well as the principles that govern the interactions between them in forming higherordered structures. We are founding our laboratory studies of CNS development and celldifferentiation on the integrative concept of a genetic network, based on the tenets of geneticinformation flow. But first it is important to establish the intellectually challenging principles bywhich complex networks of functionally cross-linked elements lead to predictable, higher-orderedbehaviors. To this end we are studying Boolean network models, which exhibit dynamicproperties similar to those of living systems, such as self-organization and cycling. In this model,genes are conceptualized as binary (on/off) elements interacting within a freely cross-wirednetwork. The on/off pattern, or state, of the entire network of genes updates itself as the genesinteract, until the system reaches a final state, the attractor. This process of updating representsthe pattern, or trajectory, of gene expression which results in the mature organism ordifferentiated cell type, representing analogies of the attractor.Since trajectories and attractors are specific expressions of the architecture of a particularsystem, any experimental strategy must gain access to the states of the biological network. In thatcontext, PCR (polymerase chain reaction) is being used to measure the expression of a largevariety genes at different time points in a tissue or experimental cell system in order to gainaccess to data on trajectories. While many alternative trajectories may be obtainedexperimentally during cell and tissue differentiation or responses to perturbation, it is equallyimportant to development the computational tools to infer genetic network architectures fromsuch data sets. Here we discuss a heuristic approach to this problem using examples fromBoolean networks as illustrations. Finally, analysis of experimental data is expected to providetestable hypotheses concerning further interconnections, some of which might not otherwise bepredicted by strict molecular/mechanistic approaches. Especially within light of the massivegenetic tool set generated by the genome projects, one may anticipate that a strategy of large scalegene expression mapping and genetic signaling network inference may become essential to thestudy of complex medical problems such as cancer or tissue regeneration.