Stefanie Fuhrman

Linear modeling of mRNA expression levels during CNS development and injury.

Large-scale gene expression data sets are revolutionizing the field of functional genomics. However, few data analysis techniques fully exploit this entirely new class of data. We present a linear modeling approach that allows one to infer interactions between all the genes included in the data set. The resulting model can be used to generate interesting hypotheses to direct further experiments.

Mining the gene expression matrix: inferring gene relationships from large scale gene expression data

In order to infer the logical principles underlying biological development and phenotypic change, it is necessary to determine large-scale temporal gene expression patters. To quote Eric Lander, “The mRNA levels sensitively reflect the state of the cell, perhaps uniquely defining cell types, stages, and responses. To decipher the logic of gene regulation, we should aim to be able to monitor the expression level of all genes simultaneously…” (Lander, 1996). One method for accomplishing this involves the use of reverse transcription polymerase chain reaction (RT-PCR) to assay the expression levels of large numbers of genes in a tissue at different time points during development, with a standard protocol. The relative amounts of mRNA produced at these time points provide a gene expression time series for each gene.

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

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.

Spearman correlation identifies statistically significant gene expression clusters in spinal cord development and injury

An important problem in the analysis of large-scale gene expression data is the validation of gene expression clusters. By examining the temporal expression patterns of 74 genes expressed in rat spinal cord under three different experimental conditions, we have found evidence that some genes cluster together under multiple conditions. Using RT-PCR data from spinal cord development and two sets of microarray data from spinal injury, we applied Spearman correlation to identify clusters and to assign P values to pairs of genes with highly similar temporal expression patterns. We found that 15% of genes occurred in statistically significant pairs in all three experimental conditions, providing both statistical and experimental support for the idea that genes that cluster together are co-regulated. In addition, we demonstrated that DNA microarray and RT-PCR data are comparable, and can be combined to confirm gene expression relationships.

Distributivity, a general information theoretic network measure, or why the whole is more than the sum of its parts

Molecular life sciences are rapidly uncovering the elementary building blocks underlying complex biological functions. A common feature encountered at all levels is the organization of basic constituents into distributed, high-dimensional networks. A major challenge lies in understanding the abstract principles that govern the logic of these networks, enabling them to execute complex functions while retaining stability and adaptability (Somogyi and Sniegoski, 1996). Boolean networks provide a model framework that captures these features, serving as a testing ground for establishing novel techniques in computational network analysis.