Samantha Kleinberg

Systems biology via redescription and ontologies (I): finding phase changes with applications to malaria temporal data.

Biological systems are complex and often composed of many subtly interacting components. Furthermore, such systems evolve through time and, as the underlying biology executes its genetic program, the relationships between components change and undergo dynamic reorganization. Characterizing these relationships precisely is a challenging task, but one that must be undertaken if we are to understand these systems in sufficient detail. One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework. In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings). The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions. This paper describes the algorithms behind GOALIE and its use in the study of the Intraerythrocytic Developmental Cycle (IDC) of Plasmodium falciparum, the parasite responsible for a deadly form of chloroquine resistant malaria. We focus in particular on the problem of finding phase changes, times of reorganization of transcriptional control.

Systems Biology via Redescription and Ontologies (II): A Tool for Discovery in Complex Systems

A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of such visible datasets are time-course description of gene-expression abundance levels, neural spike-trains, or click-streams for web pages. It has now become rather effortless to collect voluminous datasets of this nature; but how can we make sense of them and draw significant conclusions? For instance, in the case of time-course gene-expression datasets, rather than following small sets of known genes, can we develop a holistic approach that provides a view of the entire system as it evolves through time?