Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.

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Chimerism analysis after allogeneic stem cell transplantation (allo-SCT) is an important diagnostic tool for the documentation of engraftment, early detection of graft failure, and recurrence of the disease. Current assays rely on the genetic polymorphism between the donor and the recipient, and allow semiquantitative or quantitative analysis of chimerism. The most common method in use is based on the amplification of the short tandem repeats (STR). This method, with 1% to 5 sensitivity, is useful for the documentation of engraftment, but is insufficient for the detection of minimal residual disease or early relapse, when medical intervention is urgently needed. Recently, single-nucleotide polymorphism (SNP) has been suggested as an alternative, more accurate system to monitor chimerism. The purpose of our study was to develop an easy, economical, and sensitive method for the detection of chimerism following allo-SCT using the SNP technology. Our approach is based on SNP patient-specific quantitative real-time polymerase chain reaction (PCR) using nonlabeled primers. Our results show that this allele-specific SNP real-time PCR approach is sensitive, relatively cheap, and offers a fast and reliable assay for the monitoring of hematopoietic engraftment and for the detection of minimal residual disease in patients after allo-SCT.

Over the past decade, multi-level complex behavior and reactive nature of biological systems, has been a focus point for the biomedical community. We have developed a computational approach, termed Reactive Animation (RA) for simulating such complex biological systems. RA is an approach for describing the dynamic characteristics of biological systems based on facts collected from experiments. These data are integrated bottom-up by computational tools and methods for reactive systems development and are simulated concomitantly to a front-end user friendly visualization and reporting systems. Using RA, the experimenter may intervene mid-simulation, suggest new hypotheses for cellular and molecular interactions, apply them to the simulation and observe their resulting outcomes "on-line". Several RA models have been developed including models of T cell activation, thymocyte development and pancreatic organogenesis, which are describe in the in this review.

Vygotsky's notions of Zone of Proximal Development and Dynamic Assessment emphasize the importance of personalized learning that adapts to the needs and abilities of the learners and enables more efficient learning. In this work we introduce a novel adaptive learning engine called E-gostky that builds on these concepts to personalize the learning path within an e-learning system. E-gostky uses machine learning techniques to select the next content item that will challenge the student but will not be overwhelming, keeping students in their Zone of Proximal Development. To evaluate the system, we conducted an experiment where hundreds of students from several different elementary schools used our engine to learn fractions for five months. Our results show that using E-gostky can significantly reduce the time required to reach similar mastery. Specifically, in our experiment, it took students who were using the adaptive learning engine 17% less time to reach a similar level of mastery as of those who didn't. Moreover, students made greater efforts to find the correct answer rather than guessing and class teachers reported that even students with learning disabilities showed higher engagement.