Ilya Shmulevich

This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes. It also discusses the inference of model parameters from experimental data and control strategies for driving network behavior towards desirable states. The PBN model is well suited to serve as a mathematical framework to study basic issues dealing with systems-based genomics, specifically, the relevant aspects of stochastic, nonlinear dynamical systems. The book builds a rigorous mathematical foundation for exploring these issues, which include long-run dynamical properties and how these correspond to therapeutic goals; the effect of complexity on model inference and the resulting consequences of model uncertainty; altering network dynamics via structural intervention, such as perturbing gene logic; optimal control of regulatory networks over time; limitations imposed on the ability to achieve optimal control owing to model complexity; and the effects of asynchronicity. The authors attempt to unify different strands of current research and address emerging issues such as constrained control, greedy control, and asynchronicity.

Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.

Microarray technology provides researchers in the life sciences with a revolutionary tool for measuring gene expression. However, this highly developed process involves multiple steps, from sample selection to data analysis, each susceptible to potentially costly errors. Without sound quality control, experimental microarrays may produce useless or, even worse, misleading results. Microarray Quality Control provides a comprehensive resource for ensuring quality control in every step of this complex process. From experimental design to data processing, analysis, and interpretation, the emphasis in this text remains on practical advice for each stage of planning and running a microarray study. Chapters cover: *Quality of biological samples *Quality of DNA *Hybridization protocols Scanning *Data acquisition *Image analysis *Data analysis Written for the broad group of workers–biologists, mathematicians, statisticians, engineers, physicians, and computational scientists–involved in microarray studies, Microarray Quality Control features a straightforward style easily accessed by various disciplines. Useful checklists and tips help ensure the integrity of results, and each chapter contains a thorough review of pertinent literature. The only complete, systematic treatment of the topic available, Microarray Quality Control offers students and practitioners an invaluable resource for improving experimental quality and efficiency.