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.

Genomic Signal Processing (Princeton Series in Applied Mathematics)

Science & Math

Dougherty, Edward R.

Shmulevich, Ilya

Evolution

Princeton University Press

The first comprehensive treatment of probabilistic Boolean networks (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. As the PBN model can serve as a mathematical framework for studying the basic issues of systems-based genomics, 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 unify different strands of current research and address emerging issues such as constrained control, greedy control, and asynchronicity.

Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks

Biological Sciences

Society for Industrial and Applied Mathematics

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