Part I: Overview -- Advances in computational systems biology / Huma M. Lodhi -- Part II: Biological network modeling -- Models in systems biology : the parameter problem and the meanings of robustness / Jeremy Gunwardena -- In silico analysis of combined therapeutics strategy for heart failure / Sun-Young Shin, Tae-Hwan Kim, Kwang-Hyun Cho [and others] -- Rule-based modeling and model refinement / Elaine Murphy, Vincent Danos, Jérôme Feret [and others] -- A (natural) computing perspective on cellular processes / Matteo Cavaliere, Tommaso Mazza -- Simulating filament dynamics in cellular systems / Wilbur E. Channels, Pablo A. Iglesias -- Part III: Biological network inference -- Reconstruction of biological networks by supervised machine learning approaches / Jean-Philippe Vert -- Supervised inference of metabolic networks from the integration of genomic data and chemical information / Yoshihiro Yamanishi -- Integrating abduction and induction in biological inference using CF-induction / Yoshitaka Yamamoto, Katsumi Inoue, Andrei Doncescu -- Analysis and control of deterministic and probabilistic Boolean networks / Tatsuya Akutsu, Wai-Ki Ching -- Probabilistic methods and rate heterogeneity / Tal Pupko, Itay Mayrose -- Part IV: Genomics and computational systems biology -- From DNA motifs to gene networks : a review of physical interaction models / Panayiotis V. Benos, Alain B. Tchagang -- The impact of whole genome in silico screening for nuclear receptor-binding sites in systems biology / Carsten Carlberg, Merja Heinäniemi -- Environmental and physiological insights from microbial genome sequences / Alessandra Carbone, Anthony Mathelier -- Part V: Software tools for systems biology -- Ali baba : a text mining tool for systems biology / Jörg Hakenberg, Conrad Plake, Ulf Leser -- Validation issues in regulatory module discovery / Alok Mishra, Duncan Gillies -- Computational imaging and modeling for systems biology / Ling-Yun Wu, Xiaobo Zhou, Stephen T.C. Wong.

Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.