Computational Systems Biology

Paola Lecca received a M.S. in Theoretical Physics from the University of Trento (Italy) in 1997 and a PhD in Computer Science in 2006 from the International Doctorate School in Information and Communication Technologies at the University of Trento (Italy). Since 1998 she held Researcher and Principal Investigator positions in research centers and in academia. From 1998 to 2000 she was Research Assistant at the Fondazione Bruno Kessler - Center for Information Technologies of Trento by the research unit of Predictive Models for Biomedicine & Environment. From 2001 to 2002 Dr. Lecca worked at the Department of Physics of University of Trento in the area of data manipulation and predictive modelling in research programs of the National Institute of Nuclear Physics. In 2006 she joined to The Microsoft-Research University of Trento Centre for Computational and Systems Biology (COSBI), Italy. At COSBI Dr. Lecca led the group of Data Manipulation and Knowledge Inference. From 2012 to 2015 Dr. Lecca continued her researches at the Laboratory of Computational Oncology of the Centre for Integrative Biology (CIBIO) of University of Trento, Italy. She is currently collaborating with the Department of Mathematics of University of Trento, where she develops optimized techniques of simulation of hybrid (stochastic and deterministic) dynamical biochemical systems.She is a Professional Member of Association for Computing Machinery and author of seventy publications including books and journal and conference papers on international journals in computational biology, bioinformatics, and biophysics. She carries on an intense editorial activity as editor and reviewer for high impact factor journals in these subjects, and leads the organization of school and symposia of bioinformatics.Angela Re is currently Postdoctoral Fellow at the Centre for Integrative Biology (CIBIO) of the University of Trento. She earned her Bachelor Physics in 1999 at the University of Torino. In 2002 she completed her Phd Program in complex systems applied to post-genomic biology, which was inspired by the notion that the breadth and depth of complexity of living systems require that we combine 'entirety of analysis' (-omics approaches) with 'analysis of entirety' (complex systems theory). She has been Postdoctoral Fellow at the CIBIO since 2007. During this time, she developed her interests in computational systems biology by adopting a variety of computational and mathematical tools to analyse molecular, cellular and phenotypic data. In particular, she focused on the study of post-transcriptional regulatory mechanisms, their inclusion in regulatory pathways along with their potential relevance in cancer prognosis. She was interested in methods development for multi-assay omics experiments. She studied biological complexity in the context of the modular organization and dynamics of cellular interaction networks, the "wiring diagrams” displaying which biomolecules in cells regulate which one's activity.Adaoha Ihekwaba is based at the Gut Health and Food Safety, Institute of Food Research, Colney, Norwich, UK.Dr. Ivan Mura received his first degree in Computer Science and a PhD in Computer Science Engineering from the University of Pisa, Italy, and a Master of Science in Information Technology Project Management from the George Washington University School of Business. During his PhD studies he worked with the Dependable Computing Group established at the Italian National Research Council, on the reliability modeling and evaluation of phased-mission systems with Markov Regenerative Stochastic Petri Nets. In 1999 he joined Motorola Italy as a Senior Software Engineer, in charge of managing the research and development projects of the Modeling and Simulation team and leading the participation of Motorola in several EC funded projects funded under the Framework of the Fifth Research Programme. In 2007 he was appointed as a Senior Researcher for Microsoft Research at the University of Trento Center for Computational and Systems Biology, Italy, where he applied modeling and simulation techniques to the study of various organisms and biological processes. From 2012 to 2015 he hold a professorship position at the EAN University, in Bogotá, Colombia, and he is currently a visiting professor at the Department of Industrial Engineering of the Los Andes University, Bogotá, Colombia. Dr Mura's interests include the mathematical modeling of artificial and living systems, with continuous-deterministic and discrete-stochastic state-based techniques. He has extensive experience in the modeling and simulation of computing and telecom systems for reliability, availability and quality of service analysis and optimization, as well as on computational and systems biology modeling applications to cell cycle, microtubule dynamics, circadian rhythms, biological oscillators, signaling pathways in cancer, sporulation and regulation of toxicity in bacteria.Thanh-Phuong Nguyen is based at the Faculty of Science, Technology and Communication at the University of Luxembourg, Luxembourg

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems.

This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling.

The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.

Chapter 1: Overview of Biological Network Inference and Modeling of Dynamics

Chapter 2: Network Inference From Steady-State Data

Chapter 3: Network Inference From Time-Course Data

Chapter 4: Network-Based Conceptualization of Observational Data

Chapter 5: Deterministic Differential Equations

Chapter 6: Stochastic Differential Equations

Chapter 7: From Network Inference to the Study of Human Diseases

Chapter 8: Conclusions