Complexity in Biological Information Processing

Many human diseases such as cancer, diabetes and neural disorders arise from the malfunction of signalling components. This is frequently not caused by a single defect but is due to alterations to multiple components of an integrated signalling network. Experimental tools to quantify such changes precisely and describe the interconnected multichain signalling pathways in health and disease in a time–resolved manner are becoming increasingly available, resulting in a exponential increase in detailed information. For the unaided human mind, classification of this wealth of data and understanding the implications for pathophysiology is getting more and more difficult. Computational tools are a prerequsiite for understanding of the complex interactions in biological information processing from the vast array of experimental data. These tools are starting to take shape. They have the potential to integrate many details into a systematic analysis of the entire signalling network and enable prediction of diease states not easily recognizable from complex data sets. This approach may thus help to switch the analysis of biological signalling from descrptive to predictive science and capture in a more general way the behaviour of entire systems. This groundbreaking book explores the structural and temporal complexity in biological signalling exemplified in neuronal, immunological, humoral and genetic signal transduction networks. It contains interdisciplinary discussions between experimentalists and theoretically oriented scientists, in particular those working on computer simulations. Synthesis of experiment, theory and simulation should help to explain disorders of the regulation of complex biological networks and may lead to a new understanding of many human diseases.

Spis treści:
Introduction (T. Sejnowski).
Functional Modules in Biological Signalling Networks (U. Bhalla & R. Iyengar).
Design of Immune–based Interventions in Autoimmunity and Viral Infections–– the Need for Predictive Models that Integrate Time, Dose and Classes of Immune Responses (M. von Herrath).
Controlling the Immune System: Diffuse Feedback via a Diffuse Informational Network (L. Segel).
The Versatility and Complexity of Calcium Signalling (M. Berridge).
Multiple Pathways of ERK Activation by G Protein–coupled Receptors (T. Gudermann).
Heterogeneity of Second Messenger Levels in Living Cells (M. Zaccolo, et al.).
Humoral Coding and Decoding (K. Prank, et al.).
From Genes to Whole Organs: Connecting Biochemistry to Physiology (D. Noble).
Development of High–Throughput Tools to Unravel the Complexity of Gene Expression Patterns in the Mammalian Brain (U. Herzig, et al.).
Regulation of Gene Expression by Action Potentials: Dependence on Complexity in Cellular Information Processing (R. Fields, et al.).
Efficiency and Complexity in Neural Coding (S. Laughlin).
Neural Dynamics in Cortical Networks–– Precision of Joint–Spiking Events (A. Aertsen, et al.).
Predictive Learing of Temproal Sequences in Recurrent Neocortical Circuits (R. Rao & T. Sejnowski).
Final Discussion.
Index of Contributors.
Subject Index.

Okładka tylna:
Many human diseases such as cancer, diabetes and neural disorders arise from the malfunction of signalling components. This is frequently not caused by a single defect but is due to alterations to multiple components of an integrated signalling network. Experimental tools to quantify such changes precisely and describe the interconnected multichain signalling pathways in health and disease in a time–resolved manner are becoming increasingly available, resulting in a exponential increase in detailed information. For the unaided human mind, classification of this wealth of data and understanding the implications for pathophysiology is getting more and more difficult. Computat ional tools are a prerequsiite for understanding of the complex interactions in biological information processing from the vast array of experimental data. These tools are starting to take shape. They have the potential to integrate many details into a systematic analysis of the entire signalling network and enable prediction of diease states not easily recognizable from complex data sets. This approach may thus help to switch the analysis of biological signalling from descrptive to predictive science and capture in a more general way the behaviour of entire systems. This groundbreaking book explores the structural and temporal complexity in biological signalling exemplified in neuronal, immunological, humoral and genetic signal transduction networks. It contains interdisciplinary discussions between experimentalists and theoretically oriented scientists, in particular those working on computer simulations. Synthesis of experiment, theory and simulation should help to explain disorders of the regulation of complex biological networks and may lead to a new understanding of many human diseases.