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The Retinoblastoma Gene Family in Cell Cycle Regulation and Suppression of Tumorigenesis
Since its discovery in 1986, as the first tumor suppressor gene, the retinoblastoma gene (Rb) has been extensively studied. Numerous biochemical and...
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Senescence and Cell Cycle Control
In response to various stresses, such as telomere shortening during continuous proliferation, oxidative stress, DNA damage and aberrant oncogene...
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Systems Biology: necessary developments and trends
At the end of this definition of Systems Biology through exampling, we discuss ambitions, goals, and challenges relating to this new discipline. We...
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Control of Cell Proliferation and Growth by Myc Proteins
Myc proteins act as signal transducers that alter cell proliferation in dependence on signals from the extracellular environment. In normal cells,...
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Checkpoint and Coordinated Cellular Responses to DNA Damage
The DNA damage and replication checkpoints are signaling mechanisms that regulate and coordinate cellular responses to genotoxic conditions. The...
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Scientific and technical challenges for systems biology
Systems biology is an emergent discipline, yet can be rooted back almost a century when pioneering thoughts on system-oriented views were discussed....
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From isolation to integration, a systems biology approach for building the Silicon Cell
In the last decade, the field now commonly referred to as systems biology has developed rapidly. With the sequencing of whole genomes and the...
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Regulation of S Phase
Regulation of DNA replication is critical for accurate and timely dissemination of genomic material to daughter cells. The cell uses a variety of...
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Systems biology of apoptosis
New approaches are required for the mathematical modelling and system identification of complex signal transduction networks, which are characterized...
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Hydrogels for Musculoskeletal Tissue Engineering
The advancements in scaffold-supported cell therapy for musculoskeletal tissue engineering have been truly dramatic in the last couple of decades....
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From Classical Connectionist Models to Probabilistic/Generalised Regression Neural Networks (PNNs/GRNNs)
This chapter begins by briefly summarising some of the well-known classical connectionist/artificial neural network models such as multi-layered...
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Linkage Learning Genetic Algorithm
In order to handle linkage evolution and to tackle the ordering problem, Harik [47] took Holland’s call [53] for the evolution of tight linkage quite...
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Preliminaries: Assumptions and the Test Problem
After introducing the background and motivation of the linkage learning genetic algorithm, we will start to improve and understand the linkage...
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Data Driven Fuzzy Modelling with Neural Networks
Extraction of models for complex systems from numerical data of behavior is studied. In particular, systems representable as sets of fuzzy if-then...
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Bioinformatics with Evolutionary Computation
This chapter makes the presumption that it is more important to understand the domain of the problem of interest and to pursue the best achievable...
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Rough Set Theory with Applications to Data Mining
This paper is an introduction to rough set theory with an emphasis on applications to data mining. First, consistent data are discussed, including...
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The Kernel Memory Concept – A Paradigm Shift from Conventional Connectionism
In this chapter, the general concept of kernel memory (KM) is described, which is given as the basis for not only representing the general notion of...
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Language and Thinking Modules
In this chapter, we focus upon the two modules which are closely tied to the concept of “action planning”, i.e. the 1) language and 2) thinking...
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Reinforcement Learning: A Brief Overview
Learning techniques can be usefully grouped by the type of feedback that is available to the learner. A commonly drawn distinction is that between...