woolrich Outlet
isabel marant boots
woolrich Outlet
parka woolrich
isabel marant boots
hollister sale
hollister store
Systems Bioengineering - Biomatics.org

Systems Bioengineering

From Biomatics.org

Jump to: navigation, search

Systems bioengineering includes biomatic engineering. 

Systems bioengineering is designs, simulate, synthesise, and construct biological information objects in hardware and software under mathematical principles. Its first task is mathematically modelling life as information processing network.




Cybernetics is the study of feedback and derived concepts such as communication and control in living organisms, machines and organisations. Its focus is how anything (digital, mechanical or biological) processes information, reacts to information, and changes or can be changed to better accomplish the first two tasks.

The terms "systems theory" and "cybernetics" have been widely used as synonyms. Some authors use the term cybernetic systems to denote a proper subset of the class of general systems, namely those systems that include feedback loops. However Gordon Pask's differences of eternal interacting actor loops (that produce finite products) makes general systems a proper subset of cybernetics. According to Jackson (2000), Bertalanffy promoted an embryonic form of general system theory (GST) as early as the 1920s and 1930s but it was not until the early 1950s it became more widely known in scientific circles.

Cybernetics, catastrophe theory, chaos theory and complexity theory have the common goal to explain complex systems that consist of a large number of mutually interacting and interrelated parts in terms of those interactions. Cellular automata (CA), neural networks (NN), artificial intelligence (AI), and artificial life (ALife) are related fields, but they do not try to describe general (universal) complex (singular) systems. The best context to compare the different "C"-Theories about complex systems is historical, which emphasizes different tools and methodologies, from pure mathematics in the beginning to pure computer science now. Since the beginning of chaos theory when Edward Lorenz accidentally discovered a strange attractor with his computer, computers have become an indispensable source of information. One could not imagine the study of complex systems without the use of computers today.


Chronobiology is a field of science that examines periodic (cyclic) phenomena in living organisms and their adaptation to solar and lunar related rhythms.[1] These cycles are known as biological rhythms. "Chrono" pertains to time and "biology" pertains to the study, or science, of life. The related terms chronomics and chronome have been used in some cases to describe either the molecular mechanisms involved in chronobiological phenomena or the more quantitative aspects of chronobiology, particularly where comparison of cycles between organisms is required.

Chronobiological studies include but are not limited to comparative anatomy, physiology, genetics, molecular biology and behavior of organisms within biological rhythms mechanics.[1] Other aspects include development, reproduction, ecology and evolution.

By studying these clocks, scientists are beginning to understand:

  • The biological foundations of behavior.
  • Jet lag, insomnia, mental disorders, and how to treat them.
  • Rhythmic changes in heart rate and other traits that affect the diagnosis and treatment of many disorders, including fever and high blood pressure.

In mammals, the circadian system is organized in a hierarchical manner, in which a master pacemaker in the suprachiasmatic nucleus (SCN) regulates downstream oscillators in peripheral tissues. Recent findings have revealed that the clock is cell-autonomous and self-sustained not only in a central pacemaker, the SCN, but also in peripheral tissues and in dissociated cultured cells. It is becoming evident that specific contribution of each clock component and interactions among the components vary in a tissue-specific manner.

Molecular Cascades

Post-translational modifications (PTMs) define the functional and structural plasticity of proteins. Multi-site protein modification modulates protein activity and macromolecular interactions and is involved in a range of fundamental molecular processes.  


  • Phosphorylation Cascades
  • Microtubule Function
  • Plasma-membrane proteins linked to membrane by GPI anchor
  • Plasma –membrane proteins carry N-glycans
  • Nuclear and cytoplasmic proteins carry O-glycans
  • Polyubiquitylation can induce protein degradation
  • The Histone Code controls many nuclear processes


It is now evident that modified sites in proteins can not only mediate individual functions, but can also function together to fine-tune molecular interactions and to modulate overall protein activity. PTMs therefore have a fundamental role in cell biology and are recognized as important targets in molecular medicine and pharmacology.


The question as to how the precise mechanisms of functional cooperation between the various PTMs (more than 200 different types of PTM have been characterized and new ones are regularly reported) of a protein operate remains to be systematically investigated by proteomics approaches. This is the core of Biomatics as a  'protein language' — that is, the types and patterns of PTMs that are present on a protein at a given time and under given conditions. The spatial and temporal pattern of PTMs on proteins constitutes a molecular code that dictates


  • Protein conformation
  • Cellular location
  • Macromolecular interactions and activities  

depending on the cell type, the tissue and the environmental conditions.
Accordingly, the ultimate goal of proteomics could be to elucidate the principles behind the molecular and functional plasticity of proteins by mapping and quantitating PTMs.


Intracellular signaling

ATP is critical in signal transduction processes. It is used by kinases as the source of phosphate groups in their phosphate transfer reactions. Kinase activity on substrates such as proteins or membrane lipids are a common form of signal transduction. Phosphorylation of a protein by a kinase can activate this cascade such as the mitogen-activated protein kinase cascade.[25]

ATP is also used by adenylate cyclase and is transformed to the second messenger molecule cyclic AMP, which is involved in triggering calcium signals by the release of calcium from intracellular stores.[26] This form of signal transduction is particularly important in brain function, although it is involved in the regulation of a multitude of other cellular processes.[

Cellular Scaffolds

Cellular scaffolds serve as structural components to which various elements of signal transduction pathways can be associated. The association of components on a scaffold can have several important functions, for example they can: 
     1) Associate upstream regulatory components in a cascade that can increase the speed of response to a stimulus; 
     2) Restrict access of substrates to enzymes associated with the scaffold; 
     3) Permit cross talk between distinct signaling pathways, and; 
     4) Aid in the establishment of cellular polarity.

The object model

 In OOP (object oriented programming), each object is capable of receiving messages, processing data, and sending messages to other objects.


Network topology

Diagram of different network topologies.
Diagram of different network topologies.

Network topology is the study of the arrangement or mapping of the elements (links, nodes, etc.) of a network, especially the physical (real) and logical (virtual) interconnections between nodes.[1] [2] [3]

A local area network (LAN) is one example of a network that exhibits both a physical topology and a logical topology. Any given node in the LAN will have one or more links to one or more other nodes in the network and the mapping of these links and nodes onto a graph results in a geometrical shape that determines the physical topology of the network. Likewise, the mapping of the flow of data between the nodes in the network determines the logical topology of the network. It is important to note that the physical and logical topologies might be identical in any particular network but they also may be different.

Any particular network topology is determined only by the graphical mapping of the configuration of physical and/or logical connections between nodes. LAN Network Topology is, therefore, technically a part of graph theory. Distances between nodes, physical interconnections, transmission rates, and/or signal types may differ in two networks and yet their topologies may be identical[2].


Relational Models

Relational models are the most common representation of structured data. Enterprise business information, marketing and sales data, medical records, and scientific datasets are all stored in relational databases.

The relational model for database management is a database model based on first-order predicate logic. 

Knowledge engineering (KE) has been defined by Edward Feigenbaum, and Pamela McCorduck (1983) as follows:

""KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise." [1]

At present, it refers to the building, maintaining and development of knowledge-based systems (Kendal, 2007 [2] ). It has a great deal in common with software engineering, and is used in many computer science domains such as artificial intelligence [3], [4], including databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works.

External Links

Educational animations

Personal tools
Google AdSense