SYSTEM BIOLOGY is a whole-istic approach to understanding biology which aims at system-level understanding of biology, and to understand biological systems as a system or In simpler words it is examination of the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism.
Immune System and nerves System are two prominent examples of biological system
.Although idea of system level understanding is not new to biology,there have been attempts at such an approach in the past which originated in other fields of science. Scientists from other disciplines - such as physicists or (later) systems theorists - have been interested in applying their science to biology for quite some time
But concept of System Biology was originally conceived by NORBERT WIENER, the founding father of cybernetics in 1948, who explicitly considered technical as well as biological systems as objects for the same scientific approach.At the same time other attempts were made, some still under the name of cybernetics, that kept the idea alive through the following decades. Prominent are
- Biochemical Systems Theory (BST), developed in the late 1960s, and a related approach
- Metabolic Control Theory (MCT), proposed in the mid 1970s.
to create simplified mathematical models of biological systems in steady state and resulted in quite a number of tools and methods for analyzing systems modeled in the respective way. But all these attempts suffered from inadequate data to base their theories and models.
Breakthrough advances in molecular biology in the last decades in wake of human genome project , providing new data, enabling applied work in this area, making the in silico model of an organism envisionable.These advancements can be subsumed into two groups :- Bottom-up( independent experimental data into a conclusive representation of a gene regulatory network) and
- Top-down approaches(uses high-throughput data from DNA micro-array and other new measurement technologies).
These advances in measurement, data acquisition and handling technologies provide a wealth of new data to improve existing models. That data can be divided into four categories or key properties:
Progress in these areas required ``breakthroughs in our understanding of computational sciences, genomics, and measurement technologies, and integration of such discoveries with existing knowledge''which brings together scientists from a lot of different disciplines, such as biology, systems theory, computer science, physics, chemistry, and interdisciplinary areas of applied science like measurement instruments development.
APPROACHES FOR SYSTEM BIOLOGY
There are basically two approaches to SYSTEM BIOLOGY. One group wants to use a new system-oriented approach. Another wants to continue the successful work along proven lines and make progress by ``integrating the different levels of information pertaining to genes, mRNAs, proteins, and pathways'' which have up to date been used individually and interestingly both these approaches can coexist without hurting each other,even they can profit from each other's discoveries.
System biologist are trying to integrate the biological knowledge and to understand how the molecules act together within the network of interaction that makes up life.As earlier said currently we are having enormous amount of biological data, which cannot be understood by simply drawing lines between interacting molecules Model building promises to be the key in advancing understanding. Already numerous centers devoted to Systems Biology being opened worldwidea, and other research collaborations bringing together expertise in mathematics, information science and biology being funded.
WHAT IS MODEL BUILDING
Model building as an aid to understand complex systems is also the method of choice in areas like ecology or economy.This huge amount of data is familiar to engineers, for example, those designing control systems for modern passenger jets.So By taking clue and lessons from system analysis of advanced technologies and engineering theory suggest that the systems can be divided into subsystems, so one does not have to tackle and solve the whole system at once which will ease the task of understanding of complex interactions at systems level.
MODEL BUILDING IN BIOLOGY
Same Complexity as faced by other branches of Science is experienced in Biology while looking at signal signaling molecule.Prof. Kitano said "biological system is not just an assembly of genes and proteins.Therefore, its properties cannot be fully understood merely by drawing diagrams of their interconnections." Currently Biologists are using "conceptiol" models describing their pictured view of the events involved. Those models are in general purely qualitative. There is need to introduce mathematics like non-linear differential equations to elucidate the underlying mechanism on a quantitative basis to gain systems-level understanding, not just a pictured view.
The word model derives from the Latin language and refers to the simplified representation of a real system e.g. representation of TNF Signaling Network using mathematics.Systems and models are often structured and hierarchically build(modularity).
" Modularity is a concept of treating subsystems of complex molecular networks as functional units that perform identifiable tasks perhaps even able to be characterized in familiar engineering terms.It would also be the ideal basis for future developments to even more complex models, once the cellular and sub-cellular levels can be described in sufficient detail. Modular approach in biology facilitates to produce fully qualified models of first cells, then organs, then even complete organisms."
On the cellular level, for example, usually the metabolic, gene, and protein networks are distinguished, although, of course, interconnected, and organs and then the organism form the next higher levels.
TYPES OF MODEL
Process of Model building also varies based on source of Information.Here i am giving few examples,
Models can be based on a priori knowledge about system elements, e.g. physical laws,
Models based on a behavior seen, e.g. curve fitting, where the correlation between input and output is then usually treated as a black box.
Model is static when all output signals only depend on the input signal at the same time,Where only the topology is relevant for the behavior.
Model is dynamic, where the system behavior also depends on the past. This implies storage in the system. Models can be deterministic, i.e. for a certain input and certain initial conditions of the state variables, there is one output or models can be probabilistic, i.e. the output is predicted within a range of values, and each value has an assigned probability. Certainly probabilistic models are useful in biology, and are necessary if small numbers of molecules are involved. However, probabilistic models are more complicated and also more demanding in terms of computer performance to be solved.
MODELING AS AN REPEATING PROCESS
Modeling is an repeating process where in initial stage one has to consider the questions the model is supposed to answer, and then clearly define the model system, e.g. which cell line is to be modeled. Then in next step one has to intensively perform the extensive scoping to gain information. This literature provides a lot of information, but this information often is only qualitative not quantitative and often obtained on different model systems.
Often above information is sufficient to define the structure of the model, i.e. what molecules are there and who interacts.Again before modeling, the type of model has to be considered and how detailed it should be, again this depends on the questions the model should be able to answer. After the structure of the model has been defined, it has to be implemented kinetically (if a dynamic model approach is chosen, as is the case here), i.e. the kinetic parameters have to be adjusted, so that the model can explain the experimental data. The operating model resulting thereof has then to be verified by testing model predictions and comparing those with literature or new own experimental data that have not already been used in the model implementation. Also, negative tests can be performed, i.e. is the model not doing what it should not do? The experiments have to be designed to distinguish a right from a wrong model. With new data at hand, the model can be refined, maybe only by adjusting parameters, but maybe also by changing the structure, thus starting the next iteration round.
CONCLUSION
One major goal of these efforts clearly is a better understanding of how cells work. Model-building is a tool to that end as well as a standardized form of representation for knowledge about a system. This is different from the way biologist defined models in the past, using prose descriptions of concepts and ideas.
``The most feasible application of systems biology research is to create a detailed model of cell regulation, focused on particular signal-transduction cascades and molecules to provide system-level insights into mechanism-based drug discovery. Such models may help to identify feedback mechanisms that offset the effects of drugs and predict systemic side effects.''
Endless application possibilities exists :
- Easier drug design; 'personalized' drugs, i.e. built for purpose
- Side effect free medicines, developed for (or at least adapted to) individual patients,
- Directed, reliable manipulation of gene information (e.g. treatment of tumors or hereditary diseases); and more.
It may even be possible to use a multiple drug system to guide the state of malfunctioning cells to the desired state with minimal side effects. Such a systemic response cannot be rationally predicted without a model of intracellular biochemical and genetic interactions.With such models another transfer from engineering practice would become possible: Newly designed drugs could be tested in simulations before going into clinical testing. This would reduce risks to test subjects and patients and could eventually eliminate the need for animal testing.
For these applications to be realistic, though, apart from vastly increased computing power it will be absolutely necessary to be able to tune the level of Modularity.
First of all, though, before any of these visions can become reality, has to come a fundamental understanding of the processes in cells at the smallest level (i.e. level of smallest systems). The basis for macro-level insights is still micro-level knowledge, a basis that has been built continuously up till now and will increase as technologies improve.This is needed not only to understand the mechanisms to be used and manipulated. The ability to assess the risks that are inherent in manipulating so complex and intricately balanced a machinery as molecular processes in or between cells will be possibly even more important. Here simulations could considerably reduce the risk of creating potentially dangerous mutations and help clarify genetic mechanisms of inheritance and gene transfer and their consequences. This would help to understand the complexity of biological systems and make it more manageable than it is today.
That's all from my side i had put all my random thoughts about these vast branch of bioinformatics.Your thoughts and suggestions are welcomed.
Thanks all
