SYSTEMS BIOLOGY The completion of the human genome project and acquisition of large data sets as well as infusion of scientists from different disciplines such as physicists and mathematicians have been identified as core strengths in pursuing systems biology, explains Hemanth Tummala
The world of biology has exploded with a wealth of information on the genome, proteins, signalling molecules and pathways that are being dissected and studied in great detail.
Now, attempts are being made to knit together all these individual components through a process called systems biology, which is all about translating bench-top science into the language of computers and mathematics. Systems biology is adopted as a catch phrase in the current trend of bioscience research and is believed to be a natural offshoot of physiology and biochemistry in a classical way.
The issue we face here is dealing with the complexity of living systems and diseases like cancer. Due to this complexity, some form of holistic approach to the problem is required and it is believed that the one way forward is to describe the dynamics of the various molecular and cellular interactions.
In short focus, this has been helped by molecular biology and genomic data, but still lacks accuracy in the prediction of the "right drug for the right treatment at the right time during disease progression." In order to define this, systems biology emerged as the integration of complex information into a conceptual framework. This framework has to be comprehensive, competitive and have prediction ability. It can be developed through rigorous integration to help us understand the underlying complexity of biological systems.
The completion of the human genome project and acquisition of large data sets as well as infusion of scientists from different disciplines (ex: physicists, mathematicians) have been identified as core strengths in pursuing systems biology. Data acquired by biologists through research using tools of molecular biology and genomics have not helped predict any treatment outcome in diseases like cancer.
The real problem lies in their interaction in complex systems, and most importantly, the inability of a biologist in prediction with the help of these large data sets. Lately but surely, it has been realised that it is difficult to deal with these kinds of problems in the absence of mathematics and modelling. A model, in the systems perspective, should be accurate in prediction of time dynamics associated with complex network component changes.
Now the interesting question to be asked is whether there is a model for normal biology or are we working to know the model of normal biology? It would be of immense help if we could have this model, but classic research always moves from understanding a disease state and then to a normal situation. Owing to the availability of large scale genomic information, physiological information and computers, building prediction models is highly unavoidable. These models, in the long-term, will be iterative interplay tools between biology and mathematics and would certainly help in the current understanding of today's biology. Hope lies in generating a comprehensive self-explanatory model, not just a prediction model.
(The writer is PhD, MSB Research fellow, Cancer Systems Biology Group School of Contemporary Sciences, University of Abertay Dundee.)
The world of biology has exploded with a wealth of information on the genome, proteins, signalling molecules and pathways that are being dissected and studied in great detail.
Now, attempts are being made to knit together all these individual components through a process called systems biology, which is all about translating bench-top science into the language of computers and mathematics. Systems biology is adopted as a catch phrase in the current trend of bioscience research and is believed to be a natural offshoot of physiology and biochemistry in a classical way.
The issue we face here is dealing with the complexity of living systems and diseases like cancer. Due to this complexity, some form of holistic approach to the problem is required and it is believed that the one way forward is to describe the dynamics of the various molecular and cellular interactions.
In short focus, this has been helped by molecular biology and genomic data, but still lacks accuracy in the prediction of the "right drug for the right treatment at the right time during disease progression." In order to define this, systems biology emerged as the integration of complex information into a conceptual framework. This framework has to be comprehensive, competitive and have prediction ability. It can be developed through rigorous integration to help us understand the underlying complexity of biological systems.
The completion of the human genome project and acquisition of large data sets as well as infusion of scientists from different disciplines (ex: physicists, mathematicians) have been identified as core strengths in pursuing systems biology. Data acquired by biologists through research using tools of molecular biology and genomics have not helped predict any treatment outcome in diseases like cancer.
The real problem lies in their interaction in complex systems, and most importantly, the inability of a biologist in prediction with the help of these large data sets. Lately but surely, it has been realised that it is difficult to deal with these kinds of problems in the absence of mathematics and modelling. A model, in the systems perspective, should be accurate in prediction of time dynamics associated with complex network component changes.
Now the interesting question to be asked is whether there is a model for normal biology or are we working to know the model of normal biology? It would be of immense help if we could have this model, but classic research always moves from understanding a disease state and then to a normal situation. Owing to the availability of large scale genomic information, physiological information and computers, building prediction models is highly unavoidable. These models, in the long-term, will be iterative interplay tools between biology and mathematics and would certainly help in the current understanding of today's biology. Hope lies in generating a comprehensive self-explanatory model, not just a prediction model.
(The writer is PhD, MSB Research fellow, Cancer Systems Biology Group School of Contemporary Sciences, University of Abertay Dundee.)