Systems Biology Meets 'Real' Biology
07/05/10 03:59 Filed in: GenomeWeb Daily Scan
Submitted by S. Pelech - Kinexus on Mon, 05/07/2012 - 03:59.
I guess according to Dr. Tim Hunt's broad definition, it would appear that anyone who studies an organism or a biological process is a systems biologist. Dr. Hunt has certainly made enormous contributions to our understanding of cell cycle progression. However, I was also around at the time of the first discovery of cyclin-dependent kinases and cyclins, and I very actively contributed to our early understanding of the meiotic maturation of frog and sea star oocytes myself. It is plainly obvious to me that luck, or more accurately serendipity, had a major role in making important discoveries in this area.
We wonder whether the protein that we happen upon is the missing link in biological problem. We intensely study this protein and want to know everything about what it does, how it is controlled, etc., and formulate testable hypotheses toward this end. However, the tendency of hypothesis-driven science is to use our biases to try to explain complex physiological processes with a relatively small number of proteins and largely ignore the other 23,000 proteins encoded by the genome.
Dr. Hunt's major criticisms of systems biology really arise from the infancy of the field, and the enthusiasm of those in this area to make sense of the limited data that is currently available about proteins. Genomics research has provided the identification of the protein players and data about where and when they are produced. Mass spectrometry is revealing how these proteins are modified and their interactions with each other. Shortly, with improved discovery tools such as protein microarrays and better algorithms, we can amass sufficient raw data and analyze the results to much more efficiently investigate biological processes without preconceived notions.
The vast majority of the protein-protein interactions that occur in cells is likely to be inconsequential, just as most of the DNA in chromosomes has no real purpose. It just happens. The real challenge is to find the meaningful and important connections. A systems proteomics approach that builds on the genomics legacy that has been created is the best way to do this.
For example, recently at Kinexus Bioinformatics we developed an algorithm that predicted the substrate amino acid specificities of 492 human protein kinases simply from their amino acid sequences. The huge amount of published data available about substrates for well known protein kinases such as the cyclin-dependent kinases made it possible to test this algorithm for its predictive accuracy. The fact that we obtained results that closely agreed with more traditional methods for determination of kinase specificities is not where the value from this work resides. Rather, it was the elucidation of the specificities of over 300 human protein kinases for which no experimental data was previously available. This information will permit the formulation of much better hypotheses about important interactions with these other orphaned protein kinases.
The wider adoption of systems biology approaches will usher in the emergence of predictive biology. I suspect that die-hards such as Dr. Hunt will find it pretty hard to ignore the fruits of these efforts as the field matures.
Link to the original blog post.
I guess according to Dr. Tim Hunt's broad definition, it would appear that anyone who studies an organism or a biological process is a systems biologist. Dr. Hunt has certainly made enormous contributions to our understanding of cell cycle progression. However, I was also around at the time of the first discovery of cyclin-dependent kinases and cyclins, and I very actively contributed to our early understanding of the meiotic maturation of frog and sea star oocytes myself. It is plainly obvious to me that luck, or more accurately serendipity, had a major role in making important discoveries in this area.
We wonder whether the protein that we happen upon is the missing link in biological problem. We intensely study this protein and want to know everything about what it does, how it is controlled, etc., and formulate testable hypotheses toward this end. However, the tendency of hypothesis-driven science is to use our biases to try to explain complex physiological processes with a relatively small number of proteins and largely ignore the other 23,000 proteins encoded by the genome.
Dr. Hunt's major criticisms of systems biology really arise from the infancy of the field, and the enthusiasm of those in this area to make sense of the limited data that is currently available about proteins. Genomics research has provided the identification of the protein players and data about where and when they are produced. Mass spectrometry is revealing how these proteins are modified and their interactions with each other. Shortly, with improved discovery tools such as protein microarrays and better algorithms, we can amass sufficient raw data and analyze the results to much more efficiently investigate biological processes without preconceived notions.
The vast majority of the protein-protein interactions that occur in cells is likely to be inconsequential, just as most of the DNA in chromosomes has no real purpose. It just happens. The real challenge is to find the meaningful and important connections. A systems proteomics approach that builds on the genomics legacy that has been created is the best way to do this.
For example, recently at Kinexus Bioinformatics we developed an algorithm that predicted the substrate amino acid specificities of 492 human protein kinases simply from their amino acid sequences. The huge amount of published data available about substrates for well known protein kinases such as the cyclin-dependent kinases made it possible to test this algorithm for its predictive accuracy. The fact that we obtained results that closely agreed with more traditional methods for determination of kinase specificities is not where the value from this work resides. Rather, it was the elucidation of the specificities of over 300 human protein kinases for which no experimental data was previously available. This information will permit the formulation of much better hypotheses about important interactions with these other orphaned protein kinases.
The wider adoption of systems biology approaches will usher in the emergence of predictive biology. I suspect that die-hards such as Dr. Hunt will find it pretty hard to ignore the fruits of these efforts as the field matures.
Link to the original blog post.