Computational Systems Biology
Making Sense of Data
The term "theorectical biology", is a rather perplexing combination of words. However, biology is a very well-developed science and there is more than enough ground, indeed a critical need, for the development and application of theoretical methods to extend our understanding of biology - theoretical biology.
Modern research instrumentation creates enormous volumes of data and in order to make sense of this data it must be analyzed on a computer, often a super-computer. My research is focused on developing computational strategies for analyzing such data and is highly dependent on collaboration with other experimental scientists who produce those data. I help my colleagues analyze genomics, proteomics and other types of information using computational methods. In return, I use information from multiple, sometimes seemingly unrelated studies to connect the dots and acquire new knowledge through analysis and modeling of biological processes.
What if . . . the only copy of a vast amount of printed data on a particular subject or about a specific disease were accidently run through a paper shredder? Could we use math and computers to piece the fragments together, to make sense of the data and save lives? In essence, this is the challenge being addressed by theoretical biologists. We can, for instance, read whole genomes, observe the way genes are changing their activity, sometimes even one cell at a time, but only so many letters at a time: one fragment from the paper shredder. We have to assemble the multitude of pieces to make sense of it if we are to fight cancer, chase away infections, tune up metabolism, or take control of aging. We are learning to piece together the fragments of genetic code through computational analysis.
What if . . . we find we can reassemble the entire message, but still don't understand the language in which it's written? We team up with collaborators representing all walks of the life sciences, from the biochemistry lab to the research clinic, to decode the messages and create practical applications. Genes never perform a function or break down to cause a disease one gene at a time. Each function is performed by hundreds of interacting genes and each disease results in multiple genes changing the pattern of activity. We use gene interaction maps to reverse-engineer complex circuitry involved in normal and perturbed states.
What if . . . we can read the message, and understand its meaning, but miss the time of transmission? Time adds a whole new dimension to the already staggering complexity of biological pathways – it’s like going from still images to moving pictures. Factoring in the timing of molecular events allows better understanding of how everything works. Rhythm is the key to understanding the temporal organization of a living cell. The timing of gene expression is well organized, structured and regulated. As a result, most of the biological networks we observe over time have their intrinsic rhythms coordinated with environmental periodicity such as the daily (circadian) rhythm of changing light and temperature. One of my lab’s major research interests is mapping and understanding the circadian regulation of gene expression.
Andrey Ptitsyn, Ph.D.
Dr. Andrey Ptitsyn has an undergraduate background in General Biology/Ecology. He joined one of the earliest graduate programs in Mathematical Biology and graduated with a Master of Science degree from the Novosibirsk State University in Siberia. He earned his Ph.D. in Bioinformatics from the University of Western Cape, South Africa while working at the South African National Bioinformatics Institute (SANBI).
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