Why Computational Biology?

-Many aspects of biology (such as sequence information) are fundamentally digital in nature. This means that they are well suited to computational modeling and analysis. Prof. Manolis states that the genetic information must be digital, not analog because analog signals would be diluted from generation to generation. The information has to be digital to be minutely propagated throughout the cell generations. Biology is digital in several aspects, e.g., biological switches, regulatory regions, promoter regions and replication process. Several computational biology problems aim to find biological signals in DNA data (e.g. coding regions, promoters, enhancers, regulators, ...). -New technologies (such as sequencing, and high-throughput experimental techniques like microarray, yeast two-hybrid, and ChIP-chip assays) are creating enormous and increasing amounts of data that can be analyzed and processed using computational techniques -Running time & memory considerations are critical when dealing with huge datasets . An algorithm that works well on a small genome (for example, a bacteria) might be too time or space inefficient to be applied to 1000 mammalian genomes. Also, combinatorial questions dramatically increase algorithmic complexity. -Biological datasets can be noisy, and filtering signal from noise is a computational problem. -Machine learning approaches are useful to make inferences, classify biological features, & identify robust signals. -It is possible to use computational approaches to find correlations in an unbiased way, and to come up with conclusions that transform biological knowledge and facilitate active learning. This approach is called data-driven discovery. -Computational studies can suggest hypotheses, mechanisms, and theories to explain experimental observations. These falsifiable hypotheses can then be tested experimentally. -Computational approaches can be used not only to analyze existing data but also to motivate data collection and suggest useful experiments. Also, computational filtering can narrow the experimental search space to allow more focused and efficient experimental designs. -Datasets can be combined using computational approaches, so that information collected across multiple experiments and using diverse experimental approaches can be brought to bear on questions of interest. -Effective visualizations of biological data can facilitate discovery. -Computational approaches can be used to simulate & model biological data. -Optimization approaches can be used to solve, via computational technique, otherwise intractable problems. -Large scale, systems engineering approaches are facilitated by computational technique to obtain global views into the organism that are too complex to analyze otherwise.