Statistical models and methods in biology have a long and glorious history, with key contributions of many famous scientists. In recent years, the fast method and technology developments in molecular biology and genetics have revitalised statistics and data analysis in biology in several different ways.
Statistical theories of genetic recombination and population genetics, originally developed in the first half of the last century by portal scientists like Wright, Fisher, Haldane, Morgan and others, have been adapted to handle enormous amounts of genotyping data from health investigations of samples from whole populations or of case/ control studies in connection to diseases. A key idea in this progress was the development of statistical methods robust against deviations from too idealised genetical models, assuming perfect genetical balances and perfect population mixtures etc. The interplay between statistics and computation and development of computational algorithms and software is another key factor for this very active research field.
Staffan Nilsson, Marita Olsson, Serik Sagitov, Frank Eriksson and Malin Östensson work with applications and method development in this statistical genetics and population genetics tradition. Most of their research is based on collaborations with specific medical applications in focus.
Probability models and statistical methods also play a key role in interpretation of the genetic code; in finding genes, comparing or annotating function, or spatial structures, of proteins in DNA or amino acid sequences etc, in one or several genomes. Thus, bioinformatics subfields like sequence bioinformatics, structure prediction and comparative bioinformatics have been established and developed quickly in the last decades. So have statistical and computational techniques in genomics, the biological science of studying genetic and molecular systems by large scale experimental and computational methods. In this field many traditional statistical ideas, models and analysis techniques have been re-examined and vitalised. Thus current and future interplay between genomics and basic statistical theory and philosophy is of great importance for both fields.
Recently, a multitude of different stochastic models of specific biomolecular reaction kinetics of whole systems of gene regulation, gene products like mRNA, proteins etc and metabolic compounds, and detailed gene regulation mechanisms on single cell, tissue or organ levels have been suggested to explain a plethora of biologically important phenomena. In parallel statistical modelling of measurements in experiments connected to this new wave of modelling, named as systems biology, has been rapidly developed and a real challenge is now to integrate randomness of key single molecular reactions, in single cells, aggregation of stochasticity of moderately large systems of similar reactions in single cells, statistical modelling of variability in composition of physical and physiological states of cells in experimental cultures, tissues or organs, and statistical modelling of measurement variability in the key experiments, in order to get deeper, system level, understanding of key biological processes and also to find detailed molecular level causes of genetic diseases.
Mats Rudemo, Olle Nerman, Marina Axelsson-Fisk, Petter Mostad, Peter Gennemark, Frida Abel, Alexandra Jauhiainen, Janeli Sarv and Dmitrii Zholud all work with statistical applications and research in these bioinformatics or computational biology fields connected to genomics and/or systems biology.
Researchers from both subgroups are engaged in the GU-lead research school in Genomics and Bioinformatics, Chalmers International Master Programme in Bioinformatics and Systems Biology and the Systems Biology Masters Programme at the University of Gothenburg.