A framework for evolutionary systems biology Export

@article{citeulike:4096748, abstract = {BACKGROUND:Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.RESULTS:Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.CONCLUSION:EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.}, author = {Loewe, Laurence}, citeulike-article-id = {4096748}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1752-0509-3-27}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19239699}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19239699}, doi = {10.1186/1752-0509-3-27}, issn = {1752-0509}, journal = {BMC Systems Biology}, keywords = {evolutionary-theory, fitness-landscape, modelling, mutations, systems-biology}, number = {1}, pages = {27+}, posted-at = {2009-11-04 22:53:20}, priority = {2}, title = {A framework for evolutionary systems biology}, url = {http://dx.doi.org/10.1186/1752-0509-3-27}, volume = {3}, year = {2009} }

TY - JOUR ID - citeulike:4096748 L3 - citeulike-article-id:4096748 N2 - BACKGROUND:Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.RESULTS:Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.CONCLUSION:EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications. IS - 1 JF - BMC Systems Biology SN - 1752-0509 TI - A framework for evolutionary systems biology VL - 3 SP - 27 KW - evolutionary-theory KW - fitness-landscape KW - modelling KW - mutations KW - systems-biology AU - Loewe, Laurence PY - 2009/// UR - http://dx.doi.org/10.1186/1752-0509-3-27 ER -