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An abundance of data and unprecedented computational power are allowing sophisticated biological models to be devised and tested. This series of articles examines how the coupling of genetics with disciplines such as engineering, statistics, physics and computational biology has enriched our understanding in areas that range from developmental patterning to genetic association analyses.
Why does the rate of evolution vary among sites within proteins? The authors review the current understanding of site-specific variation, discuss possible limitations of current methods and models, and propose directions for future research.
The use of mathematical modelling to elucidate genetic, molecular and cellular processes is an integral part of systems biology. This Review presents the main methods to model and simulate molecular and gene networks (kinetic and logic modelling), describes their inherent advantages and limitations, and highlights recent developments that reveal the potential of hybrid approaches.
Increasing evidence suggests that competition between transcripts for binding of microRNAs and RNA-binding proteins might be a fundamental principle of post-transcriptional gene regulation. The authors use a simple steady-state model to quantitatively assess competition effects under physiological conditions and review the role of endogenous 'sponges' in light of the key features that emerge.
Constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework, which enables a mechanistic description of metabolic physiology. Recent successes in using this approach have implications for microbial evolution, interaction networks, genetic engineering and drug discovery.
The author discusses the advantages and challenges of 'cellular phenotyping' and the way in which this integrated analysis of cellular phenotypes is likely to advance our understanding of the genetic and long-term environmental influences on complex traits.
This Review describes the successful efforts of modelling approaches that combine evolutionary theory, systems biology and molecular data to interpret past evolution and, crucially, to predict evolutionary processes at the molecular and network levels.
Evolutionary developmental biology is being advanced by quantitative methods for studying morphology. This Review considers such approaches and emerging insights into interactions between genetic and non-genetic factors, as well as the evolutionary constraints that influence shape.
'Omics' technologies are making it possible to simultaneously measure a substantial portion of the molecular components of a cell. This article describes the challenges that need to be confronted to develop and refine genome-scale reconstructions of signalling networks.
The article highlights how the process of engineering biological systems has contributed to our understanding of how endogenous systems are put together and function — from a quantitative description of gene expression and signal transduction to controlling spatial organization and cell–cell interactions.
Bayesian analyses are increasingly being used in genetics, particularly in the context of genome-wide association studies. This article provides a guide to using Bayesian analyses for assessing single-SNP associations and highlights the advantages of these methods compared with standard frequentist analyses.
Microfluidic 'lab-on-a-chip' devices can be used to study the dynamics of gene networks in single cells. This Review discusses the various designs of these devices and the insights into modelling the complex dynamics of gene regulation that these new technologies have provided.
This article describes the growing and invaluable contribution that quantitative mathematical frameworks are making to generating and testing hypotheses in developmental biology, and in shaping new ways of understanding developmental processes across molecular, cellular and tissue scales.
The rapid evolution of many important pathogens, particularly RNA viruses, means that their ecological and evolutionary dynamics occur on the same timescale. This Review discusses the insights into the transmission and epidemiology of viruses that have been provided by analyses of their evolutionary dynamics across a wide range of biological scales.
This Review presents a quantitative framework for translating DNA sequences into transcriptional behaviours. Such a model, based on the binding affinity landscape of molecules to genomic sequences, can help to describe complex phenomena such as transcriptional noise and the evolution of transcriptional regulation.
The limited lack of success of many human complex disease studies is often attributed to the existence of interactions between loci. This article reviews and assesses the methods and software packages that have been developed to detect these gene by gene interactions.
A realistic understanding of how a biological system arises from interactions between its parts increasingly depends on quantitative mathematical and statistical modelling. This Review explains how statistical inferences and stochastic modelling are the best tools we have for describing heterogeneous biological systems.
