How Evolutionary Systems Biology Will Help Understand Adaptive Landscapes and Distributions of Mutational Effects

Abstract

Population genetics and ecology have been modeling biological systems quantitatively for over 8 decades and their results have contributed greatly to our understanding of the natural world and its evolution. Theories in these areas necessarily had to focus on comparisons of the contribution of different individuals to changes in the bigger picture at the expense of ignoring much of the complexity that exists inside individuals. Current systems biology provides new insights into this complexity within organisms. Here I review developments in evolutionary systems biology that have the potential to lead to a more unified approach that integrates contributions from current systems biology and population genetics. Central integrative concepts in this approach are the adaptive landscape and distributions of mutational effects. Both capture our understanding of the fitness of individuals and how it can change. Fitness is frequently used in population genetics to summarize key properties of individuals. Such properties emerge from the complexity of molecular processes within individuals, often in interaction with the environment. The general principles of this approach are reviewed here. This work can open up new avenues for computing critical quantities for models of long-term evolution, including epistasis, the distribution of deleterious mutational effects, and the frequency of adaptive mutations.

Dynamics and constraints of enzyme evolution

“…It should be noted that this review exclusively focuses on the dynamics of enzyme evolution (and more generally protein evolution), and strategies to enhance our ability to engineer and design novel enzymes, although very similar phenomena are observed at higher levels of biological systems (reviewed by Elena and Lenski, ; Phillips, ; Kogenaru et al, ; Koonin and Wolf, ; de Visser et al, ; Kawecki et al, ; Loewe, ).…”

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

“…The ultimate long-term purpose of FlyClockbase is to substantially contribute towards implementing the vision of mechanistic simulations in evolutionary systems biology as detailed elsewhere (L oewe 2009; L oewe 2012; L oewe 2016). Evolutionary systems biology aims to quantify fitness landscapes by mapping genotypes (via realistic fitness causality networks) to phenotypes and ultimately fitness.…”

“…Since circadian clocks have a large impact on fitness, their behavior is of direct evolutionary importance (B eaver et al 2002; B eaver et al 2003; D odd et al 2005; L oewe and H illston 2008; A kman et al 2010; B eaver et al 2010). Constructing a high-quality model of a circadian clock in D. melanogaster could thus provide the opportunity to explore many mutant options in silico (L oewe and H illston 2008) and thus bring us closer to the goal of quantifying fitness landscapes of interest (L oewe 2009; L oewe 2012; L oewe 2016). To enable this vision, myriads of models on the scale of FlyClockbase will need to be constructed, connected and analyzed both individually and in various combinations.…”

“…The 1970’s saw the rise of systems theory in ecology, albeit arguably too early (W olkenhauer 2001). Now systems biology has in principle a computational method at its disposal for every single step along the causality chain from genotype and environment to phenotype and fitness (L oewe 2009; L oewe 2012; L oewe 2016). However, what does not exist at the moment is a rigorous and integrated problem management for the full causality chain.…”

Lazy Updating of hubs can enable more realistic models by speeding up stochastic simulations

“…6,62 A key goal of evolutionary systems biology analyses is to enable the computational prediction of candidate fitness correlates based on the best systems biology models available by integrating all relevant data using the most rigorous analyses available. 63,64 For this goal, it is crucial to integrate hubs such as ATP, volume, and temperature into systems biology models, because the rate of cell growth and the energy spent on keeping a cell alive are important factors that can affect the growth and survival of organisms. 64 Mutations that affect hubs and thereby cell growth or survival by as little as 0.1% can already be major drivers of evolution, as many important outcomes in evolution depend on a fine balance between many small actions that slowly increase or decrease a quantity on the longer-term (e.g., no growth of cells without slowly changing cell volume; no survival in some climates without adjusting bodyheat by increasing metabolic rates).…”