Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Genetics and analysis of quantitative traits

I. the origin of species by means of natural selection

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Plasmodium falciparum resistance to artemisinin derivatives in southeast Asia threatens malaria control and elimination activities worldwide. To monitor the spread of artemisinin resistance, a molecular marker is urgently needed. Here, using whole-genome sequencing of an artemisinin-resistant parasite line from Africa and clinical parasite isolates from Cambodia, we associate mutations in the PF3D7_1343700 kelch propeller domain ('K13-propeller') with artemisinin resistance in vitro and in vivo. Mutant K13-propeller alleles cluster in Cambodian provinces where resistance is prevalent, and the increasing frequency of a dominant mutant K13-propeller allele correlates with the recent spread of resistance in western Cambodia. Strong correlations between the presence of a mutant allele, in vitro parasite survival rates and in vivo parasite clearance rates indicate that K13-propeller mutations are important determinants of artemisinin resistance. K13-propeller polymorphism constitutes a useful molecular marker for large-scale surveillance efforts to contain artemisinin resistance in the Greater Mekong Subregion and prevent its global spread.

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A molecular marker of artemisinin-resistant Plasmodium falciparum malaria

For a long time biologists have used visual representations of biochemical networks to gain a quick overview of important structural properties. Recently SBGN, the Systems Biology Graphical Notation, has been developed to standardise the way in which such graphical maps are drawn in order to facilitate the exchange of information. Its qualitative Process Diagrams (SBGN-PD) are based on an implicit Process Flow Abstraction (PFA) that can also be used to construct quantitative representations, which can be used for automated analyses of the system. Here we explicitly describe the PFA that underpins SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to capture the quantitative details of a biochemical reaction network. We implemented SBGNtext2BioPEPA, a tool that demonstrates how such quantitative details can be used to automatically generate working Bio-PEPA code from a textual representation of SBGN-PD that we developed. Bio-PEPA is a process algebra that was designed for implementing quantitative models of concurrent biochemical reaction systems. We use this approach to compute the expected delay between input and output using deterministic and stochastic simulations of the MAPK signal transduction cascade. The scheme developed here is general and can be easily adapted to other output formalisms.

Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA

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Modelling and Simulation of Biological Processes The importance of evoluti nary processes in shaping sequence patterns in genomes, as well as origin and extinction of species is well established. As the last century of research has shown, progress in these areas heavily depends on our ability to quantify verbal arguments in order to rank the significance of various ongoing processes. Most of the work in this area traditionally employs analytical mathematics, usually applying a considerable number of simplifying assumptions to become tractable. The last two decades have seen the rise of computer simulations that allow quantification for some previously intractable problems. However, solid simulation work often needs to explore a wide range of parameter space and needs enough independent repeats to allow rigorous quantification in the case of stochastic simulations. This is where problems typically arise: The conceptually high accuracy of simulated models is frequently lost due to simplifying assumptions needed to satisfy constraints in computational complexity.

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Evolution@home: Global computing quantifies evolution due to Muller's ratchet

Circadian clocks are oscillatory genetic networks that help organisms adapt to the 24-hour day/night cycle. The clock of the green alga Ostreococcus tauri is the simplest plant clock discovered so far. Its many advantages as an experimental system facilitate the testing of computational predictions. 
We present a model of the Ostreococcus clock in the stochastic process algebra Bio-PEPA and exploit its mapping to different analysis techniques, such as ordinary differential equations, stochastic simulation algorithms and model-checking. The small number of molecules reported for this system tests the limits of the continuous approximation underlying differential equations. We investigate the difference between continuous-deterministic and discrete-stochastic approaches. Stochastic simulation and model-checking allow us to formulate new hypotheses on the system behaviour, such as the presence of self-sustained oscillations in single cells under constant light conditions. 
We investigate how to model the timing of dawn and dusk in the context of model-checking, which we use to compute how the probability distributions of key biochemical species change over time. These show that the relative variation in expression level is smallest at the time of peak expression, making peak time an optimal experimental phase marker. Building on these analyses, we use approaches from evolutionary systems biology to investigate how changes in the rate of mRNA degradation impacts the phase of a key protein likely to affect fitness. We explore how robust this circadian clock is towards such potential mutational changes in its underlying biochemistry. Our work shows that multiple approaches lead to a more complete understanding of the clock.

Complementary approaches to understanding the plant circadian clock

While biochemistry evidently affects the growth rate of cells, many biochemists routinely ignore population variation, just like population geneticists usually ignore causal details of biochemistry that underpin a change in growth rate caused by a mutation. A true EvoSysBio integration requires an explicit mechanism for how molecular reaction rates affect the reproduction rates that determine the fitness of an organism. Here we simulate a very simple and completely explicit Continuous-Time Markov Chain (CTMC) model of cancer cells whose growth rate is affected by the biochemical equilibrium between two molecular complexes. Approximately 70% of breast cancers are of a type that overexpress Estrogen Receptor-alpha (ERα). Cell growth in this type of cancer is inhibited by hormonal therapies that antagonize ERα function as a transcription factor. ERα is encoded by the ESR1 gene, which itself is a target of ERα mediated transcription. When activated by estrogen, ERα binds to the ESR1 promoter, repressing new synthesis of ERα protein. Estrogen binding also induces pathways that lead to degradation of ERα protein. This negative feedback loop is finely tuned to natural levels of estrogen and results in natural levels of growth. In breast cancer, the system is thrown off its natural course such that increased levels of ERα induce levels of cell growth that can lead to cancer. Thus, both genetic changes to the ESR1 promoter, ERα protein degradation, and biochemical changes in estrogen metabolism can effectively cause changes in cell growth rates, which can be seen as the ‘fitness’ of a cancer cell. Predicting cancer cell growth in this system raises a conceptual multi-level simulation problem, because the molecular aspects of this model need to compute the biochemistry in a way that influences growth rates at the cellular level, without resetting growth at each cell division. We present progress towards addressing this simulation challenge in pure mass-action models, which we implemented using the Evolvix model description language. We found that such models can be constructed in more than one way. We explored some candidate model properties that could aid efforts to develop abstractions for more efficiently simulating the common multi-level modeling problems behind many important biological questions. These efforts are ongoing and aim to find efficient ways of encoding and exploring such models in silico. In particular, we are investigating how architecting a new compiler for a general-purpose programming language for biology could improve the efficiency of analyzing the dynamic multi-level simulation scenarios that characterize many questions in EvoSysBio. Progress can be followed at http://evolvix.org.

Laurence Loewe

Papers

Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA

Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA

Evolution@home: Global computing quantifies evolution due to Muller's ratchet

Complementary approaches to understanding the plant circadian clock

Evolutionary Systems Biology integration of multi-level CTMC interaction models of biochemistry and cancer cell growth using Evolvix.