Multiscale digital
            <i>Arabidopsis</i>
            predicts individual organ and whole-organism growth

Chew, Yin Hoon; Wenden, Bénédicte; Flis, Anna; Mengin, Virginie; Taylor, Jasper; Davey, Christopher L.; Tindal, Christopher; Thomas, Howard; Ougham, Helen J.; Reffye, Philippe de; Stitt, Mark; Williams, Mathew; Muetzelfeldt, Robert; Halliday, Karen; Millar, Andrew J.

doi:10.1073/pnas.1410238111

Cited by 92 publications

(90 citation statements)

References 72 publications

(83 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Small networks of "clock genes" drive 24-hour, biological rhythms in eukaryotic model 52 species 1 . A few among thousands of downstream, clock-regulated genes are known to mediate 53 physiological phenotypes, such as the metabolic syndrome of clock mutant animals 10 .…”

Section: (Text 1507 Words) 51mentioning

confidence: 99%

“…Circadian clocks are 34 intracellular regulators that control temporal gene expression patterns and hence metabolism, 35 physiology and behaviour, from sleep/wake cycles in mammals to flowering in plants [1][2][3] . Clock 36 genes are rarely essential but appropriate alleles can confer a competitive advantage 4,5 and have 37 been repeatedly selected during crop domestication 3,6 .…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

Chew

Seaton²,

Mengin

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

Summary paragraphPredicting a multicellular organism’s phenotype quantitatively from its genotype is challenging, as genetic effects must propagate up time and length scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour, from sleep/wake cycles in mammals to flowering in plants1–3. Clock genes are rarely essential but appropriate alleles can confer a competitive advantage4,5 and have been repeatedly selected during crop domestication3,6. Here we quantitatively explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for growth of Arabidopsis thaliana7–9. The model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants. Altered night-time metabolism of stored starch accounted for most but not all of the decrease in whole-plant growth rate. Altered mobilisation of a secondary store of organic acids explained the remaining defect. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits.

show abstract

Section: (Text 1507 Words) 51mentioning

confidence: 99%

mentioning

confidence: 99%

The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

Chew

Seaton²,

Mengin

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Systems or modeling approaches could be used to help delineate these links and to understand the interplay between light signaling, carbon signaling, metabolism, and growth. The Arabidopsis Framework model integrates information from external light inputs, carbon resource production, and allocation to leaves and growth (Chew et al, 2014). This type of modular model could be used to predict the dual action of phytochrome and photosynthesis on resource management and biomass production.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Phytochrome, Carbon Sensing, Metabolism, and Plant Growth Plasticity

et al. 2017

Self Cite

View full text Add to dashboard Cite

“…This work [39] had even been commented in the same issue of the journal as the "dawn of virtual cell biology" [40]. Such large scale models had been attempted in other organisms and had provided insights [41] even for industrial applications [42]. Larger scales of models of various single cell models had been attempted for studying the emergence of microbial ecology [43] and it is not expected for these models to be entirely highly to be useful.…”

Section: All Models Are Wrong But Some Are Usefulmentioning

confidence: 99%

Of (Biological) Models and Simulations

Ling¹

2016

MOJPB

View full text Add to dashboard Cite

All models are wrong, but some are useful. Box and Draper [1].All models are right… Most are useless. Tarpey [2]The role of models has been recognized as an important aspect to the scientific method [3] for more than 70 years [4]. In recent years, there are increasing interests in metabolic modeling in biology [5,6]. The process of building a model is known as modeling. Generally speaking, models can be classified as either material models or formal models. Rosenblueth & Wiener [7] consider material models to be a physical representation of the object under study whereas formal models are symbolic or logical representations of the object under study. For example, a scale down model of an airplane for testing its aerodynamic properties in a wind tunnel is a material model. The main purpose of a scaleddown material model is the ease of study, which had been clearly demonstrated by a study on fireplace more than 200 years ago [8].A formal model, on the other hand, is an abstract but unambiguous description of a physical object or phenomenon, usually in logical or mathematical constructs. Commonly used mathematical constructs for building formal models are rate equations, such as differential equations [9], or state transitions, such as petri nets [10]. Other modeling formalisms had been reviewed by Machado [11]. Besides differentiating models by their underlying formalism, models can also be classified by their informational complexity. At the lowest level, the presence or absence of associations between 2 entities (such as cells or molecules) can be represented as a Boolean term. This results in a binary state petri net or Boolean network, which can be used to study cycles and reachability. For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17]. The main advantage of kinetic models over steady state model is the ability to simulate each state (or molecules in the context of metabolic modeling) across time, in addition to the ability to model homeostasis [12]. This will allow for identification of bottlenecks in the metabolism [18].With advancements in computing power, formal models, being constructed in mathematical or logical format, render it possible for computation. Such computation or execution of formal models is commonly known as simulation. Hence, by definition, one can "build a model", then "run or execute a model" or "run or execute a simulation" but in no means, one can "build a simulation". The ability to render execution of a model; thus, an executable or simulatable model; defines the fundamental difference between a material model an...

show abstract

Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Cited by 92 publications

References 72 publications

The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

Phytochrome, Carbon Sensing, Metabolism, and Plant Growth Plasticity

Of (Biological) Models and Simulations

Contact Info

Product

Resources

About