Plenary Speakers

Institute of Botany, University of Basel Schönbeinstrasse, Switzerland.
Abstract--In this article I summarize a number of principles that control plant growth under natural growth condition. The main message is that under most environmental conditions, photoassimilate provision is less constrained than structural carbon investment. This means that the controls of carbon investment need to be modeled as a priority over carbon acquisition (photosynthesis). The dominance of carbon sink control over carbon source control is particularly pronounced under drought and low temperature. The fine tuning between C-sink and C-source activity makes it difficult to show which is the dominant driver. Experimental research (manipulation of plant and environmental conditions) and studies along environmental gradients show that, except for shade condition, C is rarely a limiting resource at current atmospheric CO₂ concentrations. Mutual shading in dense plant stands has to be viewed in an evolutionary, rather a leaf carbon balance context only.

Pr. Jim Hanan

The University of Queensland, Australia.
Abstract--Functional structural models are useful as a thinking tool for plant science research where spatial elements play an important role, both internally and in the local environment. But the resulting models of plant canopy also provide a platform for modelling interaction with other entities in the environment. Systems for simulating insect-plant interactions and spray-canopy interactions are described, as examples.

Dr. Xinyou Yin

Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University, Netherlands.
Abstract--Plant biologists, agronomists and breeders alike have been constantly facing challenges in narrowing genotype- phenotype gaps. Plant systems biology, as first recognized, seems to target those phenotypes at molecular, sub-cellular, or cellular levels. To emphasize the importance of bridging this gap for understanding and directionally modifying phenotypes relevant to the real-world challenges for agriculture, the concept ‘crop systems biology’ seems more appropriate. This new concept acknowledges the complementarity of the roles of modern plant biology, traditional crop physiology and advanced crop modelling in improving yield and resource use efficiencies of major crops. As a first step, biochemical modules of photosynthesis and molecular marker-based quantitative trait locus information were incorporated into existing crop models. These case studies underline that current modelling shows promise in studying complex crop traits. For further progress, crop models should be upgraded based on understandings of complicated phenomena at lower organizational levels. We expect that this crop systems biology approach will ultimately be instrumental in realizing the expected roles of in silico modelling in narrowing genotype- crop phenotype gaps, and in understanding genotype-by- environment interactions at crop level.

Pr. Eric Mjolsness

Department of Computer Science, University of California Irvine, USA.
Abstract--We survey useful ingredients for a new class of mathematical process-modeling languages aimed at spatial and developmental biology. Existing modeling languages for computational systems biology do not fully address the problems of spatial modeling that arise in morphodynamics (the local dynamics of form) and its applications to biological development. We seek to extend the operator algebra semantics approach from our previous “Dynamical Grammars” modeling language, whose most spatial object type is the labeled graph, to encompass more ﬂexible topological objects. Taking clues from current developments in 3D meshing and from topological modeling for biology, illustrated by a plant tissue example, we seek language support for the approximation of low-dimensional CW complexes (which are nontrivial topological spaces, with cardinality of the continuum) and dynamic ﬁelds thereon, by ﬁnite labeled abstract complexes. Some of the proposed types would be computationally demanding, without further restriction. Restrictions and control of these approximations can be speciﬁed by use of “metricated” types. Minimally, such approximations should permit the accurate simulation of spatial diffusion processes.

Professor B. Larry Li

Ecological Complexity and Modeling Lab, University of California at Riverside, USA.
Abstract--Plant growth processes are much more complex, nonlinear, non-stationary, multi-scale and -modulated than we usually expect them to be. Modeling plant growth requires advanced mathematics that is capable to scale and integrate the processes in space, time and organizational hierarchical fashion. In this talk, I start with two basic biological and physical principles to show how metabolic scaling emerges, and use PDEs to characterize multiple scale spatial structure of plant cell shape formation as it regulates differentiation, behavior, and function of specific cells as well as organ and tissue development and morphogenesis in multicellular organisms. At population level, I show how the stochastic nature of ecological interactions among individuals, due to spatial field effects such as the availability of neighborhood resources at the microscopic level, leads to self-thinning at the macroscopic level. I will also discuss some difficulties and challenges we face in modeling modularity and criticality in complex plant growth processes.