4^th Annual Crops in silico Symposium & Hackathon

Opening Keynote: May 1

Symposium and Workshop: May 2-3

Hackathon: May 3

Urbana, Illinois

Symposium & Hackathon Agenda

Graeme Hammer

Crop Science Professor and Director of the Centre for Crop Science in the Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland, Australia

Biological Reality and Parsimony in Crop Models – Why We Need Both in Crop Improvement!

The potential to add significant value to the rapid advances in plant breeding technologies associated with statistical whole genome prediction methods is a new frontier for crop physiology and modelling. Yield advance by genetic improvement continues to require prediction of phenotype based on genotype, and this remains challenging for complex traits despite recent advances in genotyping and phenotyping. Crop models that capture physiological knowledge and can robustly predict phenotypic consequences of genotype-by-environment-by-management (G*E*M) interactions have demonstrated potential as an integrating tool. But does this biological reality come with a degree of complexity that restricts applicability in crop improvement? Simple, high speed, parsimonious models are required for dealing with the thousands of genotypes and environment combinations in modern breeding programs. In contrast, it is often considered that greater model complexity is needed to evaluate potential of putative variation in specific traits in target environments as knowledge on their underpinning biology advances. Is this a contradiction leading to divergent futures? Here it is argued that biological reality and parsimony do not need to be separable. It is further asserted that the structure needed for the next generation of crop models to be most effective perhaps requires both jointly, along with the capacity to evolve in crop growth and development process algorithms. Specific examples in modelling photosynthesis from biochemical scale and canopy development from plant scale are used to highlight the concepts presented.

Hammer conducts research on the physiology and genetics of complex adaptive traits in field crops with a focus on water productivity in cereals. His research underpins the development of mathematical models of crop growth, development and yield that enable simulation of consequences of genetic and management manipulation of crops in specific target environments. His research approach provides unique opportunities to aid crop management and design for enhanced production in water-limited environments; enhance the utility of molecular breeding for drought adaptation; and identify avenues to cope with climate risks in field crop production. He is a Fellow of the Australian Agriculture Institute and was awarded the Australian Medal for Agricultural Science in 2013 and Farrer Memorial Medal in 2012.

Bedrich Benes

Computer Graphics Technology and Computer Science Professor,
Purdue University

Dr. Benes works in generative methods for geometry synthesis, and his main focus is in procedural, inverse procedural modeling, and simulation of natural phenomena. He has published over 140 research papers in the field and he has been sponsored by the National Science Foundation, NASA, Adobe Research, Intel, Siemens, Samsung, Department of Energy, and Ford Inc., among others. Bedrich is Purdue University faculty scholar, and a director of the High Performance Computer Graphics Laboratory.

Longyun Guo

Biochemistry Ph.D. Candidate,
Purdue University

Longyun Guo works on the mathematical modeling of secondary metabolism in plants. He obtained his B.S. in bioinformatics from Shanghai Jiao Tong University in 2012. He is completing his Ph. D in Biochemistry at Purdue under the advisement of Prof. John Morgan. Currently he focuses on the modeling of lignin biosynthesis, since lignin content and composition are engineering targets for biofuel production. Arabidopsis is an excellent model system as most enzymatic steps are well characterized and hundreds of gene regulatory interactions are reported.  A dynamic model of lignin biosynthesis was developed by integrating different types of information, which contributes to deeper understanding of underlying regulatory mechanisms, and help to provide a rational design for lignin biosynthesis manipulation. 

Christopher Hwang

Agricultural Biological Engineering Ph.D. Candidate,
University of Florida

Chris works on bringing genetic information into crop growth models. He obtained his B.S. in Agricultural Biological Engineering (ABE) from the University of Illinois at Urbana Champaign in 2014, and completed his M.S. in ABE from the University of Florida in 2016. He is completing his Ph.D. in ABE at the University of Florida with Dr. Melanie J. Correll. Currently, Chris’ focus is on incorporating nonlinear gene-by-environment interactions into phenology models that can be implemented in existing crop growth model frameworks. 

Mao Li

Postdoctoral Associate,
Donald Danforth Plant Science Center

Mao Li is a mathematician who specializes in plant science. She is a Postdoc Associate in Topp Lab at Donald Danforth Plant Science Center. Her research focuses on quantitative data analysis, development of mathematical methods such as persistent homology based approaches, and computational algorithms to quantify plant morphology, below and above ground, from microbe to the globe.

Steve Long

Plant Biology and Crop Sciences Professor,
University of Illinois at Urbana-Champaign

Stephen (Steve) P. Long is an environmental plant physiologist who studies how to improve photosynthesis—the process of turning sunlight and carbon dioxide into the sugars that drive yield—to increase the yield of food and biofuel crops. Steve has added to our understanding of the long-term impacts of climate change, such as rising levels of carbon dioxide and ozone on crops. Currently, he is the Ikenberry Endowed University Chair of Crop Sciences and Plant Biology at the University of Illinois and Distinguished Professor in Crop Sciences at Lancaster University.

James Schnable

Agronomy & Horticulture Assistant Professor,
University of Nebraska at Lincoln

James’ research group focuses on using both comparative genomics and new high-throughput phenotyping techniques to link genes to functions in maize, sorghum, and allied species such as foxtail and pearl millet. His lab is currently funded by grants from the National Science Foundation, The U.S. Department of Agriculture, ARPA-E, the Nebraska Corn Growers Association, and private industry. During his time at the University of Nebraska, he has collaborated with statisticians, engineers, biochemists, and applied plant breeders resulting in 43 peer-reviewed papers over the past four years. James has founded or co-founded three startups, including Dryland Genetics — which uses advanced breeding methods to develop higher yielding cultivars of naturally water use efficient crops. 

Lee Sweetlove

Plant Sciences Professor,
University of Oxford

The goal of the research in the Sweetlove group is to generate sufficient understanding of plant metabolic networks that they can be rationally engineered for the improvement of crop productivity and resource utilization. His background is in experimental plant biochemistry, but over the last decade his group have increasingly been using large-scale computational models of plant metabolic networks, testing the findings and hypotheses that emerge from the models with multi-gene transgenic plants.

Addie Thompson

Plant, Soil, and Microbial Sciences Assistant Professor,
Michigan State University

Addie Thompson’s lab is interested in studying maize, and how different genotypes grow in different environments. They use many technologies and approaches to investigate this area, from quantitative genetics to phenomics to statistical and physiological modeling. Emphasis is placed on addressing biologically meaningful and agriculturally relevant questions, with both domestic and international potential applications.

Diane Wang

Geography Post-doctoral Associate,
The State University of New York – Buffalo

Diane Wang’s research aims to understand the genetic and physiological controls that underlie whole plant genotype by environment interaction. She received her doctorate from Cornell University in Plant Breeding and Genetics on an NSF graduate fellowship to study natural genetic variation in rice. Her current work integrates experimentation, numerical plant modeling, and genomic analyses towards enhancing understanding of dynamic plant response to abiotic stress across various crops such as Brassica rapa and cotton.

Xiyu Yang

Horticulture Ph.D. Candidate,
Pennsylvania State University

Xiyu is interested in simulating root traits that would benefit plant nutrient and water acquisition via SimRoot, a structural functional model that simulates root architecture and growth. His current focus is to simulate two traits in maize roots, larger cortical cell size (CCS) and reduced cortical cell file number (CCFN), and their interaction with the presence of root cortical aerenchyma (RCA) improving growth under suboptimal N, P and K conditions.

Meagan Lang

Postdoctoral Research Associate in the National Center for Supercomputing Applications at the University of Illinois

Steve Long

Departments of Plant Biology and Crop Sciences, University of Illinois at Urbana-Champaign

Amy Marshall-Colón

Department of Plant Biology, University of Illinois at Urbana-Champaign

Traci Quigg Thomas

Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign

Matt Turk

School of Information Sciences and Department of Astronomy, University of Illinois at Urbana-Champaign