Modeling genetic responses to temperature and
photoperiod in the control
Judith Roe, Steven Welch, William Hsu, and Sanjoy Das
The goal of this project is to incorporate genetic information including Arabidopsis mutant phenotypes, temperature and photoperiod dependence of genes, and global gene expression patterns into predictive mathematical models of plant development.
Two approaches will be used to reach our goal; one is to collect data on the response of Arabidopsis thaliana to temperature and photoperiod changes, and the other is to then incorporate this data and other publicly available data into predictive mathematical models. Initially, we developed a static model of flowering time in Arabidopsis using a neural network approach that was based on the known qualitative genetic network of regulatory gene interactions and novel data on the response of mutants to different temperature regimes (Welch et al., 2003). The pathways involved in flowering time integrate signals from photoperiod and temperature, as well as endogenous information including hormones (for a recent review see Putterill et al. 2004). We have subsequently developed a dynamic differential equation model and validated its predictive ability with a large independent data set (Dong et al., 2004). This work will be presented at the conference "Mathematical modelling of development and gene networks", in Warwick, England on 27-28 May 2004, hosted by the Interdisciplinary Programme for Cellular Research (IPCR).
During our study on flowering time genes in Arabidopsis, we have uncovered some unique temperature dependent responses of plants with mutations in genes involved in this process (Welch et al., 2003). One of these, CONSTANS, is downstream in the pathway which integrates input from daylength (photoperiod) and the circadian oscillator (Samach and Coupland, 2000). This suggests we should investigate the temperature dependence of the expression of this and other integrator genes. A second mutant in another input pathway, the autonomous pathway, also showed differential temperature sensitivity. These two sets of genes have a major influence on plant development, and therefore, on its interaction with the environment.
We will gather expression data for these and other flowering time genes under different temperature and photoperiod regimes using real-time PCR, to be complemented with microarray analysis. Erika Charbit is the postdoctoral fellow conducting this research. Together, these responses will be used for predicting the behavior of plants by mathematical modeling. Incorporated into crop developmental models, these results will be useful to global change modeling as well as the predictions of response of crop plants to environmental change (Hanks and Ritchie, 1991; Rosensweig et al., 1996; Mearns et al., 1997; Rosenzweig and Hillel, 1998).
In addition to the above strategies, we are seeking ways to improve the efficiency of this process either through enhanced algorithms or by the inclusion of other types of biological data (Welch et al., 2004). In addition, we hope to extend this work by applying network computational modeling algorithms to extract novel genetic relationship information from gene expression data.
Dong, Z., Welch, S.M., and Roe, J.L. (2004) A dynamic gene-to-phenotype model to predict the photothermal regulation of flowering time in Arabidopsis thaliana. Manuscript in preparation.
Hanks, J., and J.T. Ritchie. 1991. Modeling plant and soil systems. ASA, CSSA, SSSA. Madison, WI.
Mearns, L. O., Rosenzweig, C., and Goldberg, R.. Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Climatic Change, 35(4):367-396, 1997.
Putterill, J., Laurie, R., Macknight, R. 2004. It’s time to flower: the genetic control of flowering time. Bioessays 26:363-73.
Rosenzweig, C. and Hillel, D. 1998. Climate Change and the Global Harvest: Potential Impacts of the Greenhouse Effect on Agriculture. New York: Oxford University Press.
Rosenzweig, C., J. Philips, R. Goldberg, J. Carroll, and T. Hodges. 1996. Potential impacts of climate change on citrus and potato production in the US. Agric. Syst. 52:455-479.
Samach, A. and Coupland, G. 2000. Time measurement and the control of flowering in plants. BioEssays 22:38-47.
Welch, S.M., Roe, J.L., Dong, Z. 2003. A genetic neural network model of flowering time control in Arabidopsis thaliana. Accepted with minor revision. Agronomy J. 95:71-81.
Welch, S.M., Roe, J.L., Kirkham, M.B., Das, S., He, R., and Dong, Z. 2004. Merging Genomic Control Networks and Soil-Plant-Atmosphere Continuum (SPAC) Models. Agricultural Sys (accepted pending minor revisions).
Koduru, P., S. Das, S.M. Welch, J.L. Roe. “A Multi-objective GA-Simplex Hybrid Approach for Gene Regulatory Network Models.” IEEE International Congress on Evolutionary Computation, Portland, Oregon, pp. 2084-2090, June 2004.
Koduru, P., S. Das, S. M. Welch, J. L. Roe. “Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models”, Lecture Notes in Computer Science: Proceedings of the Genetic and Evolutionary Computing Conference, Seattle, Washington, (Eds. Kalyanmoy Deb et al.), Springer-Verlag, Vol 3102, pp. 356-367, 2004.
Welch, S.M., J.L. Roe, M.B. Kirkham, S. Das, R. He, Z. Dong. Incorporating Genomic Science into Soil-Plant Atmospheric Continuum Studies. Agricultural Systems, in press.
Welch, S.M., Z. Dong, J.L. Roe, S. Das. Modeling gene networks controlling transition to flowering in Arabidopis. Australian Journal of Agricultural Research. Submitted.
Welch, SM, J.L. Roe, S. Das, Z. Dong, R. He, M.B. Kirkham (2004). Merging genomic control networks and Soil-Plant-Atmosphere-Continuum (SPAC) Models. Agricultural Systems. In press.
Welch, S, J Roe, Z Dong (2003). A genetic neural network model of flowering time control in Arabidopsis thaliana. Agron. J., 95:71-81.