1. Paper prepared for presentation at the 16 th ICABR Conference – 128 th EAAE Seminar “The Political Economy of the Bioeconomy: Biotechnology and Biofuel” Ravello, Italy, June 24-27, 2012 Pasquale Lucio Scandizzo, Alessandra Imperiali

2. Introduction Literature and review Data and variables Empirical analysis Concluding remarks Outline 25/06/12

3. Investigate the characteristics of genetic networks. Account for the role of desirable traits for modern agriculture. Explore the implication of a model, where the desirable traits depend not only on the properties of the individual genes, but also on their connections and the architecture of the network Aim of this paper 25/06/12 Introduction

4. Is biotechnology research in agriculture making substantial progress along new lines? Are there new paradigms for research in the sector? What is the role of network theory ? Does it promise to yield revolutionary results? Key Questions 25/06/12 Introduction

5. Part 1 Consistent with empirical research on large networks, our findings point to a scale-free relationship between the number of links among genes and the number of genes, with a significant and proportional or more than proportional increase in the percentage of links in response to a percentage increase in nodes inside each co-expression network. This relationship can be interpreted as the result of information exchanges, i.e. as a relationship between the information contained and the information exchanged by the genes. Part 2 Our findings point to a positive, but less than proportional, scale free relationship, between the number of co-expressed genes and the number of genes inside each coexpression network. Results’ overview 25/06/12 Introduction

6. An overview of a Rice coexpression network with 4,495 genes and 32,544 edges (Pearson’s correlation r ≥ 0.93) 25/06/12 Literature and review Source: A.Fukushima et al., “Characterizing gene coexpression modules in Oryza sativa based on a graph-clustering approach”, 2009.

7. From multiplicity of characters to co-expression 25/06/12 Literature and review In the past 15 years, an intense research activity has been directed towards biological Networks, where the Network Theory finds its natural application. These studies have produced remarkable progress in understanding the topological and chemical structures of the genes, and promise to make spectacular improvements to agricultural crops. Today genes can be modified and recombined into the cells of living organisms to improve crop productivity or to make crops more resistant to stress, diseases and chemical treatments (Steven D. Tanksley and Susan R. McCouch, 1997). This new technique is known as recombinant DNA technology, and has allowed scientists to carry out procedures using genes and DNA that are extremely advanced and innovative.

8. From multiplicity of characters to co-expression 25/06/12 Literature and review Recombinant DNA (rDNA) consists of DNA sequences resulting from laboratory methods that bring together genetic material from multiple sources. Scientists succeded in isolating genes responsible for main adaptive and improvement traits and were able to determine their chemical structure, together with their function. This acquired knowledge was then used to develop the potential of our wild and cultivated germ-plasm resources for improving agricultural crops. After spending decades to disassemble nature, and having provided a wealth of knowledge about the individual components, scientists developed a theory of complexity where nothing happens in isolation and most of the characteristics of living being derive from the interactions among their constituents.

9. From multiplicity of characters to co-expression 25/06/12 Literature and review Understanding and unraveling the interactions and the orchestrated activity of many interacting components constitutes a major goal for biologists of the genome era. The network approaches are used to integrate various types of genomics data in order to increase the reliability of predicted interactions. One increasingly important method used to identify interacting gene sets is represented by the construction of gene co-expression networks where traits are the result of cooperative expression (co-expression) of genes, organized according to the topology of networks. Co-expression networks includes genes involved in related biological pathways, which are expressed cooperatively for their functions. It is constructed by determining the tendency of m transcripts to exhibit similar expression patterns across a set of n microarrays.(P. Ficklin and F. Alex Feltus, 2011)

10. Gene co-expression network 25/06/12 Literature and review In gene co-expression networks, each gene corresponds to a node. Two genes are connected by an edge if their expression values are highly correlated. Definition of “high” correlation is somewhat tricky – One can use statistical significance… – Alternatively, one can use a threshold parameter: scale free topology criterion.

11. From multiplicity of characters to co-expression 25/06/12 Literature and review By exploring several large databases describing the topology of large networks, Albert and Barabasi (1999) found that the degree distribution follows a power- law for large k: Where K stands for the average degree of a node i, which represents the number of edges incident with the node. P stands for the probability that a node chosen uniformly at random has degree k. The value of the exponent varies between 2 and 3. Following this approach, the literature indicates that the intricate interwoven relationships that govern cellular functions follow a universal law. They are “scale-free, modular, hierarchical, small worlds of short paths and their connections are highly clustered” (Albert-Laszlo Barabasi and Zoltan N. Oltvai, 2004).

12. Distribution of connections per node in the coexpression network 25/06/12 Literature and review Source: V. van Noort et al, “The yeast coexpression network has a small- world, scale-free architecture and can be explained by a simple model, 2004.”

13. Meta Analysis 25/06/12 Data and variables In the paper we discuss the recombinant DNA techniques and their application to agricultural crops, focusing our attention on the regulatory mechanism in gene interactions. We aim to study the interactions underlying expressed traits, using three crops species: Arabidopsis thaliana, maize (Zea mays) and rice (Oryza sativa) We selected 57 studies aimed to identify the gene co-expression networks among these crops by examining the co-expression patterns of genes over a large number of experimental conditions. We collected both the data and the results presented by the studies on 101 networks.

14. Estimates 25/06/12 Empirical analysis Aim: Identify the correspondence between the underlying genes and the observed traits. We analyzed the role of the number of genes on two different characteristics: The number of edges (L) and the number of coexpressed genes (C). Using the data assembled from the studies, we estimated two different relations by means of ordinary least squares (OLS):

15. Variables used for our estimates 25/06/12 Empirical analysis

16. The Estimates 25/06/12 Empirical analysis Dep. Variable Number of Links (1)(2)(3) Nodes1.13*** (7.65) 0.99*** (7.35) 1.37*** (6.26) Dummy Arabidopsis0.99* (1.75) -1.73* (-1.83) Dummy Biosynthesis Dummy Metabolic Pathways Dummy Photosynthesis2.30** (3.43) 1.57* (1.82) Dummy Seed Development-0.41 (-0.40) Dummy Signaling Pathways1.48** (2.23) Dummy Stress Response0.16 (-1.27) N41 19 R-squared0.610.690.82

17. The Estimates 25/06/12 Empirical analysis Dep. Variable: N. of coexpressed genes (1)(2)(3) Nodes0.60*** (5.44) 0.79*** (10.72) 0.65*** (5.82) Dummy Arabidopsis0.33 (0.85) Dummy Biosynthesis-0.83* (-1.73) Dummy Metabolic Pathways-0.75** (-2.17) -0.53** (-2.81) Dummy Photosynthesis-0.30 (-0.60) Dummy Seed Development Dummy Signaling Pathways-1.24*** (-3.089 Dummy Stress Response-1.25*** (-3.35) -0.90*** (-3.42) -0.83* (-1.73) N5410154 R-squared0.560.610.55

18. Conclusion 25/06/12 Concluding remarks Our Analysis confirms the existence of a scale-free relationship which has been found ubiquitous in complex networks. We found also that as the number of genes increase inside the biological networks considered, the number of co-expressed genes increase, less than proportionally. This finding suggests that a strategy of research aimed to identify relevant clusters of co- expression may be more successful than one aimed at identifying single traits or groups of traits and corresponding gene determinants. Althout this conclusion seems to hold for stress response, metabolic pathways and biosynthesis, which have a lower influence on the number of edges, the intensity of seed development appears to be associated instead with an increase in the connectivity of the network.