Computational models to represent Biological systems

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Plant physiology is an assembly of different biological phenomenon spanning from intracellular molecular communications to the whole systems phenotypic response (Kanani et al., 2010). Systems biology endures to crack these multi-scale networks and bridge the link among genotype to phenotype. The organization and dynamics of these pathways/networks are responsible for managing the phenotypic state of a cell (Shao et al., 2007; Fraser et al., 2013; Baghalian et al., 2014). A diversity of cells and various tissues coordinate together to produce an organ level responses which further control the physiological states of plant systems. The pathway modeling and analysis for targeting complex diseases expected to find remedies for these diseases. This approach has some advantages such as identification of rational agrochemicals (Fungicide, Herbicide, and Insecticide) targets, effective agrochemical design with least side effects in human and environments, effective management strategies, diagnostic of actual source of disease state treatment of disease sources rather than symptoms, early and reliable diagnosis of disease using predictive models, such an approach has got tremendous potential in agricultural research for improving agricultural productivity (Yin et al., 2008; 2010). Some salient applications of systems biology in agriculture is given below.

Maximizing yield is the aim of scientist working in field of crop improvement and agricultural productivity. The population of world is continuous grow, huge amount of agriculture products are required to save the life of peoples. Exploiting the potential of systems biology for disease management and crop productivity point of view, has currently receiving a lot of attention because recent advances in field of systems biology can decode the complexity of multiple traits concerned with genotypic to phenotypic responses such as photosynthesis, carbon and nitrogen metabolism, water use efficiency, plant architecture and other physiological mechanisms. Researchers working in area of agriculture biotechnology with respect to crop improvement and agriculture productivity can understand the power of computational tool and utilize systems biology resources in their research that can be very useful for development of high yielding crop plants for society.

Agriculture is one of the most important vulnerable sectors to changes in climates, due to its reliance on adequate environmental conditions for achieving high agricultural productivity (Huntingford et al., 2005). Crops plants are affected by shortages or excesses of water or excessively high or low temperatures during growing periods (Porter and Semenov, 2005).

Recently published studies have represented the potential of adaptation strategies design, the two major goals must to be pursued in future studies: (i) a better knowledge of driving processes under future changes in climate; and (ii) a coupling among genetic and crop growth models—perhaps at the expenditure of the number of genes/ traits analysed. Significantly, the latter may imply additional complexity in crop systems modeling studies. Therefore, systems biology approaches will be useful for modularity in crop models as well as individual component testing against observational data would be critical components in any attempts to simulate crop-breeding strategies under future climate scenarios (Ramirez-Villegas et al., 2015).

Plant-pathogen interaction is a well known mechanism, which involves the activation of various signaling pathway with respect to defense response against pathogen. This type of response facilitates the host plant to avoid further infections. Disease resistant and management have always been of the main objective of any crop improvement program (Gururani et al., 2012). Efficient use of systems biology tools for disease resistant and management could not only assist us for better understanding the plant defense signaling, It could disclose new insight on the molecular interactions networks among these signaling pathways and other mechanisms linked with plant protection and agricultural productivity. The different strategies of systems biology can be applied for identification of defense related traits are highlighted below.

The pathogenesis of most diseases involves interaction of various proteins or genes. The network biology approach is being used extensively to identify the candidate genes/proteins responsible for various diseases in crop. Topological analysis of biological network provides majority of the disease genes are non-essential and do not have high degree in protein-protein interaction network and lie at boundary or periphery of the network, while essential genes-encoding most of the hubs lie centrally to the network (Qi et al., 2006; Goh et al., 2007). These observations reveal the existence of disease specific functional component. The network measures and topological analysis will significantly help in deriving productive information from the complex network such as degree, clustering centrality, shortest path connectivity and hubs in the network. This information further assists in designing experimentally or by computer simulation. An outstanding work in human by Lage et al., (2007), they applied such kind of analysis to construct the interactions network of various genes and proteins. Based on network analysis they found a total of 669 linkages out of which 298 correctly ranked the disease causing proteins as top candidates. Network construction and analysis has great potential to identify large number of genes and proteins responsible for diseases and facilitate identification of important traits for agricultural productivity.

Although pathway modeling and analysis are useful in understanding the inter-relation between different biological components (genes, proteins, metabolites) for identification of complex agricultural traits in crop plants. Theoretical analysis of biological pathway has a long history, and has been successfully applied to the analysis of metabolic pathway and physiological processes (Leclercq et al., 1983; Heinrich, 1985). Computational modeling of biological systems is becoming increasingly useful in many area of biological sciences, including in the study of signaling pathways for identification of a growing number interactions within and between signaling pathways in the cell.

Computational models should represent the biological systems as accurately as possible and be able to mimic the behavior of the systems over a wide variety of conditions like various abiotic and biotic stresses in crop plant systems. Collecting information from published literature and databases is one of the most important aspects in the development of computational models. However, often this is not sufficient. As these types of models become more complex, it will require experimental data for accurate validation and analysis, such data should include cellular concentration of the components as well as the kinetic reactions for interactions between components. Systems biology graphical notations (SBGN) are available for modeling of biological systems by using different types of available computational tools (Kitano et al., 2005). SBGN facilitates to researcher with respect to symbol for receptor, gene, protein, etc for the purpose of interlinking of cell components that provides better analysis and visualization of complex agricultural traits.The molecular interaction network comprising of bio-molecules associated with different agriculturally important traits/genes, which influence the network function from genotype to phenotype is known as traits network. The inherent complexity in the molecular interaction networks results in various components yielding an emergent property essential for normal functioning. Recognition of the components required for specific functional properties and perturbation of which results in alteration in the phenotypic response resulting in complex diseases, are essential to identify desired traits (Mackay et al., 2009; Doncheva et al., 2012; Altaf-Ul-Amin et al., 2014; De Vleesschauwer et al., 2014; Xu et al., 2014). Network based approaches indicates that multiple component nodes may be involved in a disease state which further affect the functional modules that are multigeneic in nature. The economic important crop plant disease like Alternaria blight are not single target disease, various genes/proteins of Alternaria brassicae and A. brassicicola are involved during disease progression, which is affected by 10 to 70% of yield losses in different part of the Northern India according to climate conditions (Kumar et al., 2001; Priyanka et al., 2013). There is a need of network based approach for crop systems modeling and analysis, which provide way for development of novel disease management strategy.

Research studies suggested that based on topological analysis of disease network it has been shown that disease genes lie on periphery of the network, which may effectively targeted without much side effects on society and environment (Hase et al., 2009; Zhu et al., 2009). There can be multiple targets at different network levels which can be identified by network dynamics analysis and visualization (Moller 2001). So, identifying the target and designing the potential agrochemical/peptides that can modulate the network response.

The task of network based systems targeting become very difficult that targeting single genes/protein or trait, this needs an in-depth knowledge of the regulatory dynamics of the network and availability of the accurate system parameters. The dynamics simulations of network response can be carried out by performing perturbation analyses using systems biology tool (Table 1), these approaches are continuously evolving with immense potential to discover agriculturally important traits and their behavior in desired conditions similar in health and diseases (Somvanshi et al., 2014).

The main objective of biofortification and nutraceutical development is to enrich the plant foods for essential micronutrients and proteins as plants grow naturally. It has been realized that biofortification of staple food crops would solve the malnutrition problem associated with rural poor. In addition they play essential role in development of functional food (Nutraceuticals) for nutrition, health and well-being by the application of systems biology.

Recently published studies demonstrated that to achieve the goal for providing the crops with additional health benefits on a global scale, much work is required for development of new varieties with enhanced nutritional qualities by using interdisciplinary approaches (Hefferon, 2015), thus systems biology and omics based research can play crucial role in biofortification and nutraceutical development.

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