Abstract

This is a proposed business model for a systems approach in adapting or optimizing plants towards novel products that briefly explains the growth and recent trends in plant systems using three modeling approaches: L-systems, Dynamical-biosystems analysis and cellular automata. The model discusses some of the attempts made by scientists in this field such as model development in Faba bean crops and the auto regulation in nodulation of plants and some other simulated models (ALAMEDA). Its application in horticulture or life style industry is proposed based on the meristem model applied in the roots of Zea mays. L. A grid based approach that effectively simulates and models competition among chosen species of plants on a simulated environment helps in the study of species survival amidst competition from other species, threat from pests, weeds etc. A model for the ecological risk assessment enables the study of impacts of a newly introduced plant variety on the environment and the implications on plants by the environment that helps in decision making and public acceptance. There are attempts in building the virtual cells, by choosing to apply living cell conditions with some basic cell components working effectively, leading to a thrust in the field of systems biology where this would be an application on all kind of cells in any organisms. A plant competition model in an agronomic perspective uses some of the plant models that help in increase of crop yield in agriculture. Systems biology is an integration of genomics, transcriptomics, proteomics and metabolomics where approaches in identifying the regulatory elements and conserved regulatory motifs have been successfully made leading to a possibility of molecular level study where the input data is scarce and inappropriate beyond which it enables developing hypotheses to decipher poorly understood signaling pathways. A good example of direction and path deduction in plant systems using auxin transport applies best in a well studied business environment enabling a wide range of future perspectives. Some of the novel products or services from plant systems, derived using the systems approaches are briefly discussed. The context of business settings and commercial plausibility is well supported in all attempts of applying the systems biology approach in deriving plant products or services.

Highlights

  • A systems approach to biology provides a wide and in-depth perception in analyzing complex and dynamic bio-systems

  • Three Prospective novel plant system-derived products or services: Some of the novel plant derived products or services could be: 1. A virtual cell environment could be created using the parameterized L-system where the user can define the components of the cell and study the whole system where the user can expect the system to produce the desired outcome

  • A similar approach could be used in developing plant derived medicines where a plant system that secretes some proteins or secondary metabolites which will be used as medicines by optimizing the systems parameter at the input stage of a simulation process such that a particular protein or a direct product or by-product of any described process is obtained in a required quantity

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Summary

Introduction

A systems approach to biology provides a wide and in-depth perception in analyzing complex and dynamic bio-systems. A combination of experimental and computational research provides a complete understanding of complex biological systems [1]. The systems approach in cancers yields numerous solutions at every stage of analysis where identification of key genes involved in cancer using gene regulatory networks is made simpler. Such an approach is used in the identification of the Androgen receptor gene (AR) from the primary and metastatic prostate cancer data and related pathways which conclude that the AR gene and the AR gene pathway are mediators of prostate cancer progression [6]. A vision of personalized medicine can be achieved only through such approaches that well describe the innovative combination of computational and medical knowledge [7]

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