<h5 id="111-programining-in-biology">11.1 Programining in Biology</h5> <ul> <li>The advent of high-throughput data generation in biology has led to an increased need for automated analysis and prediction.</li> <li>Popular high-level programming languages relevant to biologists include PERL, Python, and R, with the majority of successful applications being developed on the Linux platform.</li> <li>Python is widely used in bioinformatics due to its clear meaning of terms, expressivity, alignment to object-oriented programming, and the availability of libraries and third-party toolkits.</li> <li>R is a functional programming language that is ideal for high volume analysis, visualization, and simulation of biological data. It has been used for genome sequence and biomolecular pathway analysis.</li> <li>New programming languages such as GEC and Kera have emerged for designing systems, with Kera capturing information on the genome, proteins, and the cell using a user-edited biological library called Samhita.</li> </ul> <p>======</p> <h5 id="112-systems-biology">11.2 Systems Biology</h5> <ul> <li>Systems biology is an interdisciplinary field that focuses on the integration of various biological data to understand the complex behavior of biological systems.</li> <li>It involves the use of mathematical and computational models to simulate and predict the dynamics of biological systems.</li> <li>The systems biology approach often involves the following steps: data collection, model building, model simulation, and model analysis.</li> <li>Equations and formulae are crucial in systems biology, as they represent the relationships between different components of the system.</li> <li>The ultimate goal of systems biology is to understand how biological systems function as a whole, and how they respond to different stimuli and perturbations.</li> </ul> <p>======</p> <h5 id="1121-introduction">11.2.1 Introduction</h5> <ul> <li>Scientists have been conducting experiments and recording findings in the form of data for centuries. A large amount of biological data is now stored digitally in databases.</li> <li>Systems biology uses mathematical and computational models to mimic complex biological systems, also called system models. It is an interdisciplinary field that focuses on complex biological interactions within biological systems.</li> <li>Systems biology can provide theoretical descriptions for the discovery of emergent functional properties of cells, tissues, and organisms, similar to those possible only through experiments.</li> <li>The most efficient system models include metabolic or signaling networks.</li> <li>Systems biology is being applied in various biological contexts, particularly from the last two decades, with a focus on health and diseases from biological networks to modern therapeutics.</li> </ul> <p>======</p> <h5 id="1122-historical-perspective">11.2.2 Historical perspective</h5> <ul> <li>The historical perspective of systems biology can be traced back to the 1900s, where research was focused on various segmental components such as physiology, population dynamics, enzyme kinetics, control theory, and cybernetics.</li> <li>The formal evolution of systems biology is attributed to the development of a mathematical model for action potential propagation along the axon of a neuronal cell by Alan Lloyd Hodgkin and Andrew Fielding Huxley in 1952.</li> <li>The first computer model of the heart pacemaker was developed by Denis Noble in 1960, and systems biology was formally launched by systems theorist Mihajlo Mesarovic in 1966.</li> <li>The 1960s and 1970s saw the development of multiple aspects of complex molecular systems, including metabolic control analysis and biochemical systems theory.</li> <li>The systems biology project ‘Physiome’ is currently running, aimed at developing a multi-scale modelling framework for understanding physiological function, allowing models to be combined and linked in a hierarchical fashion.</li> </ul> <p>======</p> <h5 id="1123-theme-behind-the-systems-biology">11.2.3 Theme behind the systems biology</h5> <ul> <li>Systems biology is an interdisciplinary field that focuses on complex biological interactions within biological systems.</li> <li>It aims to cover diverse disciplines of biology and has been observed from different aspects.</li> <li>The reductionist approach, which focuses on identification of components and interactions, has been used but a convincing method to describe the pluralism of systems has not been developed.</li> <li>Pluralism can be better observed through quantitative measures of multiple components simultaneously, which can be achieved through mathematical models containing rigorous data integration.</li> <li>The core theme of systems biology is ‘Object network mapping and its integration with interdependent dynamic event-kinetics with partial differential equations’.</li> </ul> <p>======</p> <h5 id="1124-protocol-for-systems-biology-experiments">11.2.4 Protocol for systems biology experiments</h5> <ul> <li>The text is about data management and systems modeling tools.</li> <li>Partial differential equations (PDEs) are used to represent spatiotemporal systems and are solved by the Finite Element Method (FEM).</li> <li>Tools for solving PDEs include ANSYS, FreeFEM++, OpenFEM, and MATLAB.</li> <li>Other simulation tools mentioned are JSim, OpenCell, and FLAME.</li> <li>The development of more sophisticated simulation tools is ongoing.</li> </ul> <p>======</p> <h5 id="1125-model-analysis-methods">11.2.5 Model-analysis methods</h5> <ul> <li>Sensitivity analysis is a method to study the stability and controllability of a system using tools such as SBML-SAT, MATLAB SimBiology, ByoDyn, and SensSB.</li> <li>Bifurcation and phase-space analysis is performed to analyze the system model for steady and dynamic tendencies using tools such as AUTO, XPPAut, BUNKI, and ManLab.</li> <li>Metabolic control analysis (MCA) is used to understand the relationship between the properties of a metabolic network and component reactions using tools such as MetNetMaker.</li> <li>Programming languages like Python and R are useful for biologists to handle complex datasets for visualization and analysis.</li> <li>Data management, optimizing network development parameters, performance analysis, and evaluation are important requirements for computation in systems biology.</li> </ul> <p>======</p>