Programming and Systems Biology Chapter Notes | Biotechnology for Class 11 - NEET PDF Download

Programming in Biology

• Biology has transitioned from manual computation to high-throughput data generation, automated analysis, and prediction due to technological advancements.
• Massive data generation poses challenges in storage, visualization, transfer, analysis, and interpretation.
• Tasks that seemed daunting a decade ago are now trivial due to computational advancements.
• Artificial intelligence (AI) and machine learning (ML) have transformed research practices across fields, including biology.
• Future biotechnology students working at the cutting edge of science will likely need basic programming knowledge, along with familiarity with chemistry and statistical methods.
• Bioinformatics software is developed for various operating systems, but the Linux platform hosts the majority of successful applications.
• PERL has been central to sequence-based large data handling in bioinformatics since its inception.
• Modern platforms are enriched with advanced languages like Python and R, which offer strong statistical packages for solving biological problems.
• Python modules are continuously enhanced with visualization and analysis tools for handling large datasets on standalone systems, web servers, and cloud computing platforms.
• MATLAB provides a robust platform for bioinformatics data analysis.
• Python: A high-level, general-purpose programming language created by Guido van Rossum in 1991. It is object-oriented, interactive, and runs on Unix, Mac, and Windows. Its popularity in bioinformatics stems from clear syntax, expressivity, alignment with object-oriented programming, and availability of libraries and third-party toolkits. It is used for sequence and structure analyses, phylogenetics, and more.
• R: Developed by Robert Gentleman and Robert Ihaka, R is a free, open-source functional programming language ideal for high-volume analysis, visualization, and simulation of biological data. It is widely used for genome sequence analysis and biomolecular pathway studies.
• New programming languages for systems design include GEC (Genetic Engineering of Living Cells), a rule-based language by Microsoft, and Kera, an object-oriented, knowledge-based language by Dr. Umesh P. of the University of Kerala.
• Kera (named after Kerala, meaning coconut) captures genome, protein, and cell information using a user-edited biological library called Samhita.

Systems Biology

• Systems biology is an interdisciplinary field focusing on complex biological interactions within biological systems.
• It involves developing computational and mathematical models to mimic complex biological systems observed in in-vitro, in-vivo experiments, or real life.
• These models, called system models, represent biological systems and are used to study emergent functional properties of cells, tissues, and organisms.
• Systems biology has gained prominence over the last two decades, with applications in health, diseases, biological networks, and modern therapeutics.
• The Human Genome Project was a foundational milestone that spurred the development of modern systems biology.
• Examples of efficient system models include metabolic and signaling networks, which provide insights into biological mechanisms and practical applications.

Historical Perspective

• Before systems biology, biological research (1900–1970) focused on physiology, population dynamics, enzyme kinetics, control theory, and cybernetics as separate components.
• In 1952, Alan Lloyd Hodgkin and Andrew Fielding Huxley (Nobel laureates) developed a mathematical model for action potential propagation along neuronal axons, laying groundwork for systems biology.
• In 1960, Denis Noble created the first computer model of the heart pacemaker, advancing theoretical implementations.
• Systems biology was formally introduced by Mihajlo Mesarovic in 1966 at the Case Institute of Technology in Cleveland, Ohio, under the title "Systems Theory and Biology."
• In 1968, Ludwig von Bertalanffy published the first formal theory of systems biology, considered a precursor to the discipline.
• The 1960s and 1970s saw developments in complex molecular systems, including metabolic control analysis and biochemical systems theory.
• Theoretical biology, involving quantitative modeling of biological processes, bridged systems theory and molecular biology.
• Since the 1990s, functional genomics has generated large quantities of high-quality biological data, enabling more realistic models.
• In 2003, the National Science Foundation (NSF) challenged researchers to mathematically model an entire cell, leading to collaborative efforts with MIT and CytoSolve.
• In 2012, Mount Sinai School of Medicine developed a whole-cell model of Mycoplasma genitalium, predicting cell viability in response to genetic mutations.
• The Physiome Project (http://physiomeproject.org/) is a major systems biology initiative aimed at creating a multi-scale modeling framework for physiological functions, integrating models of ion channels, myofilament mechanics, signal transduction, tissue mechanics, wavefront propagation, and coronary blood flow developed by different research groups.

Theme Behind the Systems Biology

• Systems biology integrates diverse biological disciplines to study systems holistically.
• Reductionist approaches identified system components and interactions but struggled to describe system pluralism.
• Pluralism is better understood through quantitative measures of multiple components simultaneously, achieved via mathematical models with rigorous data integration.
• Systems biology observes systems by integrating components, with a core theme of "object network mapping and its integration with interdependent dynamic event-kinetics using partial differential equations."

Protocol for Systems Biology Experiments

• Systems biology experiments follow a standardized workflow involving discrete steps:
• Define the biological problem to be studied.
• Design experiments to address the problem.
• Execute experiments to generate data.
• Collect and arrange resultant data in appropriate file formats.
• Develop a network interface based on the data.
• Ensure the network interface is precise and mechanism-based to facilitate model development.
• Analyze discrepancies between model-based simulation results and experimental data.
• Refine the hypothesis based on observed discrepancies.
• Repeat simulations, incorporating new hypotheses into the model for iterative testing.
• The computational workflow requires data management, optimization of network development parameters, performance analysis, and evaluation.
• Data Management Standards: Three key aspects are considered:
• Minimum Information: Essential supporting information from experiments (e.g., microarray, proteomic, biological, biomedical investigations). Metadata about collected data is critical.
• File Formats: Data are stored in specific formats, typically Extensible Markup Language (XML)-based, enabling automated computer processing.
• Ontologies: Semantic annotations defining hierarchical relationships between terms. Examples include Gene Ontology (GO) and Systems Biology Ontology (SBO).
• Data Management Systems: Include spreadsheets, web-based electronic lab notebooks (ELN), and laboratory information management systems (LIMS). These systems are customized for integration with analysis tools and computational workflows.
• Workflow Tools: Systems like Konstanz Information Miner (KNIME), caGrid, Taverna, Bio-STEER, and Galaxy enable construction, execution, and sharing of specialized workflows, supporting data exchange, integration, and inter-tool communication.
• Resource Matrix (Table 11.1): Lists tools for various systems biology tasks:
• Data Management: Taverna, MAGE-TAB, Bio-STEER, caGrid.
• Network Inference: MATLAB, R, BANJO.
• Curation: CellDesigner, PathVisio, Jdesigner.
• Simulation: MATLAB, CellDesigner, insilico IDE, ANSYS, JSim.
• Model Analysis: MATLAB, BUNKI, COBRA, NetBuilder, SimBoolNet.
• Molecular Interaction: AutoDock Vina, GOLD, eHiTS.
• Physiological Modelling: PhysioDesigner, CellDesigner, OpenCell, FLAME.
• Systems modeling tools use interconnected partial differential equations (PDEs) to represent spatiotemporal systems, solved using the Finite Element Method (FEM) via tools like ANSYS, FreeFEM++, OpenFEM, and MATLAB.
• Other modeling tools include JSim, OpenCell, and Flexible Large-scale Agent-based Modelling Environment (FLAME), with ongoing development of tools for more realistic simulations.

Model-Analysis Methods

• Sensitivity Analysis: Examines system stability and controllability against distractions. Tools include SBML-SAT, MATLAB SimBiology, ByoDyn, and SensSB.
• Bifurcation and Phase-Space Analysis: Analyzes system models to identify steady and dynamic tendencies. Tools include AUTO, XPPAut, BUNKI, and ManLab.
• Metabolic Control Analysis (MCA): Studies relationships between metabolic network properties at steady state and component reactions. MetNetMaker is a key tool for MCA.