Biotechnology: Class 11 — Chapter 11: Programming and Systems Biology

The transition from manual computation to high-throughput data generation has revolutionized biological research, introducing massive datasets that require computational tools for storage, visualization, transfer, analysis, and interpretation.

• High-Throughput Data: Generated by modern technological advancements, creating massive datasets unthinkable a few decades ago.
• Artificial Intelligence (AI) & Machine Learning (ML): Transforming research practices across all scientific fields.
• Biotechnology Skillset: Future biotechnologists require basic programming knowledge, statistical methods, and chemistry to work at the cutting edge.

• Python: Created by Guido van Rossum (1991). A high-level, general-purpose, object-oriented, interactive language. Runs on Unix, Mac, and Windows. Highly popular in bioinformatics due to clear syntax, expressivity, and extensive libraries/third-party toolkits. Used for sequence/structure analysis and phylogenetics.
• R: Developed by Robert Gentleman and Robert Ihaka. A free, open-source functional programming language ideal for rapid, reliable high-volume analysis, visualization, and simulation of biological data. Widely used for genome sequence and biomolecular pathway analysis.
• GEC (Genetic Engineering of living Cells): A rule-based language developed by Microsoft for designing biological systems.
• Kera: An object-oriented, knowledge-based programming language developed by Dr. Umesh P. (University of Kerala). Captures information on genomes, proteins, and cells using a user-edited biological library called Samhita. (Note: "Kera" is short for Kerala and means coconut).

Systems biology is an interdisciplinary field of study focusing on complex biological interactions within biological systems, representing them through mathematical and computational models.

• System Models: Mathematical and computational models designed to mimic complex biological systems observed in real in-vitro or in-vivo experiments.
• Human Genome Project: One of the most glorious seedings of systems biology, opening new avenues for modern research.
• Emergent Properties: Models provide theoretical descriptions to discover emergent functional properties of cells, tissues, and organisms (e.g., metabolic or signaling networks).

1. 1900–1970: Pre-systems biology era focused on physiology, population dynamics, enzyme kinetics, control theory, and cybernetics.
2. 1952: Alan Lloyd Hodgkin and Andrew Fielding Huxley described a mathematical model for action potential propagation along a neuronal axon.
3. 1960: First computer model of the heart pacemaker developed by Denis Noble.
4. 1966: Systems biology formally launched by systems theorist Mihajlo Mesarovic at the Case Institute of Technology under the title "Systems Theory and Biology".
5. 1968: First theory about systems biology published by Ludwig von Bertalanffy.
6. 1960s–1970s: Development of metabolic control analysis and biochemical systems theory.
7. 1990s: Functional Genomics began generating large quantities of high-quality biological data, enabling realistic models.
8. 2003: Massachusetts Institute of Technology (MIT), in association with CytoSolve, began addressing the NSF challenge to mathematically model a whole cell.
9. 2012: First whole-cell model of Mycoplasma genitalium (a cell-wall-less bacterium) developed by Mount Sinai School of Medicine to predict cell viability in response to genetic mutations.
10. Present: The Physiome Project provides a multi-scale modeling framework to combine and link physiological models hierarchically (e.g., linking heart electromechanical models with subcellular ion channels).

Systems biology shifts the focus from reductionism to pluralism, integrating individual components to observe the system as a whole.

Core Theme: "Object network mapping and its integration with interdependent dynamic event—kinetics with partial differential equations."

• Reductionism: Focuses on identifying individual components and interactions.
• Pluralism: Observed through simultaneous quantitative measurements of multiple components, integrated via rigorous mathematical models.

Standard systems biology experiments follow a structured, iterative workflow:

1. Define the Problem: Establish the biological question or hypothesis.
2. Design & Execute Experiments: Generate high-quality biological data.
3. Data Collection & Formatting: Arrange data in appropriate, standardized file formats.
4. Network Inference: Develop a precise, mechanism-based network interface.
5. Model Development: Construct the computational systems biology model.
6. Simulation & Discrepancy Analysis: Compare model-based simulation results with experimental data.
7. Model Update: Refine the hypothesis and update the model based on observed discrepancies.
8. Iterate: Repeat simulations and incorporate new hypotheses.

Three basic aspects are considered for data management in systems biology:

• (i) Minimum Information: Essential supporting information needed from experiments (e.g., microarray, proteomic, biological, and biomedical investigations), including metadata.
• (ii) File Formats: Stored in specific formats, generally Extensible Markup Language (XML)-based, allowing automatic computer processing.
• (iii) Ontologies: Define semantic annotations and hierarchical relationships between terms. Examples include Gene Ontology (GO) and Systems Biology Ontology (SBO).

Data Management & Workflow Systems:

• Data-Management Systems: Spreadsheets, web-based Electronic Lab Notebooks (ELN), and Laboratory Information Management Systems (LIMS).
• Workflow Construction Tools: KNIME, caGrid23, Taverna24, Bio-STEER25, and Galaxy26 enable construction, execution, and sharing of specialized computational pipelines.

Systems modeling tools utilize systems of interconnected Partial Differential Equations (PDEs) representing spatiotemporal systems, solved numerically via the Finite Element Method (FEM).

PDE Solvers: ANSYS, FreeFEM++, OpenFEM, and MATLAB.

• (i) Sensitivity Analysis: Describes the stability and controllability of a system against various distractions.
  • Tools: SBML-SAT, MATLAB SimBiology, ByoDyn, and SensSB.
• (ii) Bifurcation and Phase-Space Analysis: Performed to analyze system models to discover steady-state and dynamical tendencies.
  • Tools: AUTO, XPPAut, BUNKI, and ManLab.
• (iii) Metabolic Control Analysis (MCA): Performed to understand the relationship between the properties of a metabolic network (at steady state) and component reactions.
  • Tools: MetNetMaker.