Metabolic Modelling
MATLAB, short for Matrix Laboratory, stands out as a robust and flexible programming language and numerical computing environment, particularly valued in the field of biological modeling. Its prowess lies in effectively managing intricate mathematical operations, manipulating extensive datasets, and streamlining the visualization of results. In the realm of biological modeling, MATLAB offers an accessible platform for crafting and simulating complex mathematical models that accurately depict various biological systems and phenomena. Its tailored features make it especially fitting for iGEM projects concentrating on modeling gene circuits, as it aligns seamlessly with the distinct requirements of synthetic biology and genetic engineering.
In our effort to address environmental concerns linked to terephthalic acid (TPA) pollutants, we've developed a gene circuit model using MATLAB. This cutting-edge model draws on synthetic biology principles to create a biological system proficient in breaking down TPA efficiently. Within the gene circuit, genetic components encode enzymes crucial for TPA degradation, transforming it into environmentally friendly byproducts. Through the utilization of MATLAB's computational modeling capabilities, we can simulate and optimize this gene circuit to enhance TPA breakdown efficiency across diverse environmental conditions. This method not only provides a sustainable solution for TPA remediation but also establishes a foundation for exploring and refining gene circuits dedicated to mitigating environmental impacts. The integration of MATLAB facilitates a systematic and adaptable analysis, empowering researchers to predict and optimize the gene circuit's performance before experimental implementation—contributing significantly to the development of eco-friendly technologies for environmental protection.
All MATLAB files for metabolic modelling can be found here in the gitlab repository.
Protein Modelling
Workflow
More information on the results from the protein modelling can be found in the Engineering and Proof of Concepts pages.
AlphaFold2
AlphaFold is a deep learning-based protein folding prediction tool developed by DeepMind. It employs a neural network architecture trained on a vast dataset of known protein structures to predict the 3D structure of a protein based on its amino acid sequence. This was particularly useful for us since PhaF did not have an experimental structure.
Autodock4
AutoDock is a molecular modeling software widely used for simulating the interaction between small ligands (such as drug molecules) and macromolecules (such as proteins or nucleic acids). In the context of our experiment we treated the PHB monomer as the ligand and our protein as the receptor.
GROMACS
GROMACS is a molecular dynamics simulation software utilized for in-depth investigations into the behavior of biomolecules, including proteins, lipids, and nucleic acids, at the atomic scale. In our study, we meticulously prepared our protein structure using CHARMM-GUI and conducted an extensive molecular dynamics simulation. Additionally, we established a comprehensive system comprising a PHB membrane and protein using GROMACS.
Pymol
PyMOL is a versatile molecular visualization software that enables detailed examination of biomolecular structures. We employed PyMOL to visualize and analyze the structures of PhaF and the hydrophobic PHB polymer. This facilitated a comprehensive understanding of their spatial arrangements and interactions, particularly when conducting and autodock analysis.