11: Systems Biotechnology
- Page ID
- 199187
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Systems biotechnology uses quantitative models and engineering to understand and design biological systems at multiple scales. It blends systems biology, metabolic engineering, and synthetic biology, using data about genes, proteins, and metabolism to predict how networks behave and to guide a design-build-test-learn cycle for making useful products. Key tools include genome-scale metabolic models with flux balance analysis, dynamic modeling, machine learning, and CRISPR-based editing. The field enables production of chemicals, fuels, medicines, improved crops, and environmental solutions,
- 11.1: Computer Generated Models
- This page discusses how advancements in computational technology have transformed biological studies by enabling computer-generated models for simulations in biotechnology. These models aid in fast, cost-effective research for drug discovery and protein structure prediction, allowing virtual clinical trials and systems biology modeling.
- 11.2: Cell Culture
- This page discusses cell culture in biotechnology, highlighting its role in growing cells outside their natural environment for research and applications. It distinguishes between primary cells and cell lines, outlines the necessary conditions for culturing, and lists applications such as drug development and tissue engineering. While cell culture reduces variability and addresses ethical concerns, it cannot fully replicate the complexity of living organisms.
- 11.3: Cloning
- This page discusses cloning in animal biotechnology, emphasizing its role in understanding genetics and cellular processes while raising ethical concerns. It outlines the process of producing genetically identical organisms, distinguishing reproductive cloning as a key focus, exemplified by Dolly the sheep. The significance of cloning techniques for research and agriculture is also highlighted, showcasing their potential impact and the complexities involved.
- 11.4: Bioprinting
- This page discusses bioprinting, a technique that combines 3D printing with living cells and biomaterials to create tissue-like structures. It includes designing shapes from medical images, selecting cells and bioinks, and using various printing methods. Key challenges are vascularization and integrity, with applications in tissue engineering and organ-on-a-chip systems. However, clinical implementation faces obstacles such as biocompatibility, scalability, and regulatory issues.
- 11.5: The Future of Systems Biotechnology
- This page discusses systems biotechnology, which integrates biology with genomics, proteomics, and AI to analyze biological systems. It highlights advancements in medicine, agriculture, and environmental science, emphasizing AI's role in data analysis and microorganism engineering. Synthetic biology aids in creating complex materials, while automation speeds up experimentation.
- 11.6: Careers in Systems Biotechnology
- This page discusses the evolving field of biotechnology and the importance of interdisciplinary skills for careers in systems biotechnology. It highlights entry-level opportunities leading to roles such as Tissue Culture Technicians, Molecular Cloning Technicians, and others, focusing on technical proficiency in areas like cell culture and bioprinting. The page emphasizes the need for laboratory expertise combined with automation and data analysis to succeed in the field.
- 11.7: New Page
- This page discusses the global health threat of antibiotic resistance and highlights MIT researchers' innovative use of systems biotechnology and artificial intelligence to discover new antibiotics more efficiently. Led by Professor James Collins, the team has identified effective candidates against drug-resistant bacteria like MRSA, significantly reducing the time needed for laboratory testing.