Industrial Biotech 2

Engineered bacteria diagram: synthetic tools

Logic gates (biological computation)

• Cells are engineered to behave like circuits (AND, OR, NOT gates)

• Example:

  • AND gate → bacteria only respond when two signals are present

  • In tumors: only activate drug release when low oxygen + tumor biomarker

• Mechanism:

  • Promoters + repressors arranged so transcription happens only under specific combinations

CRISPRi tools (gene silencing, not cutting)

• Uses dCas9 (dead Cas9) → binds DNA but does not cut

• Blocks RNA polymerase → turns genes OFF

• Why useful:

  • Precise control of metabolic pathways

  • Reduce toxicity by shutting down harmful genes

Gene circuits

• Designed DNA networks that control:

  • timing (delays, oscillations)

  • thresholds (only activate above certain signal)

• Example:

  • quorum sensing circuit → bacteria activate only when population is high

Engineered microbes

Common chassis organisms

• E. coli

  • Easy to engineer

  • Used for sensing + drug production

• Lactococcus / probiotics

  • Safe for human gut

  • Used in gut therapy (IBD, inflammation)

• Staphylococcus (modified strains)

  • Used cautiously for skin or targeted delivery

Key idea:

You take a natural bacterium and insert synthetic circuits → it becomes a programmable living machine

Biomedical applications (Biodiagnostics & Tumor killing)

Biodiagnostics

• Engineered bacteria detect biomarkers and produce signals:

  • fluorescence

  • color change

  • electrical signal

• Example:

  • detect gut bleeding → bacteria produce visible pigment in stool

Tumor killing

• Tumors have unique environments:

  • low oxygen (hypoxia)

  • abnormal nutrients

• Bacteria are engineered to:

  • accumulate in tumors

  • release:

    • toxins

    • immune activators

• Advantage:

  • high specificity vs chemotherapy

Biomedical applications (Gut inflammation, pathogen control)

Gut inflammation (IBD, Crohn’s)

• Probiotic bacteria engineered to:

  • secrete anti-inflammatory cytokines (e.g., IL-10)

  • sense inflammation markers and respond dynamically

• Acts as a living drug factory inside the gut

Pathogen control

• Bacteria detect harmful microbes and:

  • release antimicrobials

  • outcompete pathogens

• Example:

  • engineered microbiome to prevent infections

Traditional fermentation

What’s actually happening

• Microbes (yeast, bacteria, fungi) metabolize sugars → produce:

  • lactic acid (yogurt, cheese)

  • ethanol + CO₂ (bread, alcohol)

• Mostly anaerobic metabolism

Key insight (not in slide)

• The goal is food transformation, not molecule isolation

• Microbes remain in the final product → they shape:

  • flavor (organic acids, esters)

  • texture (proteolysis, gas formation)

Limitation

• Low control → variability in taste, yield, contamination risk

Biomass fermentation

What’s actually happening

• You grow microbes as the product itself

• Cells are harvested as:

  • protein-rich biomass (single-cell protein)

Example mechanism

• Fast-growing fungi/bacteria:

  • convert cheap carbon (glucose, waste) → cell mass

• No need to extract specific molecules

Key insight

• This is biomass accumulation optimization

  • maximize growth rate (μ)

  • maximize yield (Yx/s)

Why important

• Efficient protein production:

  • much lower land + water than livestock

Limitation

• Product is less customizable

• Texture/flavor need processing

Precision fermentation

What’s actually happening

• Microbes are genetically engineered to produce one specific molecule

• Example:

  • insulin

  • casein (milk protein without cows)

  • heme (plant-based meat flavor)

Mechanism (important)

• Insert gene → microbe expresses enzyme/pathway → produces target compound

• Then:

  • cells are removed

  • product is purified (high purity step)

Key insight

• This is cell as a factory, not food

• You control:

  • metabolic flux

  • pathway efficiency

  • product specificity

Precision fermentation: upstream target selection

Target selection

• Decide what molecule you want

  • protein (e.g., casein)

  • pigment (e.g., beta-carotene)

  • vitamin (e.g., B12)

This determines entire pathway design

• Complex molecules → require multi-enzyme pathways

Precision fermentation: upstream host selection

Host selection & strain development

• Choose organism:

  • bacteria (fast growth)

  • yeast (better for complex proteins)

  • fungi (high secretion)

• Then engineer:

  • insert genes

  • optimize promoters

  • remove competing pathways

• You are optimizing metabolic flux toward your product

• Otherwise cells waste energy on growth instead of production

Precision fermentation: feedstock selection upstream

Feedstock selection

• Carbon source:

  • glucose, molasses, agricultural waste

Why it matters:

• Feedstock cost = major economic factor

• Using waste streams → sustainability advantage

precision fermentation: Fermentation Fermentor design

Fermentor design

• Types:

  • submerged (liquid, most common)

  • solid-state (for fungi)

  • membrane systems (retain cells, improve yield)

Engineering goal:

• Maximize:

  • cell density

  • product formation rate

precision fermentation: Fermentation process optimization

Process optimization

Control key variables:

• pH
  → affects enzyme activity

• Temperature
  → affects growth rate + protein folding

• Oxygen
  → determines metabolism:

  • aerobic → biomass + ATP

  • anaerobic → fermentation products

• Salt / nutrients
  → stress conditions can increase product yield

Key insight:

• Small changes here can double or crash production

precision fermentation: Downstream

Pre-treatment

• Break cells (if product is intracellular)

• Remove debris

Separation

• Centrifugation / filtration

• Separate cells from liquid

Concentration

• Reduce volume

• Increase product concentration

• Methods: evaporation, ultrafiltration

Purification

• Chromatography, precipitation

• Remove impurities → achieve high purity

Important:

• This is often the most expensive step

Product formulation

• Convert into usable form:

  • powder

  • liquid

  • food ingredient

Fermentationhost selection pros: bacteria

• Prokaryotes – easier to manage
  → No nucleus, simple genome → easier gene insertion and control
  → Faster troubleshooting and strain development

• Faster generation time
  → Very short doubling time (~20–30 min)
  → Rapid biomass accumulation → high productivity (g/L/h)

• Many molecular tools available
  → Strong promoters, plasmids, CRISPR tools
  → Enables high-yield expression systems

• Have plasmids
  → Independent DNA vectors → high gene copy number
  → Can overexpress target proteins massively

• Low-cost growth (extra)
  → Grow on simple media (glucose, salts)
  → Industrial scalability is cheaper than eukaryotes

Fermentationhost selection cons: bacteria

• Genetic drift – higher mutation rates
  → Fast division = more replication errors
  → Loss of plasmids or mutations → reduced yield over time
  → Requires selection pressure (e.g., antibiotics)

• Bad public perception
  → Association with pathogens
  → Regulatory + consumer resistance (especially in food)

• Lack of post-translational modifications (extra)
  → Cannot perform proper glycosylation
  → Complex proteins may be inactive or misfolded

• Inclusion body formation (extra)
  → Overexpressed proteins aggregate → require refolding steps

Fermentationhost selection pros: yeast

• Eukaryotes – more robust than bacteria
  → Can tolerate wider:

  • pH

  • temperature

  • osmotic stress
    → Better suited for industrial conditions

• High accumulation of compounds
  → Efficient metabolic pathways
  → Used for:

  • vitamins

  • organic acids

  • bioethanol

• Post-translational modifications (extra)
  → Protein folding + glycosylation
  → Suitable for therapeutic proteins

• Secretion capability (extra)
  → Can export proteins → easier downstream processing

Fermentationhost selection cons: yeast

• Slower generation time
  → Longer fermentation cycles
  → Lower throughput than bacteria

• Higher nutrient requirements
  → More complex media → increased cost

• Non-human glycosylation (extra)
  → Adds incorrect sugar groups
  → Can affect drug safety/function

Fermentation host selection pros: Mold

• Eukaryotes – more robust
  → Similar stress tolerance advantages as yeast

• Powerhouse enzyme producers
  → Naturally secrete enzymes to digest environment
  → Very high yields of:

  • amylases

  • proteases

  • cellulases

• Extracellular enzymatic degradation
  → Break down complex substrates (e.g., plant biomass)
  → Can use cheap agricultural waste

• High secretion capacity (extra)
  → Product released into medium → easier purification
  → degrade agricultural waste → cost advantage

Fermentation host selection cons: mold

• Slower generation time
  → Slower biomass formation

• Higher nutrient demand
  → Requires more complex substrates

• Mycotoxin production risk
  → Some species produce toxic compounds
  → Requires strict strain selection and control

• Negative impact on rheology
  → Filamentous growth → thick, viscous broth
  → Poor mixing and oxygen transfer → reduced efficiency

• Scale-up challenges (extra)
  → Hard to maintain uniform growth in large reactors

Fermentation host selection pros: Microalgae

• Eukaryotes – more robust than bacteria
  → Handle environmental stress better

• Adaptive to existing fermentation infrastructure
  → Can be grown in systems similar to bacteria/fungi

• Heterotrophic growth
  → Use sugars instead of light → higher cell density
  → More efficient industrial production

• Wide range of unique molecules
  → Pigments (astaxanthin)
  → Lipids (omega-3 fatty acids)
  → Antioxidants

• Sustainability potential (extra)
  → Can use CO₂ + sunlight (autotrophic mode)

Fermentation host selection cons: Microalgae

• Target extraction challenging
  → Tough cell walls (sometimes silica-based)
  → Requires mechanical/chemical disruption → high cost

• Autotrophic growth limitations
  → Slow growth
  → Low biomass density
  → Light penetration limits scaling

• Photobioreactor cost (extra)
  → Expensive infrastructure

Feedtosck selection: Conventional feedstock

• Standardized conditions
  → Pure substrates (glucose, sucrose) → consistent composition
  → Predictable microbial growth and product yield

• No food safety issues
  → Clean inputs → low contamination risk
  → Easier regulatory approval

• Costly to use
  → Refined sugars require processing → high input cost
  → Competes with human food supply

• Less sustainable
  → Uses edible crops → land, water, fertilizer demand
  → Contributes to food vs industry competition

• Example: refined sugar

• Ideal for:

  • pharmaceuticals (need consistency)

• Not ideal for:

  • large-scale sustainable food production

Feedtosck selection: alternative feedstock

• Not standardized
  → Variable composition (different sugars, inhibitors)
  → Leads to inconsistent fermentation performance

• Production inconsistency
  → Microbes may grow differently batch-to-batch
  → Requires process optimization

• Regulatory challenges (food safety)
  → Waste streams may contain:

  • toxins

  • heavy metals
    → Needs strict purification and validation

• More sustainable
  → Uses waste instead of food crops
  → Supports circular bioeconomy

• Example: food industry side streams

Types of alternative feedstocks: agricultural

Agricultural crops

• High carbohydrate content (e.g., sugarcane)

• Used for:

  • bioethanol

  • alternative proteins

Insight:

• Still semi-conventional → not fully sustainable if primary crops are used

Types of alternative feedstocks: Lignocellulosic plant material

• Leaves, stems, wood (cellulose + hemicellulose + lignin)

Key mechanism:

• Must be pretreated:

  • lignin removed

  • cellulose → glucose

Used for:

• mycoprotein

• biofuels

Limitation (extra):

• Pretreatment is expensive and energy-intensive

Types of alternative feedstocks: Food industry by-products

• Example: fruit peels, whey, pulp

Advantages:

• Cheap or free

• Already partially processed

Use:

• alternative proteins

• fermentation substrates

Types of alternative feedstocks: Plastic waste

• Converted via:

  • pyrolysis → small hydrocarbons

  • microbes convert → biomass/protein

Insight:

• Still emerging technology

• Not widely commercial yet

Types of alternative feedstocks: Organic food waste

• Mixed waste (household, agriculture)

Use:

• fermentation into:

  • biogas

  • microbial protein

Challenge:

• Highly variable composition

Process optimization: validation of industrial performance step 1

LAB SCALE

What you actually do:

• Compare multiple hosts (e.g., bacteria vs yeast)

• Compare multiple feedstocks (glucose, waste streams, etc.)

• Measure:

  • whether the product is made at all

  • approximate yield

Why this works well:

• Everything is well mixed

• Oxygen and nutrients are uniformly available

• Heat is easily dissipated

Why it is limited:

• These conditions are artificially ideal

• Cells are not exposed to gradients or stress

• A strain that looks “high-performing” here may fail later

Process optimization: validation of industrial performance step 2

LAB SCALE BIOREACTOR

• You introduce active control systems:

  • pH control (acid/base addition)

  • dissolved oxygen control (aeration + agitation)

  • temperature regulation

• You can monitor variables in real time

What you optimize:

• Growth vs production tradeoff
  (cells often prioritize growth over product unless tuned)

• Oxygen supply vs demand

• Feeding strategy (batch vs fed-batch)

Why this step matters:

• You begin to see process behavior, not just biology

• The same organism can behave differently depending on:

  • oxygen availability

  • nutrient limitation

  • stress conditions

Process optimization: validation of industrial performance step

PILOT UNIT

Volume increases significantly (tens → hundreds → thousands of liters).

What fundamentally changes:

• Mixing is no longer uniform
  → cells experience microenvironments
  (some regions high oxygen, some low)

• Oxygen transfer becomes limiting
  → oxygen must diffuse through liquid
  → not all cells receive the same amount

• Heat removal becomes difficult
  → metabolic activity generates heat
  → large volumes retain heat longer

• Shear forces increase
  → agitation needed for mixing can damage cells

Why processes fail here:

• A strain optimized for uniform lab conditions may:

  • stop producing product

  • shift metabolism

  • die in low-oxygen zones

What you do at this stage:

• Adjust:

  • agitation speed

  • aeration rate

  • reactor geometry

• Validate whether the process is stable over time

Process optimization: validation of industrial performance step 4

INDUSTRIAL BIOREACTOR

The focus shifts from “does it work?” to:

• Can it run consistently for long periods?

• Is it economically viable?

Key constraints:

• Even small inefficiencies become expensive

• Variability between batches must be minimized

• Contamination risk becomes critical

Important requirements:

• Strain stability
  → no genetic drift over long runs

• Consistent feedstock quality
  → variability affects product output

• Reproducible process control
  → identical conditions across batches

• Efficient downstream processing
  → purification cost often dominates total cost

At this scale, optimization is continuous:

• small changes in oxygen transfer, feeding, or mixing can significantly affect:

  • yield

  • product quality

  • cost per unit

Precision fermentation: Dairy

• Purpose: Produce animal-free milk proteins identical to cow-derived ones

• Status: Most advanced and already scaled commercially

• What’s produced: Casein and whey proteins with the same amino acid sequence → same functionality (foam, emulsification, texture)

• Key advantage: Can replicate real dairy properties because functionality depends mainly on protein structure

• Main challenge:

  • High purification cost (proteins must be isolated from fermentation broth)

  • Production cost still higher than conventional dairy

Precision fermentation: eggs

• Purpose: Replicate functional properties of eggs (not just nutrition)

• Status: Commercial products exist; strong growth since ~2021

• What’s produced: Egg proteins like ovalbumin → responsible for foaming, binding, emulsifying

• Key advantage: Enables plant-based foods to behave like real eggs (e.g., baking, texture)

• Main challenges:

  • Host development → proteins must fold correctly to work

  • Scale-up → maintaining functionality at industrial volumes

  • Harder than dairy because eggs rely on multiple interacting properties, not just one protein

Precision fermentation: Fats / Oils

• Purpose: Replace animal fats in plant-based foods to improve taste and mouthfeel

• Status: Emerging; many companies still scaling

• What’s produced: Tailored lipids (specific fatty acid profiles)

• Key advantage: Can design fats with:

  • better nutrition (e.g., omega-3)

  • specific melting behavior → improves texture

• Main challenges:

  • Controlling fatty acid composition precisely

  • Small changes in lipid profile → major changes in taste and texture

  • Scale-up still difficult for high yields

Precision fermentation: Meat / Seafood

• Purpose: Produce components that mimic meat/fish

• Status: Developing; not fully mature

• What’s produced: Key molecules like:

  • heme proteins (flavor, color)

  • aroma compounds

• Key advantage: Improves realism of plant-based meat alternatives

• Main challenges:

  • Replicating:

    • texture (muscle structure)

    • juiciness

    • full flavor profile

  • Precision fermentation produces ingredients, not whole tissue

  • Competes with biomass fermentation approaches

Precision fermentation: Colours / Vitamins / Flavours

• Purpose: Replace synthetic additives with natural, microbially produced ones

• Status: Strong and growing application

• What’s produced:

  • pigments (carotenoids)

  • vitamins (e.g., B12)

  • flavor compounds

• Key advantage:

  • Cleaner label (natural origin)

  • Avoids chemical synthesis

• Main challenges:

  • Stability issues:

    • sensitive to heat, pH, light

  • Maintaining consistency during processing and storage

  • Scale-up for bulk production

Precision fermentation: Other (e.g., infant formula)

• Purpose: Produce specialized high-value compounds

• Status: Emerging but commercially promising

• What’s produced:

  • human milk oligosaccharides (HMOs)

  • specialty nutrients

• Key advantage:

  • High-value products justify higher production cost

  • Enables formulations closer to human biology

• Main challenges:

  • Product stability

  • Scale-up and consistent manufacturing

Genomics & Proteomics in Bioprocess Development

• Use genomics (DNA-level) and proteomics (protein-level) data to understand how cells behave during production

• Enables global gene expression profiling → which genes are ON/OFF under different conditions

• Applied to recombinant protein production to improve yield and stability

• Limitation: many industrial cell lines are not fully sequenced → incomplete data

• Trend: better sequencing + analytical tools → more precise control of bioprocesses

Genomics & Proteomics in Bioprocess Development: Target Molecule Discovery

• Identify what protein or molecule to produce

• Genomics helps:

  • find genes encoding useful proteins

• Proteomics helps:

  • understand protein function and interactions

• Key idea: choosing the right target early determines:

  • feasibility

  • downstream complexity

Genomics & Proteomics in Bioprocess Development: Cell Culture & Fermentation (Upstream Process)

• Core components:

  • Media → nutrients affecting growth and expression

  • Cell line → determines production capability

  • Culture conditions → pH, oxygen, temperature

• Role of genomics/proteomics:

  • Identify metabolic bottlenecks

  • Optimize gene expression levels

  • Detect stress responses that reduce yield

• Practical impact:

  • Improve productivity

  • Reduce unwanted byproducts

Protein Purification & Analytical (Downstream Process): Protein Purification & Analytical (Downstream Process)

• Includes:

  • Purification steps (filtration, chromatography)

  • Protein characterization (structure, function)

  • Concentration & purity analysis

• Role of proteomics:

  • Detect impurities or degraded proteins

  • Confirm correct folding and modifications

  • Ensure product quality and consistency

• Key constraint:

  • Downstream processing is often the most expensive stage

Genomics & Proteomics in Bioprocess Development:

Scale-Up, Characterization & Validation

• Transition from lab → industrial production

• Requires:

  • consistent product quality

  • reproducible performance

• Genomics/proteomics role:

  • Monitor how gene expression changes at larger scale

  • Identify why yield drops during scale-up

  • Ensure stability of engineered strains

• Critical issue:

  • Cells behave differently under:

    • oxygen limitation

    • nutrient gradients

    • stress conditions

Upstream vs Downstream

Upstream

• Media

• Cell line

• Culture process

→ Focus: maximize production

Downstream

• Purification

• Characterization

• Concentration

→ Focus: ensure purity and functionality

Early Stage of genomic and proteomic tools in process development: Data Integration and System Design step 1

• Genomics and proteomics
  → Identify which genes and proteins drive production
  → Detect inefficiencies:

  • low expression pathways

  • stress-induced protein changes

  • unwanted byproducts

• Stable genotype and enhanced phenotype
  → Ensure the engineered organism:

  • maintains genetic integrity over time

  • consistently produces high yields
    → Prevents loss of productivity during scale-up

Early Stage of genomic and proteomic tools in process development: Data Integration and System Design step 2

• Product characterization
  → Analyze:

  • structure and functionality

  • impurities and degradation
    → Determines if the product meets quality requirements

• Predictive bioprocess design
  → Use collected data to design:

  • optimal strain

  • optimal growth conditions

  • compatible downstream strategy
    → Reduces reliance on trial-and-error

Stage of genomic and proteomic tools in process development: Feedback loop

• Process is cyclical, not linear

• Data from each stage feeds back into:

  • strain redesign

  • process adjustment

• Outcome:
  → progressively improved performance and stability

genomic and proteomic tools in process development: late stage

• Optimized expression systems
  → Engineered host produces target efficiently with minimal waste

• Optimized process
  → Conditions refined for:

  • maximum yield

  • reproducibility at industrial scale

• Enhanced selective recovery
  → Downstream tailored to:

  • isolate product efficiently

  • improve purity and reduce costgenomic and proteomic tools in process development

Systems strategies for developing industrial microbial strains: Strain Development (Upstream Process)

• Microbial host selection
  → Choose organism based on:

  • growth rate

  • tolerance to product

  • ability to express pathway

• Biosynthetic pathway construction
  → Introduce or assemble pathways for non-native products
  → Requires inserting multiple genes and coordinating their expression

• Improvement of self-tolerance
  → Many products are toxic to the cell
  → Cells are engineered to:

  • resist product toxicity

  • maintain growth at high concentrations

• Removal of negative regulation
  → Eliminate feedback inhibition or repressor systems
  → Prevents shutdown of production pathways

• Flux rerouting (cofactors & precursors)
  → Redirect metabolic intermediates toward target product
  → Balance cofactors (e.g., NADH/NADPH) for efficient synthesis

• Optimization of metabolic fluxes
  → Maximize carbon flow into product pathway
  → Minimize diversion into:

  • biomass

  • byproducts

• High-throughput systems tools
  → Use omics + screening to rapidly test many variants
  → Enables iterative improvement

Systems strategies for developing industrial microbial strains: midstream process

Fermentation (Midstream Process)

• Carbon source selection
  → Prefer:

  • cheap

  • abundant

  • chemically defined substrates

• Culture condition optimization
  → Control:

  • pH

  • temperature

  • oxygen
    → Directly impacts enzyme activity and pathway efficiency

• Feeding strategies
  → Batch vs fed-batch:

  • fed-batch prevents substrate inhibition

  • maintains optimal growth phase

• Performance evaluation
  → Measure:

  • yield

  • productivity

  • byproduct formation

• Scale-up of bioreactors
  → Transition from lab to industrial volume
  → Must maintain:

  • oxygen transfer

  • mixing

  • temperature control

Systems strategies for developing industrial microbial strains: Separation & Purification (Downstream Process)

Separation & Purification (Downstream Process)

• Use of defined conditions
  → Simplifies purification by reducing variability

• Minimization of byproducts
  → Easier downstream processing
  → Reduces purification cost

• Optimization of recovery conditions
  → Example: low pH to improve separation
  → Tailored to product chemistry

Systems strategies for developing industrial microbial strains: Iterative Design Loop

• Arrows indicate continuous feedback between:

  • strain design

  • fermentation performance

  • downstream efficiency

• Changes in one stage affect all others:
  → e.g., improving flux may increase byproducts → harder purification

Systems strategies for developing industrial microbial strains: core challenges

Core challenge: biological complexity

• Cell behavior depends on interacting networks:

  • metabolism (carbon flow)

  • gene regulation (expression control)

  • signaling (response to environment)

• Modifying one part affects others:
  → unpredictable outcomes
  → difficult to design optimal strains directly

Practical constraints

• Time-intensive
  → multiple design–test cycles

• Cost-intensive
  → requires advanced tools, screening, validation

• Labor-intensive
  → experimental optimization at multiple levels

Systems strategies for developing industrial microbial strains: Demonstrated industrial outputs

• Amino acids

  • L-valine, L-threonine, L-lysine, L-arginine

• Bulk chemicals

  • 1,4-butanediol

  • 1,3-propanediol

  • butanol, isobutanol

  • succinic acid

• Pharmaceuticals

  • artemisinin

Microbial strain improvement: Mutagenic library → diversity generation under biological complexity

• Industrial strains are improved by creating large mutant libraries using fast methods (ARTP, HIB, etc.) because cellular behavior is governed by interconnected metabolic, regulatory, and signaling networks that are hard to model directly.

• Random mutagenesis introduces:

  • enzyme variants with higher activity

  • altered regulation (e.g., loss of feedback inhibition)

  • improved tolerance to toxic products

• In practice, this often targets traits like:

  • higher flux through a pathway

  • resistance to product inhibition (e.g., ethanol, organic acids)

  • faster substrate uptake

Microbial strain improvement: High-throughput screening → filtering functional phenotypes

• The text emphasizes automatic, high-throughput selection, while the diagram shows tools like FACS/FADS—these are necessary because only a tiny fraction of mutants improve performance.

• Screening is designed to link phenotype to measurable signal, for example:

  • fluorescence linked to product concentration

  • growth under stress (proxy for tolerance)

• Critical constraint:
  → a strain that looks good in screening must also maintain:

  • metabolic balance

  • energy efficiency (ATP usage)
    otherwise it fails later during fermentation

Microbial strain improvement: Microscale cultivation + online monitoring → translating phenotype to process

• Selected mutants are moved into microscale cultivation systems where real process variables are introduced:

  • oxygen limitation

  • pH shifts

  • nutrient gradients

• Online monitoring (e.g., dissolved oxygen, NADH signals) allows detection of:

  • metabolic bottlenecks

  • overflow metabolism (e.g., acetate formation in bacteria)

• This step ensures that selected strains are not just high producers, but:
  → robust under dynamic fermentation conditions

Microbial strain improvement: Parameter extraction → building scalable bioprocess conditions

• The text highlights “scalable and reliable bioprocessing parameters”, which come from microscale data:

  • optimal feeding rate

  • oxygen demand

  • growth phase for production

• These parameters directly influence:

  • yield (product per substrate)

  • productivity (rate of production)

  • titer (final concentration)

• Without this step, scale-up often fails due to:

  • oxygen transfer limitations

  • accumulation of toxic intermediates

Microbial strain improvement: Scale-up fermentation → validating strain–process compatibility

• The diagram’s bioreactor stage represents testing whether:

  • the strain maintains productivity at larger volume

  • metabolic behavior remains stable over time

• At scale, additional stresses appear:

  • uneven mixing → nutrient gradients

  • heat accumulation

  • shear forces

• Many mutants fail here because:
  → improvements at cellular level are not compatible with physical constraints of bioreactors

Microbial strain improvement: Iterative optimization loop → compensating for unpredictability

• The circular flow in the diagram reflects repeated cycles of:

  • mutagenesis → screening → cultivation → fermentation

• This iterative approach is necessary because:

  • modifying one pathway often disrupts others

  • improvements in yield may increase byproducts or reduce growth

• Over multiple cycles, strains are tuned for:

  • metabolic efficiency

  • stress tolerance

  • process compatibility

Microbial strain improvement: Mutagenic library creation

• Text: “mutagenic library can be created by fast, safe, efficient mutagenesis technologies”

• Meaning:

  • Instead of designing one strain, you generate thousands–millions of variants

  • Methods (ARTP, HIB, etc.) introduce random mutations across genome

• Why:
  → increases probability of discovering improved traits
  → bypasses need to fully understand complex biology

Harnessing microbes for space food engineering: Synthetic chromosomes enable new metabolic functions

• Multiple pathways are combined into synthetic chromosomes to reprogram yeast at a systems level

• Enables traits not naturally present:

  • C1 assimilation → using CO₂ or single-carbon substrates instead of sugars

  • Biofortification → direct production of vitamins (A, C) within biomass

  • Sensory traits → engineered pigment and flavor pathways

• This shifts production from:
  → sugar-dependent fermentation
  to
  → resource-efficient metabolism suitable for closed environments (space)

Harnessing microbes for space food engineering: Metabolic reprogramming under cellular constraints

• Introducing multiple pathways requires:

  • redistribution of carbon flux

  • balancing cofactors (ATP, NADPH)

• Without control:
  → growth slows
  → byproducts accumulate

• Effective designs:

  • decouple growth and production phases

  • prioritize flux toward target molecules while maintaining viability

Harnessing microbes for space food engineering: Intelligent bioreactors regulate expression dynamically

• Engineered pathways are controlled in real time using:

  • sensors (oxygen, metabolites, growth signals)

  • automated feedback systems

• Allows:

  • switching pathways ON/OFF depending on growth stage

  • maintaining optimal metabolic state

• Prevents:

  • energy waste

  • toxic intermediate buildup

Harnessing microbes for space food engineering: Biomass tuned into functional food material

• Yeast biomass is engineered to deliver:

  • nutritional value (vitamins, amino acids)

  • sensory properties (flavor, color)

  • functional traits (texture, binding)

• Composition is controlled through:

  • metabolic pathway design

  • fermentation conditions

• This makes biomass itself the final food ingredient, not just a production system

Harnessing microbes for space food engineering: Microbial 3D food structuring

• Biomass is processed into structured foods using 3D printing

• Enables:

  • controlled texture (e.g., meat-like structure)

  • personalized nutrition profiles

  • minimal waste production

• Solves the gap between:
  → biochemical production
  → consumer-ready food form

Design–build–test–learn (DBTL) cycle: Design

• Define the biological objective (e.g., produce a protein, metabolite)

• Select:

  • metabolic pathway

  • genes/enzymes

  • regulatory elements (promoters, ribosome binding sites)

• In practice:

  • use computational models (flux balance, pathway prediction)

  • predict bottlenecks before experimentation

• Constraint:
  → incomplete knowledge of cellular networks → designs are approximations

Design–build–test–learn (DBTL) cycle: Build

• Physically construct the system:

  • DNA synthesis

  • assembly of genetic parts

  • insertion into host organism

• Modern methods:

  • modular cloning (Golden Gate, Gibson assembly)

  • genome editing (CRISPR)

• Key issue:
  → introduced pathways compete with native metabolism for:

  • energy (ATP)

  • cofactors (NADH/NADPH)

Design–build–test–learn (DBTL) cycle: Test

• Evaluate engineered organism:

  • product yield

  • growth rate

  • byproduct formation

• Data collected using:

  • metabolomics

  • proteomics

  • fermentation measurements

• Important reality:
  → many designs fail due to:

  • metabolic imbalance

  • toxicity

  • regulatory interference

Design–build–test–learn (DBTL) cycle: Learn

• Analyze test data to identify:

  • bottlenecks in pathways

  • inefficient enzyme steps

  • regulatory constraints

• Update:

  • gene expression levels

  • pathway structure

  • host modifications

• Increasingly uses:

  • machine learning

  • large datasets from previous cycles

Iterative optimization

• Cycle repeats:
  → each round improves system performance

• Over time:

  • designs become more predictive

  • fewer experimental iterations needed

Synthetic biology engineering design pipeline: Specification

Specification → defining system inputs and outputs

• Start by defining:

  • inputs (substrates, signals)

  • outputs (desired product, behavior)

  • system performance targets (yield, rate, stability)

• This anchors design to industrial requirements (fuels, chemicals, food, medicines) rather than just biological curiosity

Synthetic biology engineering design pipeline: Design → assembling biological parts into systems

• Build a system blueprint using:

  • existing biological parts

  • de novo designed components

• Parts include:

  • promoters, riboswitches → control expression

  • sensors → detect environmental signals

  • enzymes → drive metabolic reactions

  • transporters → move substrates/products

• These are combined into devices (functional units), then into full systems

• Key principle:
  → modularity allows reuse and recombination of parts

Synthetic biology engineering design pipeline: Modeling → in silico validation

• Before building, designs are tested computationally:

  • metabolic flux modeling

  • pathway simulation

• Purpose:

  • predict bottlenecks

  • estimate yields

  • reduce experimental iterations

• Limitation:
  → models approximate reality; cellular networks remain partially unknown

Synthetic biology engineering design pipeline: Implementation → physical system construction

• Assemble DNA and introduce into host:

  • plasmid construction

  • genome engineering

• Includes advanced strategies:

  • minimal genomes → remove unnecessary genes

  • synthetic genomes → fully redesigned organisms

  • synthetic consortia → multiple microbes working together

• Constraint:
  → engineered systems must coexist with native cellular processes

Synthetic biology engineering design pipeline: Test / Validation → system characterization

• Evaluate:

  • product output

  • system performance

  • stability

• Includes:

  • protein expression analysis

  • metabolic profiling

  • process-level testing

• Determines whether system meets specification targets

Synthetic biology engineering design pipeline: Parts (basic biological components)

• Expression regulation

  • Promoters, transcription factors, riboswitches, UTRs
    → control how much protein is made

• Degradation

  • RNA/protein degradation tags
    → control how long molecules last → prevents buildup or toxicity

• Sensors

  • Receptors, membrane proteins
    → detect:

    • nutrients

    • stress signals

    • environmental changes

• Post-translation control

  • Kinases, phosphatases
    → modify protein activity after synthesis (on/off switching)

• Transporters

  • Channels, pumps
    → move substrates/products across membranes
    → often a bottleneck in production systems

• Metabolic enzymes

  • Catalyze reactions
    → determine pathway efficiency and yield

• Adhesion / targeting

  • Anchor proteins
    → localize proteins or attach cells to surfaces

• Mechanical / structural

  • Scaffold molecules
    → organize multi-enzyme complexes for efficient reactions

Synthetic biology engineering design pipeline: Devices (functional modules built from parts)

• Gene expression devices
  → integrate regulatory parts to control:

  • timing

  • intensity of expression
    → act like biological “switches” or “circuits”

• Post-translational devices
  → regulate activity of proteins dynamically
  → faster control than gene-level regulation

• Cell signaling devices
  → enable communication between cells using:

  • small molecules

  • peptides
    → useful in multi-cell systems or population control

• Metabolic/material devices
  → coordinate multiple enzymes to convert substrates into products
  → core of bioproduction pathways

Synthetic biology engineering design pipeline: Systems (fully integrated biological networks)

• Combine multiple devices into a coordinated system that can:

  • sense environment

  • process information

  • produce outputs

• Advanced system-level designs include:

  • Genome engineering
    → minimal genomes, synthetic genomes for efficiency

  • Artificial minimal cells
    → stripped-down systems with only essential functions

  • Synthetic microbial consortia
    → multiple organisms sharing tasks (division of labor)

Advances in synthetic biology techniques and industrial applications: C. glutamicum as a chassis cell

• Used as a model industrial microbe because it naturally produces amino acids (e.g., glutamate, lysine) at high levels

• Advantages shown at the bottom:

  • Non-toxicity → safe for food and industrial use

  • High purity → fewer unwanted byproducts

  • Simple purification → products often secreted, reducing downstream cost

  • Secretory protein stabilization → maintains protein integrity outside the cell

• Integrated implication:
  → reduces one of the biggest industrial bottlenecks: downstream processing cost

C. glutamicum as a chassis cell: Gene editing tools

• Tools (left side) represent:

  • CRISPR/Cas systems

  • recombineering

  • homologous recombination

• Enable:

  • insertion of biosynthetic pathways

  • deletion of competing pathways

  • fine-tuning of gene expression

• In practice:
  → allows targeted metabolic rewiring instead of random mutagenesis

C. glutamicum as a chassis cell: Engineering strategies

• Strategies (right side) include:

  • pathway assembly

  • promoter engineering

  • gene copy number control

• Also involve:

  • balancing metabolic flux

  • optimizing cofactor usage (NADH/NADPH)

  • reducing accumulation of toxic intermediates

• Key constraint:
  → increasing production often creates:

  • metabolic burden

  • reduced growth rate

C. glutamicum as a chassis cell: Tools + strategies → engineered microbial system

• Central cell represents integration of:

  • genetic tools

  • metabolic strategies

• Outcome:
  → a strain optimized for:

  • high yield

  • stability

  • industrial robustness

C. glutamicum as a chassis cell: Industrial applications

• Amino acids (core strength of C. glutamicum)

  • L-lysine (animal feed), L-glutamate (MSG), L-threonine, L-valine

  • Titers often >100 g/L due to strong carbon flux to central metabolism

• Detergent enzymes

  • Proteases, lipases, amylases stable at high pH/temperature

• Flavor compounds

  • Vanillin (from ferulic acid), nootkatone, fruity esters

• Drug precursors

  • Artemisinin precursor (via yeast primarily), statin intermediates, shikimate pathway products

• Pigments & antioxidants

  • Carotenoids (β-carotene, astaxanthin via engineered hosts), coenzyme Q10 intermediates

Vitamin B₁₂ fermentation pathway engineering: Microbial hosts

• Industrial synthesis relies on:

  • Pseudomonas denitrificans

  • Propionibacterium freudenreichii

• Reason:
  → B₁₂ has a highly complex corrin ring structure that chemical synthesis cannot produce economically

• Trade-off:

  • Pseudomonas → faster growth, higher productivity

  • Propionibacterium → food-grade but slower

Vitamin B₁₂ fermentation pathway engineering: Central carbon metabolism drives precursor supply

• Glucose enters:

  • EMP (glycolysis) → pyruvate

  • TCA cycle → α-ketoglutarate

• Upregulated enzymes:

  • phosphofructokinase

  • pyruvate kinase

• Purpose:
  → push carbon flux toward biosynthetic intermediates, not just energy

Vitamin B₁₂ fermentation pathway engineering: Link between TCA cycle and B₁₂ synthesis

• α-ketoglutarate → glutamate (via glutamate dehydrogenase)

• Glutamate feeds into:
  → δ-aminolevulinic acid (ALA)

• ALA is a key precursor for:
  → tetrapyrrole pathway → ultimately vitamin B₁₂

• Insight:
  → B₁₂ production depends heavily on amino acid metabolism, not just central carbon

Vitamin B₁₂ fermentation pathway engineering: Tetrapyrrole pathway and corrin ring formation

• δ-aminolevulinic acid → multi-step pathway → corrin ring

• Requires:

  • Co²⁺ (cobalt insertion)

  • methylation steps (CH₃ groups)

  • DMBI (lower ligand base)

• Bottleneck:
  → >30 enzymatic steps → very high metabolic burden

Vitamin B₁₂ fermentation pathway engineering: Cofactor and metal dependence

• Cobalt (Co²⁺) is essential:
  → directly incorporated into B₁₂ structure

• Limitation:

  • excess cobalt → toxicity

  • low cobalt → reduced yield

• Industrial control:
  → tightly regulated metal feeding during fermentation

Vitamin B₁₂ fermentation pathway engineering: Process-level intervention (rotenone example)

• Addition of compounds like rotenone:
  → inhibits competing respiratory pathways

• Effect:

  • redirects energy and reducing power toward B₁₂ synthesis

  • increases precursor availability

Vitamin B₁₂ downstream processing and purification diagram

Fermented broth → cell recovery

• Start with fermentation broth containing cells + B₁₂ (mostly intracellular)

• Centrifugation separates biomass from liquid

Extraction (warmth or alcohol)

• Cells are treated with:

  • heat

  • alcohol (e.g., ethanol)
    → cell lysis + release of B₁₂ into solution

• denatures unwanted proteins

• reduces viscosity → easier downstream handling

Adsorption-based purification + filtration

• Crude extract contains pigments, proteins, other cofactors

• Use adsorption resins:
  → selectively bind B₁₂

• Followed by filtration:
  → improves clarity (“transparency”)

Conversion to cyano form (KCN treatment)

• ~0.1% KCN added to convert B₁₂ into:
  → cyanocobalamin (stable form)

• Why:

  • native forms (adenosyl-, methyl-B₁₂) are unstable

  • cyanocobalamin is:

    • more stable

    • easier to purify

    • preferred for supplements

Liquid chromatography → fine purification

• Removes remaining impurities:

  • analogs of B₁₂

  • incomplete intermediates

Crystal formation

• Final purified B₁₂ is:
  → crystallized

• Benefits:

  • high stability

  • easy storage and transport

  • precise dosing

Xanthan gum production: Feedstock utilization

• Uses moist olive pomace and other agro-wastes (e.g., waste bread + enzymes) as carbon sources

• These substrates contain:

  • sugars

  • phenolic compounds

• Integrated effect:
  → reduces raw material cost and improves sustainability
  → phenolics can also influence polymer structure and antioxidant properties

• Constraint:
  → variability in waste composition → inconsistent fermentation performance

Xanthum production species

Core production organism

• Xanthomonas campestris (ATCC 33913)
  → primary industrial strain used for xanthan gum

• Reason:

  • naturally secretes xanthan extracellularly

  • high yield and stable production

  • well-characterized gum structure

Other Xanthomonas species shown

• Xanthomonas axonopodis pv. vesicatoria

• Xanthomonas hortorum pv. pelargonii

• Xanthomonas axonopodis pv. begoniae

• These are typically:
  → plant-pathogenic variants used in research or strain screening

• Relevance:

  • alternative strains may produce xanthan with:

    • different viscosity

    • different branching patterns

  • useful for tailoring functional properties

Xanthan gum production: Microbial production system

• Produced by Xanthomonas campestris (ATCC 33913)

• Other related strains also used depending on application

• Typical conditions:

  • ~28°C

  • agitation (~250 rpm)

  • ~72–76 hours fermentation

• During fermentation:
  → bacteria secrete xanthan gum extracellularly into the medium

• Advantage:
  → easier recovery compared to intracellular products

Xanthan gum production: Polymer structure and functional properties

• Xanthan is a heteropolysaccharide with:

  • glucose backbone

  • side chains containing mannose + glucuronic acid

• Phenolic interactions (shown in slide):
  → can enhance:

  • antioxidant activity

  • functional properties

• Key property:
  → extremely high viscosity even at low concentrations

Xanthan gum production: Yield and viscosity optimization

• Fermentation parameters strongly affect:

  • gum yield

  • viscosity (functional quality)

• Variables include:

  • carbon source concentration

  • inoculum size

  • oxygen transfer

  • agitation

• Trade-off:
  → conditions maximizing yield may not maximize viscosity

Xanthan gum production: Measured performance improvements

• Slide indicates:

  • viscosity increase (~395%)

  • antioxidant activity increase (~179%)

  • xanthan yield increase (~50%)

• Interpretation:
  → substrate choice and process tuning directly impact both quantity and quality

Xanthan gum production: Downstream recovery

• After fermentation:

  • broth contains dissolved xanthan

• Typical industrial recovery:

  • alcohol precipitation (ethanol/isopropanol)

  • drying to powder

• Because xanthan is extracellular:
  → avoids costly cell disruption steps

Industrial applications of xanthan gum

• Food industry

  • thickener (sauces, dressings)

  • stabilizer (prevents phase separation)

  • gluten replacement in baking

• Oil industry

  • drilling fluid viscosity control

• Pharmaceuticals

  • controlled drug release matrices

Xanthan gum production: process constraints

• High viscosity during fermentation:
  → reduces oxygen transfer
  → creates mixing challenges

• Requires:

  • strong agitation

  • optimized aeration

Genetic engineering to modify antibiotics

• Existing antibiotics are chemically altered via engineered biosynthetic pathways

• Done by modifying:

  • polyketide synthases (PKS)

  • non-ribosomal peptide synthetases (NRPS)

• Outcome:
  → new analogs with:

  • improved activity

  • reduced resistance susceptibility

• Example:

  • modifying erythromycin → azithromycin-like derivatives

  • altering glycosylation patterns in antibiotics

• Constraint:
  → small structural changes can drastically affect:

  • binding affinity

  • toxicity