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Our Mission

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We exist to:

• Promote global scientific collaboration by creating a shared platform for data, protocols, and results.

• Establish a culture of reliability, reproducibility, and trust in every research initiative.

• Develop and support innovative biotechnological approaches that can be applied across disciplines.

• Provide educational resources and training to researchers, students, and professionals.

• Uphold the highest international scientific standards, ensuring compliance with global regulations.

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Our Core Values

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• Integrity – Every result, method, and publication we share is built on transparency and scientific honesty.

• Collaboration – We believe the future of science depends on networks, not silos.

• Innovation – We harness AI-driven tools, computational research, and advanced analytics to push boundaries.

• Excellence – Through validated SOPs, standardized procedures, and international compliance.

• Education – Supporting continuous learning, proficiency testing, and knowledge transfer.

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What We Offer

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IRCBB integrates a comprehensive portfolio of scientific services and infrastructures, designed to support laboratories, research projects, and collaborations worldwide:

• AI-driven research and computational BAC studies

• Data center (DC) sharing platforms for seamless collaboration

• HCI-accelerated research environments for rapid data analysis

• Proficiency testing schemes to guarantee inter-laboratory reliability

• Detailed SOPs and validated protocols ensuring reproducibility

• Sample & data archives for long-term continuity of projects

• Dedicated instruments and operating procedures tailored to each study

• Expert scientific advice and consultation across disciplines

• In-house testing capabilities providing complete research solutions

• Laboratory project management & customized reporting

• Support for active risk reduction in experimental workflows

• Cutting-edge technologies and methodologies

• Global support for international research projects

• Educational instructions, training programs, and customized reports

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Why Join IRCBB?

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By participating in IRCBB, laboratories and researchers gain access to:

• A trusted international scientific network.

• A standardized framework for quality, reproducibility, and compliance.

• Advanced digital platforms for data sharing, archiving, and collaboration.

• Customized solutions for laboratory projects, reporting, and management.

• A culture of shared expertise, innovation, and integrity.

Our community is open to researchers, laboratories, and institutions that share our vision: advancing science through collaboration, education, and transparency.

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Looking Ahead

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The future of science is global, digital, and collaborative. IRCBB stands at the intersection of these principles, combining cutting-edge biotechnology with cloud-based infrastructure, AI-driven insights, and international regulatory compliance. We invite you to engage with us, share knowledge, and build partnerships that will shape the next era of biotechnology and molecular research.

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​Industrial application map
 

Biopharma: mAbs, recombinant proteins, vaccines, mRNA/LNPs, viral vectors, and cell/gene therapies.
•    Industrial enzymes & specialty proteins: detergents, textiles, food processing, diagnostics, paper & pulp, and textile stone-washing replacements.
•    Bio-based chemicals & polymers: organic acids (lactic, succinic, itaconic), solvents (butanol), monomers (PDO), and materials (PLA, PHA).
•    Food & nutrition: precision-fermented dairy/egg proteins, amino acids, vitamins, heme flavors, fermentation-enabled fats; inputs for cultivated meat.
•    Ag-biotech: microbial inoculants/biostimulants, biopesticides, gene-edited traits, RNA-based crop protection.
•    Environmental & utilities: anaerobic digestion/biogas, wastewater treatment, resource recovery, and bioremediation (case-dependent).
•    Diagnostics: enzymes, antibodies, and nucleic-acid reagents for clinical and industrial testing.

Core technical methods (Build–Test–Learn–Scale)

A. Design & build (biotech)

•    Host selection: bacteria (E. coli, Bacillus), yeasts (Saccharomyces, Komagataella), filamentous fungi, actinomycetes; CHO/HEK for complex biologics; plant/insect cells; consortia or cell-free.
•    Genome/pathway engineering: CRISPR (nuclease, base/prime), recombineering/MAGE, transposon mutagenesis, modular cloning; directed evolution for enzymes and regulatory parts.
•    Metabolic design: genome-scale models, flux balance/kinetic models, ^13C-MFA; cofactor/ATP balancing and transporter engineering.
•    High throughput: microfermenters/droplet systems, FACS, deep mutational scanning; robotics integrated with ELN/LIMS.

B. Upstream processing (USP)

•    Reactor modes: batch, fed-batch, perfusion/continuous; single-use vs stainless.
•    Key levers: oxygen transfer (kLa), mixing and shear, pH/temperature profiles, media/feed strategies (DO-stat, exponential feeds).
•    PAT & control: online pH/DO/CO₂, capacitance, Raman/NIR, off-gas; soft sensors and model-predictive control (MPC).
•    Primary metrics (TRY): Titer (g/L), Rate/space-time yield (g/L·h), Yield (g/g substrate), plus viability or specific productivity (qP).

C. Downstream processing (DSP)

•    Cell removal/clarification: centrifugation, depth filtration/TFF, flocculation.
•    Capture & purification: proteins—Protein A/affinity, IEX, HIC, mixed-mode; chemicals—extraction, distillation, crystallization, membranes, adsorption.
•    Polishing & formulation: virus removal filtration, UF/DF, lyophilization, excipient screening; for chemicals—solvent recovery and salt management.
•    Cost reality: DSP dominates COGS for proteins; feedstock dominates COGS for bulk chemicals.

D. Analytical biochemistry & QC

•    Molecular analytics: qPCR/ddPCR, NGS, copy number/stability assays.
•    Chemical analytics: LC–MS(/MS), GC–MS, HPLC/UPLC (SEC, IEX, RP), CE-SDS, glycan profiling, residuals/impurities (host-cell proteins/DNA, endotoxin).
•    Function/potency: enzyme kinetics (kcat/Km), binding assays (SPR/BLI), bioassays.
•    Quality systems: CQAs/CPPs, DoE-based process characterization, continued process verification (CPV), and (where feasible) real-time release.

Digital, AI, and automation

•    In-silico design: ML for sequence-to-function prediction, structure-guided enzyme design, promoter/RBS tuning, and codon optimization.
•    Self-driving labs: automated DBTL cycles with active learning.
•    Operations: digital twins of reactors, anomaly detection, and MPC; data integrity and electronic batch records (21 CFR Part 11/GxP).

Regulatory and compliance landscape (by sector)

•    Biopharma: GMP (EU/US), ICH Q8–Q12/Q13; viral safety, comparability, serialization; pharmacovigilance.
•    Food/feed: GRAS/novel foods (US/EU), enzyme approvals, allergen/toxicity and labeling.
•    Ag-biotech: EFSA/EPA/BPR; different paths for gene-edited vs transgenic organisms.
•    Environmental/utilities permit for digesters/biogas, biosafety/containment; gene synthesis screening/biosecurity norms.

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Economics and scale-up risk

•    Engineering similarity: match kLa, P/V, shear, and mixing times across scales; use CFD and scale-down models to expose failure modes.
•    Dominant cost drivers:
o    Proteins: resins/consumables, facility overhead, low product concentration penalties.
o    Chemicals: feedstock cost & yield, utilities (steam/cooling), solvent loss.
•    Learning curves & platformization: standard cell lines, vector backbones, and DSP trains cut COGS and accelerate validation.

Risk & mitigation

•    Technical: strain instability, phage/contamination, oxygen transfer limits → genetic stability circuits, closed processing, robust CIP/SIP, phased scale-up.
•    Regulatory: shifting requirements → early agency engagement, QbD dossier strategy, strong data integrity.
•    Market & supply: petro price swings, long qualification cycles, resin/media lead times → offtake agreements, dual-sourcing, inventory strategy.