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Metabolic Flux Optimization

At CD ComputaBio, we harness the power of cutting-edge AI technology to revolutionize metabolic engineering through AI-aided metabolic flux optimization. Our expert team, comprising computational biologists, bioinformaticians, and computer scientists, is dedicated to providing innovative solutions to meet the demands of the biotechnology and pharmaceutical industries. We understand the complexities of metabolic pathways and are committed to optimizing metabolic flux with unparalleled precision and efficiency.

Our Services

Fig 1: Metabolic flux optimization

Metabolic Modeling and Simulation

Our experts employ sophisticated metabolic modeling techniques to depict the intricate web of biochemical reactions within a cellular system.

Fig 4: Metabolic flux optimization

High-Throughput Data Analysis

We utilize AI-driven tools to analyze high-throughput omics data, including genomics, transcriptomics, proteomics, and metabolomics.

Fig 3: Metabolic flux optimization

Machine Learning-Based Optimization

Our AI-driven strategies enable the identification of optimal genetic and environmental perturbations to maximize the production of target compounds.

Fig 2: Metabolic flux optimization

Pathway Design and Engineering

Our services extend to the design and engineering of novel metabolic pathways tailored to specific bioproduction goals.

Our Analysis Methods

  • Flux Balance Analysis (FBA)
    We employ FBA to analyze and optimize metabolic flux distributions within a cellular network. By formulating stoichiometric constraints and defining cellular objectives, FBA simulates cellular metabolism, enabling the prediction of optimal flux distributions under various conditions. Through AI-enhanced FBA, we harness predictive modeling to guide the rational engineering of cellular metabolism.
  • Constraint-Based Modeling
    Our approach integrates constraint-based modeling techniques to systematically explore the metabolic capabilities of an organism. By accounting for nutrient availability, cellular energetics, and regulatory constraints, we identify metabolic engineering strategies that align with biological and physiological principles. AI-assisted constraint-based modeling facilitates the design of targeted interventions for metabolic flux optimization.
  • Statistical Learning and Predictive Modeling
    We leverage advanced statistical learning techniques and predictive modeling to elucidate complex relationships within metabolic systems. By training AI algorithms on diverse biological datasets, we uncover patterns and correlations that guide the identification of key regulatory targets and metabolic interventions.

Service Highlights

Unparalleled Precision

Our AI-aided metabolic flux optimization services deliver unparalleled precision in modeling, simulation, and predictive optimization. By harnessing the analytical power of AI, we unravel the intricacies of metabolic networks with unprecedented accuracy, enabling precise interventions for metabolic flux engineering.

Accelerated Bioprocess Development

Through AI-driven metabolic engineering, we expedite bioprocess development and optimization. By rapidly identifying optimal genetic and environmental perturbations, we reduce the timeline for strain improvement and bioproduction optimization, leading to accelerated time-to-market for valuable bio-based products.

Customized Solutions

We understand that each bioproduction goal is unique. Our team works closely with clients to develop customized solutions tailored to specific targets, whether it involves maximizing the production of a specific compound or optimizing the overall metabolic network for enhanced productivity and sustainability.

With the integration of artificial intelligence (AI) and machine learning (ML) algorithms, our AI-aided metabolic flux optimization services offer a transformative approach to address these challenges. By leveraging vast datasets, predictive modeling, and advanced algorithms, we empower our clients to achieve superior results in metabolic engineering and bioprocess optimization. If you are interested in our services or have any questions, please feel free to contact us.

References:

  • Eckdahl T T, Campbell A M, Heyer L J, et al. Programmed evolution for optimization of orthogonal metabolic output in bacteria[J]. PLoS One, 2015, 10(2): e0118322.
  • Hajkarim M C, Karjalainen E, Osipovitch M, et al. Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models[J]. Elife, 2022, 11.
  • Merzky A, Turilli M, Jha S. Raptor: Ravenous throughput computing[C]//2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2022: 595-604.

Services

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