Innovation Capabilities & Products

Morbidostat is a computer-controlled continuous culture device that automatically adjusts drug concentration to maintain constant growth inhibition in microbial cultures. As bacteria acquire mutations that give them resistance against drugs, they are able to tolerate higher drug concentrations and grow faster, thus removing selective pressure, the driving force of evolution. To compensate for this, morbidostat increases drug concentration sufficiently to keep bacteria at their original growth rate, therefore maintaining selective pressure over time. This system allows for data acquisition to model microbial evolution under antimicrobial stress, optimize biocide dosage strategies and develop highly antimicrobial-resistant strains used to test the performance of new biocides, among other applications.

Design and development of seafood products for target consumers (elderly, young, diabetic, allergic to seafood, etc.) that guarantee specific nutritional requirements, safety and sustainability. Development of techniques (i.e. metabolomics, proteomics, genomics, lipidomics, etc.), using animal models and cell cultures, to characterize the response of a patient/consumer to diet and to design personalized nutrition treatments and products (i.e. nutraceutics, hypoallergenic fish products, immunostimulants, functional foods, etc).

SYNBADm is a Matlab-based toolbox for the automatic design of biological circuits with targeted functions from component libraries.

More information here.

Method described in Csendes, T., L. Pal, J.O.H. Sendin, J.R. Banga (2008) The GLOBAL Optimization Method Revisited. Optimization Letters, 2(4):445-454.

Software available upon request (email us if interested).

Assessment and development of protocols to identify the most problematic areas and products in terms of microbial contamination and parasitic infestation along the value chain of seafood products.

AMIGO2 is a MATLAB-based multiplatform toolbox designed to solve mathematical optimization problems at the core of systems biology, within the context of parametric model identification, the underlying hypothesis for model development and the optimal control of biological systems to synthetically achieve the desired behavior.

More information here.

Method described in: Exler, O., L.T. Antelo, J.A. Egea, A.A. Alonso and J.R. Banga (2008) A Tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design. Computers & Chemical Engineering, 32(8):1877-1891.

Software available upon request (email us if interested).

Development of Deep Learning algorithms that allow automating fisheries monitoring processes and reducing time and costs compared with processing by human observers. The applications developed range from innovative systems for real-time remote electronic monitoring, which identify and quantify total catches of fishing vessels (e.g., iObserver), to new image recognition techniques that allow individually identifying fish and estimating population parameters. 

PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction) is an open-source, multi-platform software tool for inferring network structures from data using information-theoretic measures. While this general purpose tool has been developed with biological networks in mind, it can be applied to other areas.

More information here.

The method is described in Schlater, M., J. A. Egea, J. R. Banga (2009) Extended ant colony optimization for non-convex mixed integer nonlinear programming. Computers & Operations Research 36(7): 2217-2229.

Software available upon request (email us if interested).