Imaging Biomarkers
in Glioblastoma

Glioblastoma is the most frequent and aggressive primary brain tumor. We study glioblastoma morphology in medical images to predict survival and personalize treatments.

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Big data and
biomarker design

Different types of explorations provide a plethora of data from cancer patients. Mathematical models help make sense of those data and identify patterns associated with the prognosis.

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Mathematics and
brain metastasis

Brain metastases are cancer cells that spread to the brain from other organs. We use mathematics to find biomarkers of survival and response to radio-surgery and design optimized treatments.

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Biomarkers in diabetes
and applications

Diabetes constitutes a major health problem with increasing incidence. We use mathematics to design biomarkers of utility for improving diabetes diagnosis and followup.

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PET-based biomarkers
in cancer

Positron-emission tomography (PET) is an imaging technique showing tumor metabolism. We use PET images and mathematical algorithms to define measures of tumor aggressiveness.

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Mathematics of
cancer models​

Mathematical models may describe processes of relevance in cancer research. We study their theoretical properties to understand their potential utility.

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Mathematics against
resistances

Resistance to chemotherapy is a major cause of cancer treatment failure. Mathematical models can describe how resistances develop and provide strategies to defer them the most.

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Mathematics and the tumor
microenvironment

Models of human tumors are used in biomedical research to resemble their complex behavior. We use mathematical models to understand the tumor microenvironment in-vivo and in-vitro.

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Scaling laws and fractals
in cancer

We study fractals and scaling laws in cancer data to find regularities behind the observed phenomena and define metrics of utility for
cancer treatments.

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Immunity and
immunotherapies

Cancer immunotherapies use the patient’s immune system against tumor cells. Mathematical models help to understand the actions of immunotherapies and to design more effective therapeutic approaches.

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Mathematics and
radiation therapy

Radiation therapy uses high doses of radiation to kill cancer cells. We use mathematical models to study how to best deliver radiation therapy and combine it with other treatments.

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Mathematics of cancer
metabolism

Normal cells obtain their energy by oxidation of glucose. Cancer cells use a less efficient way: glycolysis.
Mathematical models may help in understanding cancer cell metabolism and finding novel targets.

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Mathematics against
leukemias

Acute Lymphoblastic Leukemias are the most frequent type of cancer in children.
We use mathematical models to improve patient stratification and therapeutic schedules.

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Theranostics

Theranostics is a novel technique in which treatment and diagnostics merge through the use of radiopharmaceuticals. During its incipient first steps, mathematics is important for optimizing this emerging approach.

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Oncolytic viruses against pediatric neuroblastomas

Neuroblastomas are the most common tumors in children under one year of age. Through mathematical modeling, we study the efficacy of using oncolytic viruses to treat them.

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Mathematics against fibrous dysplasia

Fibrous dysplasia is a condition in which abnormal fibrous tissue grows in place of normal bone. We develop models of bone growth which are used to optimize the treatment of the patients.

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In-silico approaches to prostate cancer

Prostate cancer is the most frequent male cancer. We develop mathematical models of growth post-radiation therapy and use them to provide indicators of potential relapse.

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Biomarkers in lung cancer

Lung and bronchus cancer is responsible for the most deaths by a substantial margin. Mathematics can help classifying lung cancer lesion aggressiveness and help in the clinical decisions of when & how to treat.

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Mathematics
of cancer

Mathematical models help in understanding the details of tumor cells behavior and interactions, and in designing more effective therapeutic approaches.

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Evolutionary
dynamics

Cancer is an evolutionary process where different genotypes and phenotypes compete and cooperate. Mathematical models serve to quantify these dynamics, perform simulations, and gain insight into evolutionary mechanisms.

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Analytics

The digital revolution has brought an explosion of medical data. We work together with health and research institutions to gather data from big cohorts of patients in which we identify meaningful features.

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Oncolytic viruses against pediatric neuroblastomas

Neuroblastomas are the most common tumors in children under one year of age. Through mathematical modeling, we study the efficacy of using oncolytic viruses to treat them.

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In silico therapy
optimization

Clinical trials are expensive and take years to complete. We use mathematical modeling and computer simulations to carry out virtual clinical trials that identify optimum ways to apply a treatment.

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Biomarkers
design

An increasing amount of data from cancer patients is available through different omics technologies (genomics, proteomics, transcriptomics, etc.), as well as medical imaging like Magnetic Resonance (MRI) or Positron Emission Tomography (PET).

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Mathematics of cancer
metabolism

Normal cells obtain their energy by oxidation of glucose. Cancer cells use a less efficient way: glycolysis.

Mathematical models may help in understanding cancer cell metabolism and finding novel targets.

Learn More >

Mathematics against
leukemias

Acute Lymphoblastic Leukemias are the type of cancer with the highest incidence in children.

We use mathematical models to improve patient classification schemes and therapeutical combinations.

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Hyperspectral imaging
in gliomas

Identifying the precise boundaries of brain tumors for their resection is sometimes a difficult task even for skilled neurosurgeons. We collaborate with the HELICoiD project to discriminate between normal and cancerous tissues in real time using hyperspectral imaging.

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Scaling laws and fractals
in cancer

We study fractals and scaling laws in cancer data to find regularities behind the observed phenomena and define metrics of utility for
cancer treatments.

Learn More >

Immunity and
immunotherapies

The immune system is the complex set of biological defenses fighting infections and other diseases. We develop mathematical models of the immune system and immunotherapies.

Learn More >

Mathematics and
radiation therapy

Radiation therapy uses high doses of radiation to kill cancer cells. We use mathematical models to study how to best deliver radiation therapy and combine it with other treatments.

Learn More >

Mathematics of
cancer models​

Mathematical models may describe processes of relevance in cancer research. We study their theoretical properties to understand their potential utility.

Learn More >

Mathematics against
resistances

Resistance to chemotherapy is a major cause of cancer treatment failure. Mathematical models can describe how resistances develop and provide strategies to defer them the most.

Learn More >

Mathematics and the tumor
microenvironment

Models of human tumors are used in biomedical research to resemble their complex behavior. We use mathematical models to understand the tumor microenvironment in-vivo and in-vitro.

Learn More >

Optimizing therapies
"in-silico"

Gliomas are the most common class of brain tumors.
We use human data and mathematical methods to find treatment schedules and combinations improving survival.

Learn More >

Biomarkers in diabetes
and applications

Diabetes constitutes a major health problem with increasing incidence. We use mathematics to design biomarkers of utility for improving diabetes diagnosis and followup.

Learn More >

PET-based Biomarkers
in cancer

Positron-emission tomography (PET) is an imaging technique showing tumor metabolism. We use PET images and mathematical algorithms to define measures of tumor aggressiveness.

Learn More >

Imaging Biomarkers
in Glioblastoma

Glioblastoma is the most frequent and aggressive primary brain tumor. We study glioblastoma morphology in medical images to predict survival and personalize treatments.

Learn More >

Big data and
biomarker design

Many data provided by the different ‘omics’ technologies are available in cancer. We try to find quantifiers of tumor properties that can be used as biomarkers of clinical applicability.

Learn More >

Mathematics and
brain metastasis

Brain metastases are cancer cells that spread to the brain from other organs. We use mathematics to find biomarkers of survival and response to radio-surgery and design optimized treatments.

Learn More >