Systems and Personalized Medicine involves iterative and reciprocal feedback between clinical investigations and practice with computational, statistical and mathematical multiscale analysis and modelling disease onset, progression and remission mechanisms, both at the epidemiological and individual patient level. This new paradigm of Systems science and Medicine oppose or better, complement, the traditional reductionist approach and ideally will lead to the identification of mechanisms related to disease pathophysiology, selection of novel drug targets and biomarkers, and patient risk assessment and on the long run to individualized treatment.
We deploy Systems Medicine approaches to investigate:
We develop methods for network-based analysis of omcis data (transcriptomics, metabolomics, proteomics) and multivariate approaches to analyze high-dimensional data with a special focus on sparse component models. We use a combination chemometric, statistical and machine learning approaches to extract and to model relevant information from (large) biochemical and biomedical data sets.
See our software page for an overview of data analysis tools and software developed at SSB.
Please contact Dr. Edoardo Saccenti (email@example.com) for more information about possible collaborations and the possibility of MSc thesis and internships.
Internships at Erasmus MC:
PERMIT: Personalized Medicine in Infections: from Systems Biomedicine to Precision Diagnosis and Stratification Permitting Individualized Therapies
The project focuses on life-threatening infectious diseases, including the destructive necrotizing soft tissue infections (NSTI) and the large group of sepsis patients. The severity of these infections is dictated by the individual’s response to the pathogen and greatly depends on subject-specific host-pathogen interactions. For this reason, personalized therapeutic strategies targeting both the pathogen and host response are needed. To achieve this, PERMIT builds on the knowledge and resources created in the EU FP7-project INFECT including the world’s largest multicenter, prospectively enrolled patient cohort on NSTI, a biobank, multi-omics data, strategic data stewardship and pathophysiologic models. Building on this exceptional foundation, PERMIT will move towards preclinical validation of disease signatures, underlying mechanisms and biomarkers with the aim of translating these findings into improved or novel diagnostics for personalized treatment of NSTI and sepsis patients. The activities performed during the three years of the PERMIT project will demonstrate the clinical feasibility and potential benefit of a personalized medicine approach to the treatment of acute infectious diseases. The work will be conducted by a transnational interdisciplinary consortium of clinicians, experimentalists, bioinformaticians and computational biologists.
Principal investigator: Edoardo Saccenti; Project coordinator: Anna Norrgby-Teglund (Karolinska Institutet)
PERAID: Personalized Medicine in Acute Infectious Diseases
The project proposes to implement personalized medicine (PM) in severe infectious diseases, specifically focusing on the life-threatening necrotizing soft tissue infections (NSTI) as well as the large heterogeneous group of sepsis patients that represent a global health priority. Personalized medicine is a neglected, albeit much desired, development in the field of acute infectious diseases. The patient population is highly heterogeneous with different pathogens, pathogenic mechanisms, as well as varying predisposing host factors, and importantly, a dysregulated host response to infection is directly linked to severity and outcome of severe infections, i.e. sepsis and NSTI. Hence, tailored immunotherapy in stratified patients holds great potential. PERAID builds on the advances and resources created in the EU FP7-project INFECT, including the world’s largest patient cohort on NSTI (Scandinavian patients enrolled by PERAID’s partners), biobank, multi-omics data, and pathophysiologic models. These already available resources combined with an extended Nordic consortium enables PERAID to advance to the next phase of implementing PM in NSTI as well as extending the efforts into the sepsis field.
Principal investigator: Edoardo Saccenti; Project coordinator: Anna Norrgby-Teglund (Karolinska Institutet)
PerICo: Peroxisome Interactions and Communication
PerICo fosters education of 15 Early Stage Researchers (ESRs) in projects aimed at uncovering how peroxisomes, important cellular organelles, participate in the intricate cellular interaction and signaling network. In light of the current understanding of the central role of peroxisomes in a variety of metabolic diseases, cancer and aging – this timely research programme will benefit society. The ESRs will be trained at world-leading academic institutions, including university hospitals and companies, thus forming strong interdisciplinary links between industry, life/medical sciences, and end-users. For more information check the project website.
In collaboration with LifeGlimmer
INFECT: Improving Outcome of Necrotizing Fasciitis: Elucidation of Complex Host and Pathogen Signatures that Dictate Severity of Tissue Infection. (2013 – 2018)
The overall goal of INFECT was to advance our understanding of the pathophysiological mechanisms, prognosis, and diagnosis of the multifactorial highly lethal necrotizing soft tissue infections (NSTIs). NSTI’s are rapidly spreading infections that may cause extensive soft tissue or limb loss, multiorgan failure and are associated with a considerable fatality rate. It is undisputed that rapid diagnosis and prompt intervention is directly related to survival. The initial presentation may be limited to unspecific symptoms such as tenderness, swelling, erythema and pain. Thus, diagnosis and management are difficult due to heterogeneity in clinical presentation, in co-morbidities and in microbiological aetiology. There is an urgent need for novel diagnostic and therapeutic strategies in order to improve outcome of NSTIs. To achieve this, a comprehensive and integrated knowledge of diagnostic features, causative microbial agent, treatment strategies, and pathogenic mechanisms (host and bacterial disease traits and their underlying interaction network) is required.
INFECT was designed to obtain such insight through an integrated systems biology approach in patients (WP2) and different clinically relevant experimental models (WP1 and WP6). The work flow includes a comprehensive set of analyses (WP3 and WP5) followed by integration of results in advanced computational platforms, which enabled generation of pathophysiological models of the disease (WP4) and advanced understanding of the underlying mechanisms and hots-pathogen interactions. The results were translated into novel diagnostic tests (WP7) and improved patient management (WP2 & 8). The work was conducted by the INFECT consortium, which consisted of a team of multidisciplinary researchers, clinicians, SMEs and a patient organization, each with a unique expertise, technical platform and/or model systems that together provided the means to successfully conduct the multifaceted research proposed and efficiently disseminate/exploit the knowledge obtained.
You can read more about the project and its achievements here.
(1) Jahagirdar, S.; Suarez-Diez, M.; Saccenti, E., Simulation and reconstruction of metabolite-metabolite association networks using a metabolic dynamic model and correlation based-algorithms J. Proteome Res 2019.
(2) Camacho, J.; Maciá-Fernández, G.; Fuentes-García, N. M.; Saccenti, E., Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats IEEE Transactions on Information Forensics and Security 2019.
(3) Hechler, C.; Borewicz, K.; Beijers, R.; Saccenti, E.; Riksen-Walraven, M.; Smidt, H.; de Weerth, C., Association between Psychosocial Stress and Fecal Microbiota in Pregnant Women Scientific Reports 2019, 9, 4463.
(4) Camacho, J.; Saccenti, E., Group-wise partial least square regression J. Chemometrics 2018, 32, e2964-n/a.
(5) Madsen, M. B.; Skrede, S.; Bruun, T.; Arnell, P.; Rosén, A.; Nekludov, M.; Karlsson, Y.; Bergey, F.; Saccenti, E.; Martins dos Santos, V. A. P.; Perner, A.; Norrby-Teglund, A.; Hyldegaard, O., Necrotizing soft tissue infections – a multicentre, prospective observational study (INFECT): protocol and statistical analysis plan Acta Anaesthesiol. Scand. 2018, 62, 272-279.
(6) Wintjens, D.; Bergey, F.; Saccenti, E.; Jeuring, S.; Romberg-Camps, M.; Oostenbrug, L.; Masclee, A.; Jonkers, D.; Martins dos Santos, V.; Pierik, M., Assessment of disease activity patterns during the first 10 years after diagnosis in a population-based Crohn’s disease cohort shows a quiescent disease course for a substantial proportion of the population Journal of Crohn’s and Colitis 2018, 12, S001-S003.
(7) Rosato, A.; Tenori, L.; Cascante, M.; De Atauri Carulla, P. R.; Martins dos Santos, V. A. P.; Saccenti, E., From correlation to causation: analysis of metabolomics data using systems biology approaches Metabolomics 2018, 14, 37.
(8) Rosén, A.; Arnell, P.; Madsen, M.; Nedrebø, B.; Norrby?Teglund, A.; Hyldegaard, O.; Dos Santos, V.; Bergey, F.; Saccenti, E.; Skrede, S., Diabetes and necrotizing soft tissue infections—A prospective observational cohort study: Statistical analysis plan Acta Anaesthesiol. Scand. 2018.
(9) Saccenti, E.; Smilde, A. K.; Camacho, J., Group-wise ANOVA simultaneous component analysis for designed omics experiments Metabolomics 2018, 14, 73.
(10) Vitale, R.; Saccenti, E., Comparison of dimensionality assessment methods in Principal Component Analysis based on permutation tests Chemometrics Intellig. Lab. Syst. 2018, 181, 79-94.
(11) Xu, W.; Vervoort, J.; Saccenti, E.; van Hoeij, R.; Kemp, B.; van Knegsel, A., Milk Metabolomics Data Reveal the Energy Balance of Individual Dairy Cows in Early Lactation Scientific Reports 2018, 8, 15828.
(12) Vignoli, A.; Tenori, L.; Luchinat, C.; Saccenti, E., Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects J. Proteome Res 2018, 17, 97-107.
(13) Suarez-Diez, M.; Adam, J.; Adamski, J.; Chasapi, S. A.; Luchinat, C.; Peters, A.; Prehn, C.; Santucci, C.; Spyridonidis, A.; Spyroulias, G. A.; Tenori, L.; Wang-Sattler, R.; Saccenti, E., Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling J. Proteome Res 2017.
(14) Camacho, J.; Rodríguez-Gómez, R. A.; Saccenti, E., Group-wise principal component analysis for exploratory data analysis Journal of Computational and Graphical Statistics 2017, 26, 501-512.
(15) Saccenti, E.; Timmerman, M. E., Considering Horn’s Parallel Analysis from a Random Matrix Theory Point of View Psychometrika 2017, 82, 186-209.
(16) Bergey, F.; Saccenti, E.; Jonkers, D.; van den Heuvel, T.; Jeuring, S.; Pierik, M.; Dos Santos, V. M., New approaches for Ibd management based on text mining of digitalised medical reports and latent class modelling Journal of Crohn’s & colitis 2017, 11, S237-S238.
(17) Venkatasubramanian, P. B.; Toydemir, G.; Wit, N.; Saccenti, E.; dos Santos, V. A. M.; Baarlen, P.; Wells, J. M.; Suarez-Diez, M.; Mes, J. J., Use of Microarray Datasets to generate Caco-2-dedicated Networks and to identify Reporter Genes of Specific Pathway Activity Scientific Reports 2017, 7, 6778.
(18) Stroeve, J. H. M.; Saccenti, E.; Bouwman, J.; Dane, A.; Strassburg, K.; Vervoort, J.; Hankemeier, T.; Astrup, A.; Smilde, A. K.; van Ommen, B.; Saris, W. H. M., Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study Obesity 2016, 24, 379-388.
(19) Saccenti, E.; Timmerman, M. E., Approaches to Sample Size Determination for Multivariate Data: Applications to PCA and PLS-DA of Omics Data J. Proteome Res 2016, 15, 2379-2393.
(20) Saccenti, E.; Menichetti, G.; Ghini, V.; Remondini, D.; Tenori, L.; Luchinat, C., Entropy-Based Network Representation of the Individual Metabolic Phenotype J. Proteome Res 2016, 15, 3298-3307.
(21) Saccenti, E., Correlation Patterns in Experimental Data Are Affected by Normalization Procedures: Consequences for Data Analysis and Network Inference J. Proteome Res 2016.
(22) Cacciatore, S.; Saccenti, E.; Piccioli, M., Hypothesis: The Sound of the Individual Metabolic Phenotype? Acoustic Detection of NMR Experiments OMICS: J. Integrative Biol. 2015, 19, 147-156.
(23) Saccenti, E.; van Duynhoven, J.; Jacobs, D. M.; Smilde, A. K.; Hoefsloot, H. C. J., Strategies for Individual Phenotyping of Linoleic and Arachidonic Acid Metabolism Using an Oral Glucose Tolerance Test PLoS ONE 2015, 10, e0119856.
(24) Smilde, A. K.; Timmerman, M. E.; Saccenti, E.; Jansen, J. J.; Hoefsloot, H. C. J., Covariances Simultaneous Component Analysis: a new method within a framework for modeling covariances J. Chemometrics 2015, 29, 277-288.
(25) Saccenti, E.; Camacho, J., On the use of the observation-wise k-fold operation in PCA cross-validation J. Chemometrics 2015, 29, 467 – 478.
(26) Ghini, V.; Saccenti, E.; Tenori, L.; Assfalg, M.; Luchinat, C., Allostasis and Resilience of the Human Individual Metabolic Phenotype J. Proteome Res 2015, 14, 2951-2962.
(27) Suarez-Diez, M.; Saccenti, E., Effects of sample size and dimensionality on the performance of four algorithms for inference of association networks in metabonomics J. Proteome Res 2015.
(28) Saccenti, E.; Camacho, J., Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods Chemometrics Intellig. Lab. Syst. 2015, 149, Part A, 99-116.
(29) Calabrò, A.; Gralka, E.; Luchinat, C.; Saccenti, E.; Tenori, L., A Metabolomic Perspective on Coeliac Disease Autoimmune diseases 2014, 2014.
(30) Saccenti, E.; Tenori, L.; Verbruggen, P.; Timmerman, M. E.; Bouwman, J.; van der Greef, J.; Luchinat, C.; Smilde, A. K., Of Monkeys and Men: A Metabolomic Analysis of Static and Dynamic Urinary Metabolic Phenotypes in Two Species PloS one 2014, 9, e106077.
(31) Saccenti, E.; Suarez-Diez, M.; Luchinat, C.; Santucci, C.; Tenori, L., Probabilistic Networks of Blood Metabolites in Healthy Subjects As Indicators of Latent Cardiovascular Risk J. Proteome Res 2014, 14, 1101–1111.