Associated material
Related literature
Other articles by authors
Related articles/pages
Download to ...
Share this article
Your browser does not support iframes
Email updates
This article is part of the supplement:
Data integration and exploration for the identification of molecular mechanisms in tumor-immune cells interaction
Bernhard Mlecnik, Fatima Sanchez-Cabo, Pornpimol Charoentong, Gabriela Bindea, Franck Pagès, Anne Berger, Jerome Galon* and Zlatko Trajanoski*
Corresponding authors:
Trajanoski
Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria
INSERM, U872, Integrative Cancer Immunology, Paris, France
Genomics Unit, Spanish National Centre for Cardiovascular Research, Madrid, Spain
AP-HP, Georges Pompidou European Hospital, Paris, France
For all author emails, please .
BMC Genomics 2010, 11(Suppl 1):S7&
doi:10.64-11-S1-S7
The electronic version of this article is the complete one and can be found online at:
Published:10 February 2010
& 2010 M licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cancer progression is a complex process involving host-tumor interactions by multiple
molecular and cellular factors of the tumor microenvironment. Tumor cells that challenge
immune activity may be vulnerable to immune destruction. To address this question
we have directed major efforts towards data integration and developed and installed
a database for cancer immunology with more than 1700 patients and associated clinical
data and biomolecular data. Mining of the database revealed novel insights into the
molecular mechanisms of tumor-immune cell interaction. In this paper we present the
computational tools used to analyze integrated clinical and biomolecular data. Specifically,
we describe a database for heterogenous data types, the interfacing bioinformatics
and statistical tools including clustering methods, survival analysis, as well as
visualization methods. Additionally, we discuss generic issues relevant to the integration
of clinical and biomolecular data, as well as recent developments in integrative data
analyses including biomolecular network reconstruction and mathematical modeling.
Background
Despite extensive characterization of environmental and intrinsic and underlying mechanisms
[,], markers of the oncogenic process remain so far poorly predictive of patient survival
and fail to prove their reliability in clinical use. For example, colorectal cancer
is one of the most common malignancies for both men and women []. The rate of localized cancers (stage I-II; UICC-TNM classification) is about 40%
[,]. Despite surgery with curative intent, the risk of recurrence of these early-stage
patients is high (approximately 20-30%). To subject all of these patients to post-operative
chemotherapy may be inappropriate and costly []. Genetic and molecular tumor prognostic factors have been proposed to identify patients
who may be at risk for recurrence. None has yet been sufficiently informative for
inclusion in clinical practice []. Identification of patients with high-risk of recurrence is therefore a major clinical
issue. However, in order to develop stratified or personalized strategies for such
complex multifactorial disease it is of importance to understand how numerous and
diverse elements function together in human pathology. A comprehensive understanding
of cancer requires the integration and analysis of data not only from the tumor but
also its microenvironment including the immune cells.
Tumors are composed of a complex network of tumor cells, immune cells, stromal components
including fibroblasts, and a complex vasculature. To grow, invade, and metastasize,
a tumor interacts with its microenvironment, composed of diverse cells of various
origins. The microenvironment contains cells of the immune system, including inflammatory
infiltrates of innate immunity and infiltrates of the adaptive immune response. In
colorectal cancer, previous studies have suggested a clinical role of the immune infiltrates
[-]. In order to investigate the role of the immune infiltrates and analyze the tumor
immunological microenvironment in humans we developed and installed a database for
cancer immunology with more than 1700 patients and associated clinical data and biomolecular
data. By analyzing the data we showed the importance of early-metastatic invasion
in colorectal cancer and could pinpoint a novel prognostic marker for survival []. We evidenced that the recently characterized immune cell subpopulation of effector-memory
T cells (TEM), may have a central role in the control of tumor spreading to lymphovascular and
perineural structures but also to lymph node or distant organs. In subsequent study
we demonstrated the role of the adaptive immune system for predicting clinical outcome
[]. Furthermore, we revealed the importance for patient prognosis of the nature, the
functional orientation, the density and the localization of immune cell populations
within the primary tumor. Thus, adaptive immune reaction and intratumoral T-cell subpopulations
were better predictor of survival than traditional staging based on a cancer's size
and spread [].
In the light of these studies it was of utmost importance to integrate the data and
develop tools for analysis and visualization. In this paper, we present the solutions
developed to analyze the tumor immunological microenvironment in humans including
database, analytical tools, and tools for visualization. Specifically, we describe
here the database for clinical and biomolecular data, the interfacing bioinformatics
and statistical tools including clustering methods, survival analysis, as well as
visualization methods. Furthermore, we discuss upcoming developments for integrative
data analyses including biomolecular network reconstruction and mathematical modeling.
Bioinformatics and statistics tools for cancer immunology
Database for cancer immunology
The database developed for cancer immunology (Tumor Microenvironment (TME)) integrates
clinical and biomolecular data. The underlying relational database model is designed
as a cancer patient oriented database which takes all the patients anamnesis and clinical
and medical history information into account whereby all patients are linked to a
speci?c hospital. Security issues were treated in regard to the interest of patients.
Ethical, Legal and Social Implications (ELSI) have been fulfilled (agreement #903434),
security modules implemented, and anonymous information stored. The patient information
additionally includes medical problems, surgery and detailed cancer information. Additionally
TME.db allows the storage of a variety of different high-throughput experiments including:
o Real-Time TaqMan qPCR gene expression data (Low density arrays, single probes, T-cell
repertoire analysis)
o Microsatellite instability (MSI) and mutations data
o Flow cytometric (FACS) phenotyping data
o Protein quantification (ELISA, Quantibody, cytometric beads assays) data
o Functional data (proliferation, survival, apoptosis, migration assays)
o Immunohistochemical data (Tissue Micro Array (TMA) and whole slide analysis)
TME.db joins and integrates all different types of data and stores them in a common
place where all the determined analysis parameters are linked in a clear way dependent
on the sample material and the experiment type. For accessing all the stored information
again sophisticated query methods were developed in order to retrieve the data in
a pre-modi?ed way, already prepared for statistical analysis. As of May 2009, the
database incorporates 1784 patients with associated clinical data with 60 parameters
(e.g. tumor staging, treatment, cancer relapse) and 16400 different material information
as well as biomolecular measurements (including qPCR for 400 genes from 125 patients,
820 FACS parameters from 40 patients, 20 tissue microarray assays for 600 patients).
Software architecture
TME is a multi-tier client-server application and can be subdivided into different
functional modules which interact as self-contained units according to their defined
responsibilities: presentation tier, business tier and runtime environment. The presentation
tier within TME is formed by a Web interface, which allows programming access to parts
of the application logic. Thus, on the client side, a user requires an Internet connection
and a recent Web browser with Java support, available for almost every platform. The
business tier is realized as view-independent application logic, which stores and
retrieves datasets by communicating with the persistence layer. The internal management
of files is also handled from a central service component, which persists the meta-information
for acquired files to the database. All services of this layer are implemented as
STRUTS and are using SITEMESH.
Model driven development
In order to reduce coding and to increase the long term maintainability, the model
driven development environment AndroMDA is used to generate components of the persistence
layer and recurrent parts from the above mentioned business layer. AndroMDA accomplishes
this by translating an annotated UML-model into a JEE-platform-specific implementation
using Enterprise Java Beans (EJB), STRUTS and SITEMESH. Due to the flexibility of
AndroMDA, application external services, such as the user management system, have
a clean integration in the model. Dependencies of internal service components on such
externally defined services are cleanly managed by its build system. By changing the
build parameters in the AndroMDA configuration, it is also possible to support different
relational database management systems. This is because platform specific code with
the same functionality is generated for data retrieval. Furthermore, technology lock-in
regarding the implementation of the service layers was also addressed by using AndroMDA,
as the implementation of the service facade can be switched during the build process
from Spring based components to distributed Enterprise Java Beans. At present, TME
is operating on one local machine and, providing the usage scenarios do not demand
it, this architectural configuration will remain. However, chosen technologies are
known to work on Web server farms and crucial distribution of the application among
server nodes is transparently performed by the chosen technologies.
Data retrieval, collaboration and data sharing
TME offers search masks which allow keyword based searching in the recorded projects,
experiments and notes. These results are often discussed with collaboration partners
to gain different opinions on the same raw data. In order to allow direct collaboration
between scientists TME is embedded into a central user management system which offers
multiple levels of access control to projects and their associated experimental data.
The sharing of projects can be done on a per-user basis or on an institutional basis.
For small or local single-user installations, the fully featured user management system
can be replaced by a file-based user management which still offers the same functionalities
from the sharing point of view, but lacks institute-wide functionalities.
Bioinformatics analysis tools
The database was mined using standard bioinformatics tools. Specifically, qPCR and
FACS data were explored using two-dimensional hierarchical clustering of correlation
matrices (i.e. gene-wise correlation of the respective patient groups []). Genesis clustering software was used to visualize the correlation matrix and to
perform Pearson un-centered hierarchical clustering []. This tool was developed for large-scale gene expression cluster analysis and integrates
various tools for microarray data analysis such as filters, normalization and visualization
tools, distance measures as well as common clustering algorithms including hierarchical
clustering, self-organizing maps, k-means, principal component analysis, and support
vector machines [].
Statistical analysis
Survival analysis provides a statistical framework for the modeling and statistical
analysis of the time to event for a cohort of patients []. Since the distribution of survival times might have an unusual and often unknown
form, nonparametric Kaplan-Meier estimates are widely used when censoring is present
for the characterization of groups of patients with different underlying characteristics,
i.e. calculating median survival times and patients at risk after a given period.
Similarly, the log-rank non-parametric test is used to check the null hypothesis that
at any time point there is no difference in the probability of the event of interest
between the groups []. The magnitude of the difference and its confidence interval can be calculated using
a Cox proportional hazards model. Furthermore the effect of a novel biomarker can
be adjusted for traditional parameters if this modeling strategy is used on several
covariates.
TME implements the previous tests within a statistical analysis module. Calculations
are done using the survival package from R [] to which TME connects using RServe []. The aim is the automatic detection of biomarkers or sets of biomarkers that - alone
or in combination with other parameters - are able to discriminate groups of colorectal
cancer patients with good prognosis from those with bad prognosis for both, overall
and disease-free survival. In particular, TME provides:
- Kaplan-Meier curves, estimates of the median survival time and number of patients
at risk after a certain time period for the different groups of patients
- Log-rank test for the analysis of the differences in survival between groups of
patients with different underlying characteristics
- Univariate Cox proportional hazards model to estimate the magnitude of the effect
of the covariate in survival
- Tools for the categorization of numeric covariates into a fixed number of levels.
This can be useful for the classification of the patients into groups based on the
biomolecular markers stored in TME for each patient, such as the expression level
of a gene or the number of cells of a given type found at different locations of the
tumor sample.
Although categorization of the patients into groups might result in loss of information
[], this is often done in clinical practice. The way the cut-off is set for dichotomizing
a continuous variable is also controversial: A previously described value or a biologically
justified level can be used as suggested by Altman et al []. In the absence of a biologically sound cut-off value, using a statistic of the sample
(such as the median) balances the number of cases per group but results in different
levels across studies making the comparison of results from different groups difficult
[]. Hence, the analysis must be repeated in an independent cohort of patients categorized
using the cut-off previously selected. The same is true when using the "minimum p-value"
approach [], i.e. taking the point yielding the "maximum" significance between groups. This approach
has additional important problems such as the overestimation of the prognostic importance
of the covariate and multiple testing issues that might be accounted for []
TME allows the inspection of the covariates dichotomizing them based in any of the
previous options. In particular, if the minimum p-value approach is used the log-rank
p-value can be corrected using either the formula proposed by Altman et al [] or with cross-validation as proposed by Faraggi & Simon []. Additionally, TME implements the shrinkage method proposed by Holl?nder et al [] to correct the hazard ratios.
Next version of TME will also include multivariate analysis using a Cox proportional
hazards model and decision trees, which can easily accommodate heterogeneous variables
and have yielded already satisfactory results in the discovery of biomarkers for breast
cancer [].
Data visualization
Data visualization was carried out using the publicly available software tools Cytoscape,
ClueGO, and GOlorize. Cytoscape is free software package for visualizing, modeling
and analyzing molecular and genetic interaction networks [-]. In Cytoscape, the nodes represent genes or proteins and they are connected with
edges which representing interactions. Typical biological networks at the molecular
level are gene regulation networks, signal transduction networks, protein interaction
networks, and metabolic networks. In order to capture biological information, ClueGO
[], a Cytoscape plug-in, uses Gene Ontology [] categories that are overrepresented in selected one or two lists of genes. ClueGO
takes advantage of GOlorize [] plug-in, an efficient tool to the same class node-coloring and the class-directed
layout algorithm for advanced network visualization.
Discussion
In this paper we described computational tools developed specifically to address biological
questions in cancer immunology. The computational tools include: 1) a database for
clinical and biomolecular data comprising &1700 patients with associated clinical
information, FACS data, qPCR data, ti 2) bioinformatics tools
developed for the analyses of medium and large-scale data, 3) statistical tools for
t and 4) tools for visualization of the data. The power of the
dedicated informatics solution is leveraged by the integration of all computational
resources using various interfaces. During the course of the development of the database,
the implementation of the analytical tools, and the analysis of the data we have learned
several important lessons.
Lessons learned
First, development of a dedicated database is time-consuming but indispensable task.
In recent years, the biology community has expended considerable effort to confront
the challenges of managing heterogeneous data in a structured and organized way and
as a result developed information management systems for both raw and processed data.
Laboratory information management systems (LIMS) have been implemented for handling
data entry from robotic systems and tracking samples as well as data management systems
for processed data including microarrays, proteomics data, and microscopy data. In
general, these sophisticated systems are able to manage and analyze data generated
for only a single type or a limited number of instruments, and were designed for only
a specific type of molecule. Thus, addressing a biological question relying on several
complementary technologies requires a specific off-the-shelf database. It should be
noted that such a database could absorb several person-years of software engineering
and this effort tends to be underestimated.
Second, incorporation of clinical data poses additional challenges. Many institutions
have electronic patient records and in principle, extracting the information could
be straightforward. However, technical, ethical, and legal issues might delay or even
prohibit the process of data collection. Heterogeneous clinical and departmental information
systems, accessibility of patient data, and managing sensitive information can introduce
several levels of complexity and require extensive stakeholder discussions. A complex
information management system that captures in a secure way the relevant data is suggestive
only for large (i.e. several hundred PIs) institutions. The majority of the labs are
better off with a design of a relatively small, departmental database for only few
specific cohorts. The patient data should be first de-identified and then provided
to the biologists and bioinformaticians.
Third, primary data should be archived at a separate location and only preprocessed
and normalized data should be stored in the dedicated database. Although it is tempting
to upload and analyze all types of data in a single system, experience shows that
primary data is mostly used once. This approach is even more advisable for large-scale
data including microarrays, proteomics of sequence data. However, links to the primary
data need to be secured so that later re-analyses using improved tools can be guaranteed.
In this context it is noteworthy that in the analyses we have performed so far only
medium-throughput data was used, meaning that the number of analyzed molecular species
was in the range of 100-1000. With this number of elements the majority of the tools
perform satisfactorily on a standard desktop computer. Performance is a crucial issue
if the number of molecules detected in a single patient sample increases to &10.000
(like in microarray studies) or &100.000 (proteomics studies) and the used methods
need to be re-evaluated.
In this paper we show a powerful approach for integrative analyses of heterogenous
biomolecular data and clinical data. Although powerful, our approach was sequential,
i.e. the data was integrated in the database and the query masks allowed sequential
analyses of specific biomolecular data, and their correlation with clinical data.
We strongly believe that integrative data analyses methods will provide additional
insights otherwise hidden in the complex data sets. Several approaches were suggested
previously (e.g. [-,-]). However, normalization of the data, availability of reference datasets, and scarcity
of the data (specific measurements are not available for all patients) are non-trivial
issues which are difficult to address. In this context, novel data integration approaches
are highly desirable. In the following paragraphs we highlight two approaches, namely
biomolecular network reconstruction and mathematical modelling, which have the potential
to provide mechanistic insights and ultimately translation of this knowledge to clinical
applications.
Biomolecular network reconstruction
One emerging field, which was not addressed in this paper is biomolecular network
reconstruction. The data we have so far used are actual measurements and are limited
to the available technology and/or samples. There is a wealth of information stored
in public databases on protein-protein interactions, text mining, two-hybrid screens,
or gene silencing using siRNA. The integration of this datasets in databases like
STRING [] and the visualization tools like Cytoscape [] and associated-software such as ClueGO [] opens new avenues of exploration of biomolecular networks.
Mathematical modeling
Since the pathophysiological mechanisms underlying cancer are highly complex and involve
many different cell types and processes, mathematical modeling is becoming an important
tool to integrate the biological information and enhance our understanding of interaction
between cancer and immune system. Moreover, mathematical modeling may direct direction
of experimental work for treatment and diagnosis. Here we briefly describe relevant
modeling efforts for tumour-immune cells interaction.
Mathematical models of cancer
Traditionally, mathematical models of cancer fall into two broad camps: descriptive
and mechanistic []. Descriptive models tend to focus on reproducing the gross characteristic of tumors
such as size and cell numbers, are generally used to investigate tumor cell population
dynamics, without emphasis on cell biological detail [-]. Over the last decades, many mathematical models have been proposed that focus on
tumor growth. Macklin et al. [] performed a new multiscale mathematical model for solid tumor growth which couples
an improved model of tumor invasion with a model of tumor-induced angiogenesis. A
large number of studies have described deterministic models which have been used to
model the spatio-temporal spread of tumors []. By contrast, mechanistic models focus on specific aspects of tumor progression in
order to explain the underlying biological processes that drive them [,,].
Mathematical models of immune response
The regulation of immune system involves the interaction between populations of pathogen
and immune cell. Immunological memory and specificity are property of the immune system.
This ability to respond more rapidly and effective than to the first exposure []. Understanding of these aspects requires quantitative models of proliferation and
differentiation of T lymphocytes. Mathematical modeling can describe these behaviors
as deterministic or stochastic models. De Boer et al. proposed the simple mathematical model in which parameters can be estimated (proliferation
and death rate) during clonal expansion and contraction phase [,]. Three models have been proposed by Ganusov [] to discriminate between alternative memory cell differentiation pathways.
Mathematical models of cancer-immune interactions
Mathematical modeling of tumor growth that includes the immune response and chemotherapy
treatment would provide an analytical predictive framework. Kim et al. developed a mathematical model with the new experimental data to gain insights into
the dynamics and potential impact of the resulting anti-leukemia immune response on
chronic myelogenous leukemia (CML) []. Moore et al. modeled the interaction T cell subpopulations and CML cancer cells in the body,
using a system of ordinary differential equations []. Steffen et al. presented a mathematical model of melanoma invasion into healthy tissue with an
immune response. They used this model as a framework with which to investigate primary
tumor invasion and treatment by surgical excision [].
Conclusion
In this paper we presented computational tools developed to manage and explore clinical
and biomolecular data for the identification of molecular mechanisms in the tumor
microenvironment. The presented bioinformatics and statistics solutions were applied
on a patient cohort with colorectal cancer and revealed novel insights in the tumor-immune
cells interaction. Although used to address a specific question, the approach is generic
and can be applied also to different cancers as well as to other multifactorial diseases
like diabetes or cardiovascular diseases.
List of abbreviations used
JavaEE: Java Enterprise E MDA: Model Driven A SOAP: Simple
Object Access Protocol
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BM developed the database. BM, FSC, GB, and PC carried out the analyses. FP and AB
collected and annotated the clinical data. JG and ZT coordinated the project. All
authors contributed to the drafting of the manuscript, and read and approved the final
manuscript.
Acknowledgements
This work was supported by the Austrian Ministry for Science and Research, GEN-AU
Project Bioinformatics Integration Network (BIN), Austrian Science Fund (SFB Project
Lipotoxicity), INSERM, the National Cancer Institute (INCa), Association pour la Recherche
sur le Cancer (ARC), the Cancéropole Ile de France, Ville de Paris, and by the European
Commission (FP7, Geninca Consortium, grant number 202230).
This article has been published as part of BMC Genomics Volume 11 Supplement 1, 2010: International Workshop on Computational Systems Biology
Approaches to Analysis of Genome Complexity and Regulatory Gene Networks. The full
contents of the supplement are available online at .
References
Halverson D,
Palmieri D,
Salerno M:
Metastasis suppressor genes: basic biology and potential clinical use. Clin Breast Cancer 2003,
Hanahan D,
Weinberg RA:
The hallmarks of cancer. Cell 2000,
100:57-70.
Parkin DM,
Global cancer statistics, 2002. CA Cancer J Clin 2005,
55:74-108.
Sobin LWC: TNM classification of malignant timors.
Locker GY,
RJ B, ASCO:
ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal
cancer. J Clin Oncol 2009,
Benson AB III,
Somerfield MR,
Figueredo AT,
Krzyzanowska MK,
McAllister P,
Van Cutsem E, et al.:
American Society of Clinical Oncology recommendations on adjuvant chemotherapy for
stage II colon cancer. J Clin Oncol 2004,
Dalerba P,
Maccalli C,
Castelli C,
Parmiani G:
Immunology and immunotherapy of colorectal cancer. Crit Rev Oncol Hematol 2003,
Neurath MF:
Immune cells in colorectal cancer: prognostic relevance and therapeutic strategies. Expert Rev Anticancer Ther 2008,
8:561-572.
Sanchez-Cabo F,
Kirilovsky A,
Mlecnik B,
Lagorce-Pages C,
Tosolini M,
Wind P, et al.:
Type, density, and location of immune cells within human colorectal tumors predict
clinical outcome. Science 2006,
Sanchez-Cabo F,
Molidor R,
Mlecnik B,
Kirilovsky A,
Nilsson M,
Damotte D, et al.:
Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 2005,
Fridman WH,
The adaptive immunologic microenvironment in colorectal cancer: a novel perspective. Cancer Res 2007,
Quackenbush J,
Trajanoski Z:
Genesis: cluster analysis of microarray data. Bioinformatics 2002,
18:207-208.
Harrel FE: Regression modeling strategies: with applications to Linear Models, Logistic Regression
and Survival analysis.
Springer Series in S
Altman DG:
The logrank test. BMJ 2004,
Altman DG,
Royston P:
The cost of dichotomising continuous variables. BMJ 2006,
Altman DG,
Sauerbrei W,
Schumacher M:
Dangers of using "optimal" cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst 1994,
86:829-835.
Heinzl HTC:
A cautionary note on segmenting a cyclical covariate by minimum P-value search. Computational Statistics & Data Analysis 2009,
35:451-461.
Faraggi D,
A simulation study of cross-validation for selecting an optimal cutpoint in univariate
survival analysis. Stat Med 1996,
<a target="_blank" href="http://dx.doi.org/10.1002/(SICI):20Publisher&Full&Text
Hollander N,
Sauerbrei W,
Schumacher M:
Confidence intervals for the effect of a prognostic factor after selection of an 'optimal'
cutpoint. Stat Med 2004,
Pittman J,
Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes. Biostatistics 2004,
5:587-601.
Shannon P,
Markiel A,
Baliga NS,
Schwikowski B,
Cytoscape: a software environment for integrated models of biomolecular interaction
networks. Genome Res 2003,
Kuchinsky A,
Workman C,
Christmas R,
Avila-Campilo I,
Gross B, et al.:
Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007,
Mlecnik B,
Charoentong P,
Tosolini M,
Kirilovsky A,
Fridman WH,
Trajanoski Z,
ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway
annotation networks. Bioinformatics 2009,
Saveanu C,
Fromont-Racine M,
Jacquier A,
Schwikowski B,
Aittokallio T:
GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout
and coloring. Bioinformatics 2007,
23:394-396.
Ashburner M,
Botstein D,
Cherry JM,
Dolinski K,
Dwight SS,
Eppig JT, et al.:
Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000,
Leslie DM,
Weston AD,
de Atauri P,
Aitchison JD,
Siegel AF, et al.:
A data integration methodology for systems biology. Proc Natl Acad Sci USA 2005,
Fuhrman S,
Somogyi R:
Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput .
Margolin AA,
Nemenman I,
Wiggins C,
Stolovitzky G,
Califano A:
ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian
cellular context. BMC Bioinformatics 2006,
7(Suppl 1):S7.
von Mering C,
Jensen LJ,
Hooper SD,
Foglierini M,
Jouffre N,
Huynen MA,
STRING: known and predicted protein-protein associations, integrated and transferred
across organisms. Nucleic Acids Res 2005,
33:D433-D437.
Anderson AR,
Quaranta V:
Integrative mathematical oncology. Nat Rev Cancer 2008,
8:227-234.
Araujo RP,
McElwain DL:
A history of the study of solid tumour growth: the contribution of mathematical modelling. Bull Math Biol 2004,
Kozusko F,
Bourdeau M:
A unified model of sigmoid tumour growth based on cell proliferation and quiescence. Cell Prolif 2007,
40:824-834.
Macklin P,
McDougall S,
Anderson AR,
Chaplain MA,
Cristini V,
Lowengrub J:
Multiscale modelling and nonlinear simulation of vascular tumour growth. J Math Biol 2009,
58:765-798.
Chapman SJ,
Mathematical models of avascular tumor growth. Siam Review 2007,
49:179-208.
Anderson ACMRK: Single-Cell-Based Models in Biology and Medicine (Mathematics and Biosciences in Interaction).
1st edition.
Birkhauser B
Beverley PC:
Primer: making sense of T-cell memory. Nat Clin Pract Rheumatol 2008,
De Boer RJ,
Murali-Krishna K,
Perelson AS:
Recruitment times, proliferation, and apoptosis rates during the CD8(+) T-cell response
to lymphocytic choriomeningitis virus. J Virol 2001,
De Boer RJ,
Perelson AS:
Different dynamics of CD4+ and CD8+ T cell responses during and after acute lymphocytic
choriomeningitis virus infection. J Immunol 2003,
Ganusov VV,
The role of models in understanding CD8+ T-cell memory. Nat Rev Immunol 2005,
5:101-111.
Dynamics and potential impact of the immune response to chronic myelogenous leukemia. PLoS Comput Biol 2008,
4:e1000095.
A mathematical model for chronic myelogenous leukemia (CML) and T cell interaction. J Theor Biol 2004,
227:513-523.
Eikenberry S,
Thalhauser C,
Tumor-immune interaction, surgical treatment, and cancer recurrence in a mathematical
model of melanoma. PLoS Comput Biol 2009,
5:e1000362.
Sign up to receive new article alerts from BMC Genomics