Publications
Systems Biology
“The Potential of Biologic Network Models in Understanding the Etiopathogenesis of Ovarian Cancer”
Iya Khalil & Tom Neyarapally (GNS) and Molly Brewer & Carolyn Runowicz (UConn).
Gynecologic Oncology
(Epub 11/18/2009, Pub 1/12/2010).
Ovarian cancer is one of the most common gynecologic malignancies and is the 5th leading cause of cancer mortality in women in the United States. Understanding the biology and molecular pathogenesis of ovarian epithelial tumors is key to developing improved prognostic indicators and effective therapies. The selection of ovarian serous carcinomas as one of the cancer types for extensive genomic and proteomic characterization of The Cancer Genome Atlas (TCGA) project offers an important opportunity to extend our knowledge of ovarian cancer. The data portal includes molecular characterization, high throughput sequencing, and clinical data. Models to determine which of these genes act as "key drivers" of ovarian carcinogenesis and which are innocent "passengers" are needed. Standard statistical approaches often fail to differentiate between these driver and passenger genes, given that the correlation between sets of genes or genes and endpoints alone does not establish causality. As contrasted to basic correlations analyses, biological network models offer the ability to resolve causality by elucidating the directional linkages between genetics, molecular characterizations of the system, and clinical measures. This article describes the use of a novel, supercomputer-driven approach named REFS to learn network models directly from the TGCA ovarian cancer data set and simulate these models to learn the "key drivers" of ovarian carcinogenesis. The model can be validated by out-of-sample testing, and may provide a powerful new tool for ovarian cancer research. ( PubMed Citation )
“Customized Care 2020: How Medical Sequencing and Network Biology Will Enable Personalized Medicine”
Mark Boguski, Ramy Arnaout & Colin Hill.
Faculty of 1000 Biology
(2009).
Applications of next-generation nucleic acid sequencing technologies will lead to the development of precision diagnostics that will, in turn, be a major technology enabler of precision medicine. Terabyte-scale, multidimensional data sets derived using these technologies will be used to reverse engineer the specific disease networks that underlie individual patients' conditions. Modeling and simulation of these networks in the presence of virtual drugs, and combinations of drugs, will identify the most efficacious therapy for precision medicine and customized care. In coming years the practice of medicine will routinely employ network biology analytics supported by high-performance supercomputing.
“Achieving confidence in mechanism for drug discovery and
development”,
Zach Pitluk and Iya G.Khalil,
Drug Discovery Today
(2007).
An invited paper by Drs. Khalil and Pitluk that establishes GNS as a thought leader in applying inference technologies to improve drug discovery. The paper examines crucial gaps in the knowledge formation process, consequences of the gaps and, how inference technologies can enable dramatic improvements in the drug discovery processes from specific projects to portfolio planning. ( PubMed citation )
“Birhythmicity, Trirhythmicity and Chaos in
Bursting Calcium Oscillations”
,
T.
Haberitcher, M. Marhl, R. Heinrich, Biophysical Chemistry 90
17-30 (2001).
Various patterns of oscillatory behavior of a mathematical model for calcium dynamics are analyzed, with emphasis on multirhythmic and chaotic bursting modes. The former appear through saddle-node-of-periodics bifurcations, the latter through two different routes to chaos: period doubling cascades and intermittency.
“An integrated approach for inference and
mechanistic modeling for advancing drug
development”
,
Sergej V. Aksenov, Bruce Church, Anjali Dhiman, Anna Georgieva,
Ramesh Sarangapani, Gabriel Helmlinger, Iya G. Khalil, FEBS
Letters (2005).
Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models.
“Trading ‘wet-work’ for
network”
V. Periwal and Z. Szallasi,
Nature Biotechnology, 20:10 345-346 (2002).
This paper points out the dire need for communicating between the disparate communities of scientists involved in systems biology in a manner that is both precise and jargon-free. It suggests guidelines for published papers in systems biology: experimental work with explicit uncertainties, theoretical work with clearly explained assumptions and approximations, and novel computationally generated hypotheses verified by experiment.
“From Topology to Dynamics in Biochemical
Networks”
,
J. J. Fox and C. C. Hill, Chaos,
11:4 809-815 (2001)
.
We study models of biochemical networks using Boolean networks with the number of inputs K to each element given by one of three distributions: delta function, Poisson, and power law (scale-free). We show that finite, scale-free networks are more ordered than the other two distributions, suggesting that the topology of scale-free biochemical networks may provide a source of order in living cells.
“Diagrammatic Notation and Computational Grammar
for Gene Networks”
,
R. Maimon and S. Browning
(published in the proceedings of the The Second International
Conference on Systems Biology,
This paper introduces a concise, modular, and mathematically precise visual notation for the representation of biochemical networks. This language can be used to create an unambiguous diagram of a given network such that the diagram can be directly translated into a mathematical simulation of the system.
“Towards the Development of a Minimal Cell Model
by Generalization of a Model of Escherichia coli: Use of
Dimensionless Rate Parameters”
S.Browning,
M. Shuler, Biotechnology and Bioengineering, 76 187-192
(2001).
This paper establishes the concept of a minimal cell model, based on an earlier model of Escherichia coli, and describes its potential uses. Dimensionless rate parameters are used to generalize the rate parameters specific to the E. coli model, and experimental data from a variety of bacteria are used to justify this scaling.
“Complex Calcium Oscillations and the Role of
Mitochondria and Cytosolic Proteins”
,
M. Marhl, T. Haberitcher, M. Brumen, R. Heinrich, BioSystems
57 75-86 (2000).
A new possible mechanism for complex calcium oscillations based on the interplay between three calcium stores in the cell is developed: the endoplasmic reticulum, mitochondria and cytosolic proteins. Depending on the permeability of the ER channels and on the kinetic properties of calcium binding to the cytosolic proteins, different patterns of complex calcium oscillations, such as multirhythmicity, bursting and chaos appear.
“Nonlinear Dynamics of Gene and Neural Networks”
L. Glass, C. Hill, T. Mestl
Proceedings of Indian National Science Academy, “Nonlinear Phenomena in Physical and Biological Systems”, (eds. S. K. Malik and N. Pradhan) (1999).
“Transition to Chaos in Models of Genetic
Networks”
C.C. Hill, B.K. Sawhill, S. Kauffman,
and L. Glass, Proceedings of the XV Sitges Euroconference
“Statistical Mechanics of Biocomplexity” (eds. M
Vilar and M. Rubi) Springer-Verlag (1999).
“Ordered and Disordered Dynamics in Random
Networks“
L. Glass and C. Hill, Europhys.
Letts. 41 599-604 (1998).
Cancer
“A systems biology dynamical model of mammalian G1
cell cycle progression”
,
Thomas Haberichter,
Britta Mädge, Renee A Christopher, Naohisa Yoshioka, Anjali Dhiman,
Robert Miller, Rina Gendelman, Sergej V Aksenov, Iya G Khalil and
Steven F Dowdy, Molecular Systems Biology 3:84 (Feb.
2007).
We developed a mathematical model of G1 progression using physiological expression and activity profiles from synchronized cells exposed to constant growth factors and included a metabolically responsive, activating modifier of cyclin E:Cdk2. Our mathematical model accurately simulates G1 progression, recapitulates observations from targeted gene deletion studies and serves as a foundation for development of therapeutics targeting G1 cell-cycle progression.
“Individualised cancer therapeutics: dream or
reality?”
,
Neil Senzer, Yuqiao Shen, Colin
Hill & John Nemunaitis, Expert Opinion on Therapeutic Targets.
9 (6): 1189-1201 (Dec. 2005).
This review focuses on
the concept of designing individualised therapeutics based on
genomic and proteomic profile of malignant tissue. Genetic and
epigenetic perturbations in signal pathways drive cancer growth,
survival, invasion and metastatic spread. The burgeoning evidence
which supports the concept that each patientís cancer has a unique
complement of pathogenic genetic and molecular derangements is
reviewed. Such evidence supports the strategy of individualised
selection of a therapeutic complex from a menu of targeting options
that best complements the specific oncomolecular profile of the
‘tumourñhost’ system.
“Systems biology for cancer”
Khalil, I G, Hill, C, Current Opinion in Oncology. 17(1):44-48
(2005).
Significant insight can be gained into complex biologic mechanisms of cancer via a combined computational and experimental systems biology approach. This review highlights some of the major systems biology efforts that were applied to cancer in the past year.
“Data-Driven Computer Simulation of Human Cancer
Cell”
,
R. Christopher,A. Dhiman, J. Fox, R.
Gendelman, T. Haberitcher, D. Kagle, G. Spizz, I. G. Khalil and
C. Hill, Ann. N.Y. Acad. Sci. 1020: 132-153
(2004).
Using the Diagrammatic Cell Language TM, Gene Network Sciences (GNS) has created a network model of interconnected signal transduction pathways and gene expression networks that control human cell proliferation and apoptosis. Using the cell simulation, GNS tested the efficacy of various drug targets and performed validation experiments to test computer simulation predictions.