Posts Tagged ‘#endcancer’

Real Time Coverage @BIOConvention #BIO2019: Keynote: Siddhartha Mukherjee, Oncologist and Pulitzer Author; June 4 9AM Philadelphia PA

Reporter: Stephen J. Williams, PhD. @StephenJWillia2


Hematologist and oncologist Siddhartha Mukherjee was born in New Delhi, India. He holds a BS in biology from Stanford University, a DPhil in immunology from Oxford University (where he was a Rhodes Scholar), and an MD from Harvard Medical School. He completed his internal medicine residency and an oncology fellowship at Massachusetts General Hospital. Dr. Murkherjee is an assistant professor of medicine at Columbia University Medical Center. He lives in Manhattan with his wife, artist Sarah Sze, and their two daughters. His Pulitzer Prize-winning book, The Emperor of All Maladies: A Biography of Cancer, tells the story of cancer from its first description in an ancient Egyptian scroll to the gleaming laboratories of modern research institutions. A three-part documentary series based on the book, directed by Barak Goodman and executive produced by Ken Burns, debuts on PBS stations March 30 and continues on March 31 and April 1. The film interweaves a sweeping historical narrative with intimate stories about contemporary patients and an investigation into the latest scientific breakthroughs. He has also written the award winning book “The Gene: An Intimate History” and is Founder of Vor Biopharma, who had just published on their CD33 engineered hematopoetic stem cells as an immunooncology therapy VOR33.

Hon. James C. Greenwood- former Congressional representative and Founder CEO of BIO: moderator

Greenwood: Never have the threats from DC to innovation in the biotech field been so great.  Focused on some great recent innovations and successes in gene therapy.  Although the cost high, father of two LMR retinopathy patients said if his sons had to go through a lifetime of constant care it would cost much more than the gene therapy from Spark cost.  Politicians need to realize that medicines that completely cure diseases are worth much more.  They should meet in the middle with respect to developing a new payer model that will not hurt innovation.

Dr. Mukherjee:  He go into oncology from a virology PhD because he liked to understand the human aspect

of disease.  As an oncologist he gets to interact more closely with patients.  The oncology horizon is always changing.  He likened his view of oncology and cancer as a pyramid with prevention the base, then early detection then therapy at top.

We haven’t found preventable human carcinogens, none that is highly proven causal

This will be the next challenge for cancer researchers, to figure out why we can’t identify these preventable carcinogens.





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Other Articles on this Open Access Journal on Interviews with Scientific Leaders Include:

Medical Scientific Discoveries for the 21st Century & Interviews with Scientific Leaders at – electronic Table of Contents

Jennifer Doudna and NPR science correspondent Joe Palca, several interviews

Practicing Oncology: Medscape Editor-in-Chief Eric J. Topol, MD interviews Siddhartha Mukherjee, MD, PhD

Eric Topol interviews Al Gore on Genomics and Privacy

Dr. Mercola Interviews Dr. Saul About Beta-Blockers

Volume Two: Medical Scientific Discoveries for the 21st Century & Interviews with Scientific Leaders



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A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information

Reporter: Stephen J. Williams, Ph.D.

Multifractal bioinformatics: A proposal to the nonlinear interpretation of genome

The following is an open access article by Pedro Moreno on a methodology to analyze genetic information across species and in particular, the evolutionary trends of complex genomes, by a nonlinear analytic approach utilizing fractal geometry, coined “Nonlinear Bioinformatics”.  This fractal approach stems from the complex nature of higher eukaryotic genomes including mosaicism, multiple interdispersed  genomic elements such as intronic regions, noncoding regions, and also mobile elements such as transposable elements.  Although seemingly random, there exists a repetitive nature of these elements. Such complexity of DNA regulation, structure and genomic variation is felt best understood by developing algorithms based on fractal analysis, which can best model the regionalized and repetitive variability and structure within complex genomes by elucidating the individual components which contributes to an overall complex structure rather than using a “linear” or “reductionist” approach looking at individual coding regions, which does not take into consideration the aforementioned factors leading to genetic complexity and diversity.

Indeed, many other attempts to describe the complexities of DNA as a fractal geometric pattern have been described.  In a paper by Carlo Cattani “Fractals and Hidden Symmetries in DNA“, Carlo uses fractal analysis to construct a simple geometric pattern of the influenza A virus by modeling the primary sequence of this viral DNA, namely the bases A,G,C, and T. The main conclusions that

fractal shapes and symmetries in DNA sequences and DNA walks have been shown and compared with random and deterministic complex series. DNA sequences are structured in such a way that there exists some fractal behavior which can be observed both on the correlation matrix and on the DNA walks. Wavelet analysis confirms by a symmetrical clustering of wavelet coefficients the existence of scale symmetries.

suggested that, at least, the viral influenza genome structure could be analyzed into its basic components by fractal geometry.
This approach has been used to model the complex nature of cancer as discussed in a 2011 Seminars in Oncology paper
Abstract: Cancer is a highly complex disease due to the disruption of tissue architecture. Thus, tissues, and not individual cells, are the proper level of observation for the study of carcinogenesis. This paradigm shift from a reductionist approach to a systems biology approach is long overdue. Indeed, cell phenotypes are emergent modes arising through collective non-linear interactions among different cellular and microenvironmental components, generally described by “phase space diagrams”, where stable states (attractors) are embedded into a landscape model. Within this framework, cell states and cell transitions are generally conceived as mainly specified by gene-regulatory networks. However, the system s dynamics is not reducible to the integrated functioning of the genome-proteome network alone; the epithelia-stroma interacting system must be taken into consideration in order to give a more comprehensive picture. Given that cell shape represents the spatial geometric configuration acquired as a result of the integrated set of cellular and environmental cues, we posit that fractal-shape parameters represent “omics descriptors of the epithelium-stroma system. Within this framework, function appears to follow form, and not the other way around.

As authors conclude

” Transitions from one phenotype to another are reminiscent of phase transitions observed in physical systems. The description of such transitions could be obtained by a set of morphological, quantitative parameters, like fractal measures. These parameters provide reliable information about system complexity. “

Gene expression also displays a fractal nature. In a Frontiers in Physiology paper by Mahboobeh Ghorbani, Edmond A. Jonckheere and Paul Bogdan* “Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Networks“,

the authors describe that gene expression networks display time series display fractal and long-range dependence characteristics.

Abstract: Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in Escherichia coliand Saccharomyces cerevisiae. Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.


Given that multitude of complex biomolecular networks and biomolecules can be described by fractal patterns, the development of bioinformatic algorithms  would enhance our understanding of the interdependence and cross funcitonality of these mutiple biological networks, particularly in disease and drug resistance.  The article below by Pedro Moreno describes the development of such bioinformatic algorithms.

Pedro A. Moreno
Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia

Eje temático: Ingeniería de sistemas / System engineering
Recibido: 19 de septiembre de 2012
Aceptado: 16 de diciembre de 2013




The first draft of the human genome (HG) sequence was published in 2001 by two competing consortia. Since then, several structural and functional characteristics for the HG organization have been revealed. Today, more than 2.000 HG have been sequenced and these findings are impacting strongly on the academy and public health. Despite all this, a major bottleneck, called the genome interpretation persists. That is, the lack of a theory that explains the complex puzzles of coding and non-coding features that compose the HG as a whole. Ten years after the HG sequenced, two recent studies, discussed in the multifractal formalism allow proposing a nonlinear theory that helps interpret the structural and functional variation of the genetic information of the genomes. The present review article discusses this new approach, called: “Multifractal bioinformatics”.

Keywords: Omics sciences, bioinformatics, human genome, multifractal analysis.

1. Introduction

Omic Sciences and Bioinformatics

In order to study the genomes, their life properties and the pathological consequences of impairment, the Human Genome Project (HGP) was created in 1990. Since then, about 500 Gpb (EMBL) represented in thousands of prokaryotic genomes and tens of different eukaryotic genomes have been sequenced (NCBI, 1000 Genomes, ENCODE). Today, Genomics is defined as the set of sciences and technologies dedicated to the comprehensive study of the structure, function and origin of genomes. Several types of genomic have arisen as a result of the expansion and implementation of genomics to the study of the Central Dogma of Molecular Biology (CDMB), Figure 1 (above). The catalog of different types of genomics uses the Latin suffix “-omic” meaning “set of” to mean the new massive approaches of the new omics sciences (Moreno et al, 2009). Given the large amount of genomic information available in the databases and the urgency of its actual interpretation, the balance has begun to lean heavily toward the requirements of bioinformatics infrastructure research laboratories Figure 1 (below).

The bioinformatics or Computational Biology is defined as the application of computer and information technology to the analysis of biological data (Mount, 2004). An interdisciplinary science that requires the use of computing, applied mathematics, statistics, computer science, artificial intelligence, biophysical information, biochemistry, genetics, and molecular biology. Bioinformatics was born from the need to understand the sequences of nucleotide or amino acid symbols that make up DNA and proteins, respectively. These analyzes are made possible by the development of powerful algorithms that predict and reveal an infinity of structural and functional features in genomic sequences, as gene location, discovery of homologies between macromolecules databases (Blast), algorithms for phylogenetic analysis, for the regulatory analysis or the prediction of protein folding, among others. This great development has created a multiplicity of approaches giving rise to new types of Bioinformatics, such as Multifractal Bioinformatics (MFB) that is proposed here.

1.1 Multifractal Bioinformatics and Theoretical Background

MFB is a proposal to analyze information content in genomes and their life properties in a non-linear way. This is part of a specialized sub-discipline called “nonlinear Bioinformatics”, which uses a number of related techniques for the study of nonlinearity (fractal geometry, Hurts exponents, power laws, wavelets, among others.) and applied to the study of biological problems ( For its application, we must take into account a detailed knowledge of the structure of the genome to be analyzed and an appropriate knowledge of the multifractal analysis.

1.2 From the Worm Genome toward Human Genome

To explore a complex genome such as the HG it is relevant to implement multifractal analysis (MFA) in a simpler genome in order to show its practical utility. For example, the genome of the small nematode Caenorhabditis elegans is an excellent model to learn many extrapolated lessons of complex organisms. Thus, if the MFA explains some of the structural properties in that genome it is expected that this same analysis reveals some similar properties in the HG.

The C. elegans nuclear genome is composed of about 100 Mbp, with six chromosomes distributed into five autosomes and one sex chromosome. The molecular structure of the genome is particularly homogeneous along with the chromosome sequences, due to the presence of several regular features, including large contents of genes and introns of similar sizes. The C. elegans genome has also a regional organization of the chromosomes, mainly because the majority of the repeated sequences are located in the chromosome arms, Figure 2 (left) (C. elegans Sequencing Consortium, 1998). Given these regular and irregular features, the MFA could be an appropriate approach to analyze such distributions.

Meanwhile, the HG sequencing revealed a surprising mosaicism in coding (genes) and noncoding (repetitive DNA) sequences, Figure 2 (right) (Venter et al., 2001). This structure of 6 Gbp is divided into 23 pairs of chromosomes (diploid cells) and these highly regionalized sequences introduce complex patterns of regularity and irregularity to understand the gene structure, the composition of sequences of repetitive DNA and its role in the study and application of life sciences. The coding regions of the genome are estimated at ~25,000 genes which constitute 1.4% of GH. These genes are involved in a giant sea of various types of non-coding sequences which compose 98.6% of HG (misnamed popularly as “junk DNA”). The non-coding regions are characterized by many types of repeated DNA sequences, where 10.6% consists of Alu sequences, a type of SINE (short and dispersed repeated elements) sequence and preferentially located towards the genes. LINES, MIR, MER, LTR, DNA transposons and introns are another type of non-coding sequences which form about 86% of the genome. Some of these sequences overlap with each other; as with CpG islands, which complicates the analysis of genomic landscape. This standard genomic landscape was recently clarified, the last studies show that 80.4% of HG is functional due to the discovery of more than five million “switches” that operate and regulate gene activity, re-evaluating the concept of “junk DNA”. (The ENCODE Project Consortium, 2012).

Given that all these genomic variations both in worm and human produce regionalized genomic landscapes it is proposed that Fractal Geometry (FG) would allow measuring how the genetic information content is fragmented. In this paper the methodology and the nonlinear descriptive models for each of these genomes will be reviewed.

1.3 The MFA and its Application to Genome Studies

Most problems in physics are implicitly non-linear in nature, generating phenomena such as chaos theory, a science that deals with certain types of (non-linear) but very sensitive dynamic systems to initial conditions, nonetheless of deterministic rigor, that is that their behavior can be completely determined by knowing initial conditions (Peitgen et al, 1992). In turn, the FG is an appropriate tool to study the chaotic dynamic systems (CDS). In other words, the FG and chaos are closely related because the space region toward which a chaotic orbit tends asymptotically has a fractal structure (strange attractors). Therefore, the FG allows studying the framework on which CDS are defined (Moon, 1992). And this is how it is expected for the genome structure and function to be organized.

The MFA is an extension of the FG and it is related to (Shannon) information theory, disciplines that have been very useful to study the information content over a sequence of symbols. Initially, Mandelbrot established the FG in the 80’s, as a geometry capable of measuring the irregularity of nature by calculating the fractal dimension (D), an exponent derived from a power law (Mandelbrot, 1982). The value of the D gives us a measure of the level of fragmentation or the information content for a complex phenomenon. That is because the D measures the scaling degree that the fragmented self-similarity of the system has. Thus, the FG looks for self-similar properties in structures and processes at different scales of resolution and these self-similarities are organized following scaling or power laws.

Sometimes, an exponent is not sufficient to characterize a complex phenomenon; so more exponents are required. The multifractal formalism allows this, and applies when many subgroups of fractals with different scalar properties with a large number of exponents or fractal dimensions coexist simultaneously. As a result, when a spectrum of multifractal singularity measurement is generated, the scaling behavior of the frequency of symbols of a sequence can be quantified (Vélez et al, 2010).

The MFA has been implemented to study the spatial heterogeneity of theoretical and experimental fractal patterns in different disciplines. In post-genomics times, the MFA was used to study multiple biological problems (Vélez et al, 2010). Nonetheless, very little attention has been given to the use of MFA to characterize the content of the structural genetic information of the genomes obtained from the images of the Chaos Representation Game (CRG). First studies at this level were made recently to the analysis of the C. elegans genome (Vélez et al, 2010) and human genomes (Moreno et al, 2011). The MFA methodology applied for the study of these genomes will be developed below.

2. Methodology

The Multifractal Formalism from the CGR

2.1 Data Acquisition and Molecular Parameters

Databases for the C. elegans and the 36.2 Hs_ refseq HG version were downloaded from the NCBI FTP server. Then, several strategies were designed to fragment the genomic DNA sequences of different length ranges. For example, the C. elegans genome was divided into 18 fragments, Figure 2 (left) and the human genome in 9,379 fragments. According to their annotation systems, the contents of molecular parameters of coding sequences (genes, exons and introns), noncoding sequences (repetitive DNA, Alu, LINES, MIR, MER, LTR, promoters, etc.) and coding/ non-coding DNA (TTAGGC, AAAAT, AAATT, TTTTC, TTTTT, CpG islands, etc.) are counted for each sequence.

2.2 Construction of the CGR 2.3 Fractal Measurement by the Box Counting Method

Subsequently, the CGR, a recursive algorithm (Jeffrey, 1990; Restrepo et al, 2009) is applied to each selected DNA sequence, Figure 3 (above, left) and from which an image is obtained, which is quantified by the box-counting algorithm. For example, in Figure 3 (above, left) a CGR image for a human DNA sequence of 80,000 bp in length is shown. Here, dark regions represent sub-quadrants with a high number of points (or nucleotides). Clear regions, sections with a low number of points. The calculation for the D for the Koch curve by the box-counting method is illustrated by a progression of changes in the grid size, and its Cartesian graph, Table 1

The CGR image for a given DNA sequence is quantified by a standard fractal analysis. A fractal is a fragmented geometric figure whose parts are an approximated copy at full scale, that is, the figure has self-similarity. The D is basically a scaling rule that the figure obeys. Generally, a power law is given by the following expression:

Where N(E) is the number of parts required for covering the figure when a scaling factor E is applied. The power law permits to calculate the fractal dimension as:

The D obtained by the box-counting algorithm covers the figure with disjoint boxes ɛ = 1/E and counts the number of boxes required. Figure 4 (above, left) shows the multifractal measure at momentum q=1.

2.4 Multifractal Measurement

When generalizing the box-counting algorithm for the multifractal case and according to the method of moments q, we obtain the equation (3) (Gutiérrez et al, 1998; Yu et al, 2001):

Where the Mi number of points falling in the i-th grid is determined and related to the total number Mand ɛ to box size. Thus, the MFA is used when multiple scaling rules are applied. Figure 4 (above, right) shows the calculation of the multifractal measures at different momentum q (partition function). Here, linear regressions must have a coefficient of determination equal or close to 1. From each linear regression D are obtained, which generate an spectrum of generalized fractal dimensions Dfor all q integers, Figure 4 (below, left). So, the multifractal spectrum is obtained as the limit:

The variation of the q integer allows emphasizing different regions and discriminating their fractal a high Dq is synonymous of the structure’s richness and the properties of these regions. Negative values emphasize the scarce regions; a high Dindicates a lot of structure and properties in these regions. In real world applications, the limit Dqreadily approximated from the data using a linear fitting: the transformation of the equation (3) yields:

Which shows that ln In(Mi )= for set q is a linear function in the ln(ɛ), Dq can therefore be evaluated as q the slope of a fixed relationship between In(Mi )= and (q-1) ln(ɛ). The methodologies and approaches for the method of box-counting and MFA are detailed in Moreno et al, 2000, Yu et al, 2001; Moreno, 2005. For a rigorous mathematical development of MFA from images consult Multifractal system, wikipedia.

2.5 Measurement of Information Content

Subsequently, from the spectrum of generalized dimensions Dq, the degree of multifractality ΔDq(MD) is calculated as the difference between the maximum and minimum values of : ΔD qq Dqmax – Dqmin (Ivanov et al, 1999). When qmaxqmin ΔDis high, the multifractal spectrum is rich in information and highly aperiodic, when ΔDq is small, the resulting dimension spectrum is poor in information and highly periodic. It is expected then, that the aperiodicity in the genome would be related to highly polymorphic genomic aperiodic structures and those periodic regions with highly repetitive and not very polymorphic genomic structures. The correlation exponent t(q) = (– 1)DqFigure 4 (below, right ) can also be obtained from the multifractal dimension Dq. The generalized dimension also provides significant specific information. D(q = 0) is equal to the Capacity dimension, which in this analysis is the size of the “box count”. D(q = 1) is equal to the Information dimension and D(q = 2) to the Correlation dimension. Based on these multifractal parameters, many of the structural genomic properties can be quantified, related, and interpreted.

2.6 Multifractal Parameters and Statistical and Discrimination Analyses

Once the multifractal parameters are calculated (D= (-20, 20), ΔDq, πq, etc.), correlations with the molecular parameters are sought. These relations are established by plotting the number of genome molecular parameters versus MD by discriminant analysis with Cartesian graphs in 2-D, Figure 5 (below, left) and 3-D and combining multifractal and molecular parameters. Finally, simple linear regression analysis, multivariate analysis, and analyses by ranges and clusterings are made to establish statistical significance.

3 Results and Discussion

3.1 Non-linear Descriptive Model for the C. elegans Genome

When analyzing the C. elegans genome with the multifractal formalism it revealed what symmetry and asymmetry on the genome nucleotide composition suggested. Thus, the multifractal scaling of the C. elegans genome is of interest because it indicates that the molecular structure of the chromosome may be organized as a system operating far from equilibrium following nonlinear laws (Ivanov et al, 1999; Burgos and Moreno-Tovar, 1996). This can be discussed from two points of view:

1) When comparing C. elegans chromosomes with each other, the X chromosome showed the lowest multifractality, Figure 5 (above). This means that the X chromosome is operating close to equilibrium, which results in an increased genetic instability. Thus, the instability of the X could selectively contribute to the molecular mechanism that determines sex (XX or X0) during meiosis. Thus, the X chromosome would be operating closer to equilibrium in order to maintain their particular sexual dimorphism.

2) When comparing different chromosome regions of the C. elegans genome, changes in multifractality were found in relation to the regional organization (at the center and arms) exhibited by the chromosomes, Figure 5 (below, left). These behaviors are associated with changes in the content of repetitive DNA, Figure 5 (below, right). The results indicated that the chromosome arms are even more complex than previously anticipated. Thus, TTAGGC telomere sequences would be operating far from equilibrium to protect the genetic information encoded by the entire chromosome.

All these biological arguments may explain why C. elegans genome is organized in a nonlinear way. These findings provide insight to quantify and understand the organization of the non-linear structure of the C. elegans genome, which may be extended to other genomes, including the HG (Vélez et al, 2010).

3.2 Nonlinear Descriptive Model for the Human Genome

Once the multifractal approach was validated in C. elegans genome, HG was analyzed exhaustively. This allowed us to propose a nonlinear model for the HG structure which will be discussed under three points of view.

1) It was found that the HG high multifractality depends strongly on the contents of Alu sequences and to a lesser extent on the content of CpG islands. These contents would be located primarily in highly aperiodic regions, thus taking the chromosome far from equilibrium and giving to it greater genetic stability, protection and attraction of mutations, Figure 6 (A-C). Thus, hundreds of regions in the HG may have high genetic stability and the most important genetic information of the HG, the genes, would be safeguarded from environmental fluctuations. Other repeated elements (LINES, MIR, MER, LTRs) showed no significant relationship,

Figure 6 (D). Consequently, the human multifractal map developed in Moreno et al, 2011 constitutes a good tool to identify those regions rich in genetic information and genomic stability. 2) The multifractal context seems to be a significant requirement for the structural and functional organization of thousands of genes and gene families. Thus, a high multifractal context (aperiodic) appears to be a “genomic attractor” for many genes (KOGs, KEEGs), Figure 6 (E) and some gene families, Figure 6 (F) are involved in genetic and deterministic processes, in order to maintain a deterministic regulation control in the genome, although most of HG sequences may be subject to a complex epigenetic control.

3) The classification of human chromosomes and chromosome regions analysis may have some medical implications (Moreno et al, 2002; Moreno et al, 2009). This means that the structure of low nonlinearity exhibited by some chromosomes (or chromosome regions) involve an environmental predisposition, as potential targets to undergo structural or numerical chromosomal alterations in Figure 6 (G). Additionally, sex chromosomes should have low multifractality to maintain sexual dimorphism and probably the X chromosome inactivation.

All these fractals and biological arguments could explain why Alu elements are shaping the HG in a nonlinearly manner (Moreno et al, 2011). Finally, the multifractal modeling of the HG serves as theoretical framework to examine new discoveries made by the ENCODE project and new approaches about human epigenomes. That is, the non-linear organization of HG might help to explain why it is expected that most of the GH is functional.

4. Conclusions

All these results show that the multifractal formalism is appropriate to quantify and evaluate genetic information contents in genomes and to relate it with the known molecular anatomy of the genome and some of the expected properties. Thus, the MFB allows interpreting in a logic manner the structural nature and variation of the genome.

The MFB allows understanding why a number of chromosomal diseases are likely to occur in the genome, thus opening a new perspective toward personalized medicine to study and interpret the GH and its diseases.

The entire genome contains nonlinear information organizing it and supposedly making it function, concluding that virtually 100% of HG is functional. Bioinformatics in general, is enriched with a novel approach (MFB) making it possible to quantify the genetic information content of any DNA sequence and their practical applications to different disciplines in biology, medicine and agriculture. This novel breakthrough in computational genomic analysis and diseases contributes to define Biology as a “hard” science.

MFB opens a door to develop a research program towards the establishment of an integrative discipline that contributes to “break” the code of human life. (http://pharmaceuticalintelligence. com/page/3/).

5. Acknowledgements

Thanks to the directives of the EISC, the Universidad del Valle and the School of Engineering for offering an academic, scientific and administrative space for conducting this research. Likewise, thanks to co authors (professors and students) who participated in the implementation of excerpts from some of the works cited here. Finally, thanks to Colciencias by the biotechnology project grant # 1103-12-16765.

6. References

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Vélez, P.E., Garreta, L.E., Martínez, E., Díaz, N., Amador, S., Gutiérrez, J.M., Tischer, I., & Moreno, P.A. (2010). The Caenorhabditis elegans genome: a multifractal analysis. Genet and Mol Res , 9, 949-965.         [ Links ]

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Other articles on Bioinformatics on this Open Access Journal include:

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CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics

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Announcement 11AM- 5PM: Live Conference Coverage  from Mediterranean Diet and Lifestyle: A Symposium on Diet and Human Health @S.H.R.O. and Temple University October 19, 2018

Reporter: Stephen J. Williams, Ph.D.


 The Sbarro Health Research Organization, in collaboration with the Consulate General of Italy in Philadelphia will sponsor a symposium on the Mediterranean Diet and Human Health on October 19, 2018 at Temple University in Philadelphia, PA.  This symposium will discuss recent finding concerning the health benefits derived from a Mediterranean-style diet discussed by the leaders in this field of research.

Mediterranean Diet

The description of the Mediterranean Diet stems from the nutritionist Ancel Keys, who in 1945, in the wake of the US Fifth Army, landed in Southern Italy, where he observed one of the highest concentrations of centenarians in the world. He also noticed that cardiovascular diseases, widespread in the USA, were less frequent there. In particular, among the Southern Italians, the prevalence of “wellness” diseases such as hypertension and diabetes mellitus, was particularly associated with fat consumption, suggesting that the main factor responsible for the observations was the type of diet traditionally consumed among people facing the Mediterranean Sea, which is low in animal fat, as opposed to the Anglo-Saxon diet. The link between serum cholesterol and coronary heart disease mortality was subsequently demonstrated by the Seven Countries Study. Later, the concept of Mediterranean Diet was extended to a diet rich in fruits, vegetables, legumes, whole grains, fish and olive oil as the main source of lipid, shared among people living in Spain, Greece, Southern Italy and other countries facing the Mediterranean basin …

Prof. Antonino De Lorenzo, MD, PhD.



The Symposium will be held at:

Biolife Science Building, Room 234

Temple University, 1900 North 12th street

Philadelphia, PA 19122


For further information, please contact:

Ms. Marinela Dedaj – Sbarro Institute,  Office #: 215-204-9521


11:00 Welcome

Prof. Antonio Giordano, MD, PhD.

Director and President of the Sbarro Health Research Organization, College of Science and Technology, Temple University



Fucsia Nissoli Fitzgerald

Deputy elected in the Foreign Circumscription – North and Central America Division


Consul General, Honorable Pier Attinio Forlano

General Consul of Italy in Philadelphia


11:30 The Impact of Environment and Life Style in Human Disease

Prof. Antonio Giordano MD, PhD.


12.00 The Italian Mediterranean Diet as a Model of Identity of a People with a Universal Good to Safeguard Health?

Prof. Antonino De Lorenzo, MD, PhD.

Director of the School of Specialization in Clinical Nutrition, University of Rome “Tor Vergata”


12:30 Environment and Health

Dr. Iris Maria Forte, PhD.

National Cancer Institute “Pascale” Foundation | IRCCS · Department of Research, Naples, Italy


13:00 Lunch


2:30 Mediterranean Diet, Intangible Heritage and Sustainable Tourism?

Prof. Fabio Parasecoli, PhD.

Nutrition and Food Department, New York University


3.00 Italy as a Case Study: Increasing Students’ Level of Awareness of the Historical, Cultural, Political and Culinary Significance of Food

Prof. Lisa Sasson

Nutrition and Food Department, New York University


3:30 Italian Migration and Global Diaspora

Dr. Vincenzo Milione, PhD

Director of Demographics Studies, Calandra Institute, City University of New York


4:00 Pasta Arte: New Model of Circular Agricultural Economy: When an Innovated Tradition Takes Care of You and of the Environment

Dr. Massimo Borrelli

CEO and Founder of Arte


4:15 Conclusions

Prof. Antonio Giordano, MD, PhD.


Coordinator of the Symposium, Dr. Alessandra Moia, PhD.


Prof. Antonio Giordano, MD, PhD.

Professor of Molecular Biology at Temple University in Philadelphia, PA where he is also Director of the Sbarro Institute for Cancer Research and Molecular Medicine. He is also Professor of Pathology at the University of Siena, Italy. He has published over 500 articles, received over 40 awards for his contributions to cancer research and is the holder of 17 patents.


Prof. Antonino De Lorenzo, MD, PhD.

Full Professor of Human Nutrition and Director of the Specialization School in Food Science at the University of Rome “Tor Vergata”. He is the Coordinator of the Specialization Schools in Food Science at the National University Council and Coordinator of the PhD. School of “Applied Medical-Surgical Sciences” Director of UOSD “Service of Clinical Nutrition, Parenteral Therapy and Anorexia”. He also serves as President of “Istituto Nazionale per la Dieta Mediterranea e la Nutrigenomica”.


Dr. Iris Maria Forte, PhD.

Iris Maria Forte is an oncology researcher of INT G. Pascale Foundation of Naples, Italy. She majored in Medical Biotechnology at the “Federico II” University of Naples, earned a PhD. in “Oncology and Genetics” at the University of Siena in 2012 and a Master of II level in “Environment and Cancer” in 2014. Iris Maria Forte has worked with Antonio Giordano’s group since 2008 and her research interests include both molecular and translational cancer research. She published 21 articles mostly focused in understanding the molecular basis of human cancer. She worked on different kinds of human solid tumors but her research principally focused on pleural mesothelioma and on cell cycle deregulation in cancer.


Prof. Fabio Parasecoli, PhD.

Professor in the Department of Nutrition and Food Studies. He has a Doctorate in Agricultural Sciences ( from Hohenheim University, Stuttgart (Germany), MA in Political Sciences from the Istituto Universitario Orientale, Naples (Italy), BA/MA in Modern Foreign Languages and Literature from the Università La Sapienza, Rome (Italy). His research explores the intersections among food, media, and politics. His most recent projects focus on Food Design and the synergies between Food Studies and design.


Prof. Lisa Sasson, MS

Dietetic Internship Director and a Clinical Associate Professor in the department. She has interests in dietetic education, weight and behavior management, and problem-based learning. She also is a private practice nutritionist with a focus on weight management. She serves as co-director of the Food, Nutrition and Culture program in Florence Italy, the New York State Dietetic Association and the Greater New York Dietetic Association (past president and treasurer).


Dr. Vincenzo Milione, PhD.

Director of Demographic Studies for The John D. Calandra Italian American Institute, Queens College, City University of New York. He has conducted social science research on Italian Americans. His research has included the educational and occupational achievements; Italian language studies at the elementary and secondary levels, high school non-completion rates; negative media portrayals of ethnic populations including migration studies and global diaspora.


Dr. Massimo Borrelli

Agricultural entrepreneur, Manager of the Italian Consortium for Biogas (CIB) and delegate for the Bioeconomy National Department of Confagricoltura. He developed A.R.T.E based on a model of agricultural circular economy, beginning and ending in the ground. He constructed the first biogas plant in the territory creating a new way to make agriculture, investing in research and development, experimentation and most of all, in people. In a few short years, he succeeded to close the production chain producing goods characterized by their high quality and usage of renewable energy.


Dr. Alessandra Moia, PhD.

Vice-President for Institutional and International Relations of the Istituto Nazionale per la Dieta Mediterranea e la Nutrigenomica (I.N.D.I.M.). Has managed relations with the academic institutions to increase awareness and develops projects for the diffusion of the Mediterranean Diet. She served as Director of Finance for the National Institute of Nutrition, for the Ministry of Agriculture and Forestry.


About the Sbarro Health Research Organization

The Sbarro Health Research Organization (SHRO) is non-profit charity committed to funding excellence in basic genetic research to cure and diagnose cancer, cardiovascular diseases, diabetes and other chronic illnesses and to foster the training of young doctors in a spirit of professionalism and humanism. To learn more about the SHRO please visit

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Dr. Foti Recognized With Honorary Member Award from Oncology Nursing Society

Margaret Foti, PhD, MD (hc), chief executive officer (CEO) of the American Association for Cancer Research (AACR), was honored this morning during the opening ceremony of the 41st Annual Congress of the Oncology Nursing Society (ONS) in San Antonio, TX, with the Honorary Member Award for her unwavering dedication to improving cancer care and her commitment to the prevention and cure of all cancers.
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Collaboration With Bristol Myers Squib Led to Successful Launch of Ono Pharmaceutical’s Cancer Immune Therapy (Opdivio®)

Reporter: Stephen J. Williams, Ph.D.

Below are excerpts and a great story on the origins on Opdivo and its early marketing troubles and eventual success when Bristol Myers partnered with a small Japanese pharma, Ono Pharmaceuticals.

As seen in Biospace News 

Ten years ago, representatives from Japan’s Ono Pharmaceutical Co. went from hospital to hospital, attempting to convince doctors to test a new product under development: drugs that helped the body’s immune system fight cancer. Nobody would listen.

Immuno-therapy was another fad, they were told. The treatment probably offered no bigger benefit than eating mushrooms to fight cancer, one critic opined. Another said he’d shave his head if it worked.


Read at Bloomberg



From Bloomberg

This $150,000 Cancer Treatment Saved a Pharma Company


By  Natasha Khan natashakhanhk

Ten years ago, representatives from Japan’s Ono Pharmaceutical Co. went from hospital to hospital, attempting to convince doctors to test a new product under development: drugs that helped the body’s immune system fight cancer. Nobody would listen.

Immuno-therapy was another fad, they were told. The treatment probably offered no bigger benefit than eating mushrooms to fight cancer, one critic opined. Another said he’d shave his head if it worked.

Ono’s Chief Executive Officer Gyo Sagara says he received plenty of apologies when Opdivo, the drug the Japanese company worked on with Bristol-Myers Squibb Co., got the green light from regulators. The drug’s approval in Japan 20 months ago was the first worldwide in a new class of cancer treatments called PD-1 inhibitors.

It is among a string of therapies coming to market in the immuno-oncology category – medicines that help the body combat cancer rather than directly attacking the cancer cells themselves. The influential Science journal called cancer immunotherapy the “breakthrough of the year” in 2013, and the biggest global pharmaceutical companies are rushing into the field.

“They found the treasure of the century,” said Fumiyoshi Sakai, a health-care analyst with Credit Suisse, who boosted his target price for the stock to 25,000 yen in mid February. Ono’s shares closed at an all-time high of 22,605 yen on Thursday after climbing more than 70 percent over the past year.

The drug is pumping fresh life into Ono, which for years has battled slumping sales, patent expirations and rising competition from cheaper generics. Analysts now forecast that the Japanese company — among the biggest makers of specialty pharmaceuticals in Asia with a market cap of about $23 billion — will more than double annual revenue to about $3 billion by fiscal year end March 2018.

For the average U.S. patient, Opdivo costs about $12,500 a month, or $150,000 for a year of therapy. Bloomberg Intelligence says that consensus analyst estimates suggest that by 2020, Bristol-Myers and Ono’s Opdivo could have global sales of $9.5 billion and Merck & Co.’s Keytruda $5.1 billion.


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Reporter: Stephen J. Williams, Ph.D.

  • Emily Whitehead was 5 years old when came to UPENN and CHOP (2010) with unresponsive leukemia
  • she was healthy up to day she was diagnosed and went to Hershey Medical Center and recieved diagnosis of CLL (came in with 21 bruises, symptom of leukemia)
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1:45PM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston


1:45 p.m. Panel Discussion – Oncology


There has been a remarkable transformation in our understanding of the molecular genetic basis of cancer and its treatment during the past decade or so. In depth genetic and genomic analysis of cancers has revealed that each cancer type can be sub-classified into many groups based on the genetic profiles and this information can be used to develop new targeted therapies and treatment options for cancer patients. This panel will explore the technologies that are facilitating our understanding of cancer, and how this information is being used in novel approaches for clinical development and treatment.


Opening Speaker & Moderator:

Lynda Chin, M.D.
Department Chair, Department of Genomic Medicine
MD Anderson Cancer Center     @MDAnderson   #endcancer

  • Who pays for personalized medicine?
  • potential of Big data, analytics, Expert systems, so not each MD needs to see all cases, Profile disease to get same treatment
  • business model: IP, Discovery, sharing, ownership — yet accelerate therapy
  • security of healthcare data
  • segmentation of patient population
  • management of data and tracking innovations
  • platforms to be shared for innovations
  • study to be longitudinal,
  • How do we reconcile course of disease with personalized therapy
  • phenotyping the disease vs a Patient in wait for cure/treatment


Roy Herbst, M.D., Ph.D.    @DrRoyHerbstYale

Ensign Professor of Medicine and Professor of Pharmacology;
Chief of Medical Oncology, Yale Cancer Center and Smilow Cancer Hospital     @YaleCancer

Development new drugs to match patient, disease and drug – finding the right patient for the right Clinical Trial

  • match patient to drugs
  • partnerships: out of 100 screened patients, 10 had the gene, 5 were able to attend the trial — without the biomarker — all 100 patients would participate for the WRONG drug for them (except the 5)
  • patients wants to participate in trials next to home NOT to have to travel — now it is in the protocol
  • Annotated Databases – clinical Trial informed consent – adaptive design of Clinical Trial vs protocol
  • even Academic MD can’t read the reports on Genomics
  • patients are treated in the community — more training to MDs
  • Five companies collaborating – comparison of 6 drugs in the same class
  • if drug exist and you have the patient — you must apply personalized therapy


Lincoln Nadauld, M.D., Ph.D.
Director, Cancer Genomics, Huntsman Intermountain Cancer Clinic @lnadauld @intermountain

  • @Stanford, all patients get Tumor profiles Genomic results, interpretation – deliver personalized therapy
  • Outcomes from Genomics based therapies
  • Is survival superior
  • Targeted treatment – Health economic impact is cost lower or not for same outcome???
  • genomic profiling of tumors: Genomic information changes outcome – adverse events lower
  • Path ways and personalized medicine based on Genomics — integration not yet been worked out

Question by Moderator: Data Management

  • Platform development, clinical knowledge system,
  • build consortium of institutions to share big data – identify all patients with same profile





See more at




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