Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer (original) (raw)

Biophysical Approach to Understand Life and Cancer

Background: From the perspective of biology, the characteristics of life can be categorized as cellular organizations, homeostasis, metabolism, growth, reproduction and heredity, response to stimuli and adaptation to the environment. From the perspective of physics, living organisms are highly ordered, complex, thermodynamically open systems; they are in non-equilibrium phase. According to the second law of thermodynamics, every system in the universe is going to the disorder spontaneously. Maximum entropy signifies a thermodynamic equilibrium which means the death. Today, cancer is a major global health problem and despite the explosive development of our knowledge about the carcinogenesis the "war on cancer" has not yet been won. Aims: To examine life and cancer together based on current biophysical approach and to shed light on new paradigms. Materials & Methods: A systematic literature search for publications on a thermodynamic approach to understand life and cancer was conducted in PubMed without language restrictions. The reference lists of identified studies were also used as additional knowledge. Results: Living organisms exchange entropy and they gain information from the external environment. Information can be stored as memory. Life is a category of emergence like art composed from correct modules. On the contrary, cancer is the emergence of a disease formed by incorrect modules. Cancer is a chaotic disease at least for a limited period and cannot arise from healthy functional tissue units. Chaos of the lower level may be associated with the entropy increase of upper level. Conclusions: Biologic chaos control is possible. Life is governed by energy; it is conserved according to the first law of thermodynamics and, according to the second law, it is This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Systems biology and cancer: Promises and perils

Progress in Biophysics and Molecular Biology, 2011

Systems biology uses systems of mathematical rules and formulas to study complex biological phenomena. In cancer research there are three distinct threads in systems biology research: modeling biology or biophysics with the goal of establishing plausibility or obtaining insights, modeling based on statistics, bioinformatics, and reverse engineering with the goal of better characterizing the system, and modeling with the goal of clinical predictions. Using illustrative examples we discuss these threads in the context of cancer research.

A systems biology view of cancer

Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, 2009

In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new -omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B-cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.

The role of coherence in a systems view of cancer development

Theoretical biology forum, 2012

Theories of cancer origin are going through a paradigm shift, opening cancer research to new hypotheses. Accumulating evidence from the tissue microenvironment research, from bioenergetics, epigenetics, systems biology and thermodynamics tends to converge in characterising cancer as essentially a genetically non-deterministic disease. Instead, it is characterised by progressive disorganisation at a variety of organisational levels, from the genome and metabolic networks, to tissue integrity. As biological self-organisation is fuelled by the continuous supply of energy and infdrmation, these represent systemic roots of cancer origin, when compromised. The coherence of molecular dynamics has been recognised as an organising principle behind the long-range coordination of biological processes which can explain the remarkable efficiency of biological systems. Recent methodological advances have enabled the rapid accumulation of experimental evidence pointing to coherence as indeed playi...

Cancer as a complex adaptive system

Medical hypotheses, 1996

The second leading cause of death in the USA is cancer. Institutions worldwide are devoting significant resources to the treatment of cancer, and the elucidation of the disease pathway. While great progress has been made in understanding and treating carcinogenesis, many aspects of the disease remain intractable. Throughout the history of science many other disciplines -astronomy, particle physics, etc. -have been advanced when the fundamental ideas governing the discipline were redefined. These redefinitions are often termed 'paradigm shifts'. The new sciences of chaos theory and complexity have led to paradigm shifts in many unrelated disciplines such as economics, meteorology and seismology. Our current understanding of carcinogenesis has resulted from a conventional view of the disease process. In this perception, the mutation of a gene, or several genes, leads to cancer. Applying the formalism of chaos theory and complexity to carcinogenesis, however, leads to a different perception of the disease. If we look closer, cancer can be viewed as a complex adaptive system. Redefining our perception of cancer may lead to a deeper understanding of the disease, and possibly result in novel methods of therapeutic intervention.

Cancer control through principles of systems science, complexity, and chaos theory: A model

International journal of medical sciences, 2007

Cancer is a significant medical and societal problem. This reality arises from the fact that an exponential and an unrestricted cellular growth destabilizes human body as a system. From this perspective, cancer is a manifestation of a system-in-failing.A model of normal and abnormal cell cycle oscillations has been developed incorporating systems science, complexity, and chaos theories. Using this model, cancer expresses a failing subsystem and is characterized by a positive exponential growth taking place in the outer edge of chaos. The overall survival of human body as a system is threatened. This model suggests, however, that cancer's exponential cellular growth and disorganized complexity could be controlled through the process of induction of differentiation of cancer stem cells into cells of low and basic functionality.This concept would imply reorientation of current treatment principles from cellular killing (cyto-toxic therapies) to cellular retraining (cyto-education).

Complexity in cancer biology: is systems biology the answer

Complex phenotypes emerge from the interactions of thousands of macromolecules that are organized in multimolecular complexes and interacting functional modules. In turn, modules form functional networks in health and disease. Omics approaches collect data on changes for all genes and proteins and statistical analysis attempts to uncover the functional modules that perform the functions that characterize higher levels of biological organization. Systems biology attempts to transcend the study of individual genes/proteins and to integrate them into higher order information. Cancer cells exhibit defective genetic and epigenetic networks formed by altered complexes and network modules arising in different parts of tumor tissues that sustain autonomous cell behavior which ultimately lead tumor growth. We suggest that an understanding of tumor behavior must address not only molecular but also, and more importantly, tumor cell heterogeneity, by considering cancer tissue genetic and epigenetic networks, by characterizing changes in the types, composition, and interactions of complexes and networks in the different parts of tumor tissues, and by identifying critical hubs that connect them in time and space.

A Systems Biology Approach to Cancer: Fractals, Attractors, and Nonlinear Dynamics

OMICS: A Journal of Integrative Biology, 2011

Cancer begins to be recognized as a highly complex disease, and advanced knowledge of the carcinogenic process claims to be acquired by means of supragenomic strategies. Experimental data evidence that tumor emerges from disruption of tissue architecture, and it is therefore consequential that the tissue level should be considered the proper level of observation for carcinogenic studies. This paradigm shift imposes to move from a reductionistic to a systems biology approach. Indeed, cell phenotypes are emergent modes arising through collective nonlinear interactions among different cellular and microenvironmental components, generally described by a phase space diagram, 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 the gene-regulatory network. However, the system's dynamics cannot be reduced to only the integrated functioning of the genomeproteome network, and the cell-stroma interacting system must be taken into consideration in order to give a more reliable picture. As cell form represents the spatial geometric configuration shaped by an integrated set of cellular and environmental cues participating in biological functions control, it is conceivable that fractal-shape parameters could be considered as ''omics'' descriptors of the cell-stroma system. Within this framework it seems that function follows form, and not the other way around. Paradigm Instability A central feature of the prevailing interpretative paradigm for carcinogenesis is the underlying notion that cancer originates at the cellular level of organization. This approach roots in the work of Theodor Boveri and Ernest Tyzzer, who first used the term ''somatic mutation'' connecting it with cancer (Boveri, 1914; Wunderlich, 2007). The somatic mutation theory (SMT) posits that cancer is related in a deterministic fashion to a point-mutation of a proto-oncogene and results from a progressive accumulation of mutations in somatic cells that lead to the ''cancer phenotype,'' characterized by ''specific,'' both molecular and functional (gene expression, metabolic phenotype), features (Fearon and Vogelstein, 1990; Hahn et al., 1999). The acquired transformation is thought to confer some kind of ''selective advantage'' and is then transmitted to cell progeny. Tumor progression is therefore explained as a sort of microevolutionary Darwinian process leading to different and more malignant phenotypes (Michor er al., 2004).

Emergent properties of a non-physiological computational model of tumour growth

While there have been enormous advances in our understanding of the genetic drivers and molecular pathways involved in cancer in recent decades, there also remain key areas of dispute with respect to fundamental theories of cancer. The accumulation of vast new datasets from genomics and other fields, in addition to detailed descriptions of molecular pathways, cloud the issues and lead to ever greater complexity. One strategy in dealing with such complexity is to develop thought experiments which selectively focus on different levels of abstraction in order to build models to replicate salient features of the system and therefore to build hypotheses which reflect on the real system. NEATG is a simple non-physiological tumour growth model which displays emergent behaviours that correspond to a number of clinically relevant phenomena including tumour growth, intra-tumour heterogeneity, growth arrest and accelerated repopulation following cytotoxic insult. Analysis of model data suggest...