Xiaomei Zhu

Quantitative implementation of the endogenous molecular–cellular network hypothesis in hepatocellular carcinoma

A quantitative hypothesis for cancer genesis and progression—the endogenous molecular–cellular network hypothesis, intended to include both genetic and epigenetic causes of cancer—has been proposed recently. Using this hypothesis, here we address the molecular basis for maintaining normal liver and hepatocellular carcinoma (HCC), and the potential strategy to cure or relieve HCC. First, we elaborate the basic assumptions of the hypothesis and establish a core working network of HCC according to the hypothesis. Second, we quantify the working network by a nonlinear dynamical system. We show that the working network reproduces the main known features of normal liver and HCC at both the modular and molecular levels. Lastly, the validated working network reveals that (i) specific positive feedback loops are responsible for the maintenance of normal liver and HCC; (ii) inhibiting proliferation and inflammation-related positive feedback loops and simultaneously inducing a liver-specific positive feedback loop is predicated as a potential strategy to cure or relieve HCC; and (iii) the genesis and regression of HCC are asymmetric. In light of the characteristic properties of the nonlinear dynamical system, we demonstrate that positive feedback loops must exist as a simple and general molecular basis for the maintenance of heritable phenotypes, such as normal liver and HCC, and regulating the positive feedback loops directly or indirectly provides potential strategies to cure or relieve HCC.

From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis

Experimental evidences and theoretical analyses have amply suggested that in cancer genesis and progression genetic information is very important but not the whole. Nevertheless, “cancer as a disease of the genome” is still currently the dominant doctrine. With such a background and based on the fundamental properties of biological systems, a new endogenous molecular-cellular network theory for cancer was recently proposed by us. Similar proposals were also made by others. The new theory attempts to incorporate both genetic and environmental effects into one single framework, with the possibility to give a quantitative and dynamical description. It is asserted that the complex regulatory machinery behind biological processes may be modeled by a nonlinear stochastic dynamical system similar to a noise perturbed Morse-Smale system. Both qualitative and quantitative descriptions may be obtained. The dynamical variables are specified by a set of endogenous molecular-cellular agents and the structure of the dynamical system by the interactions among those biological agents. Here we review this theory from a pedagogical angle which emphasizes the role of modularization, hierarchy and autonomous regulation. We discuss how the core set of assumptions is exemplified in detail in one of the simple, important and well studied model organisms, Phage lambda. With this concrete and quantitative example in hand, we show that the application of the hypothesized theory in human cancer, such as hepatocellular carcinoma (HCC), is plausible, and that it may provide a set of new insights on understanding cancer genesis and progression, and on strategies for cancer prevention, cure, and care.

Endogenous molecular-cellular hierarchical modeling of prostate carcinogenesis uncovers robust structure

We explored endogenous molecular-cellular network hypothesis for prostate cancer by constructing relevant endogenous interaction network model and analyzing its dynamical properties. Molecular regulations involved in cell proliferation, apoptosis, differentiation and metabolism are included in a hierarchical mathematical modeling scheme. This dynamical network organizes into multiple robust functional states, including physiological and pathological ones. Some states have characteristics of cancer: elevated metabolic and immune activities, high concentration of growth factors and different proliferative, apoptotic and adhesive behaviors. The molecular profile of calculated cancer state agrees with existing experiments. The modeling results have additional predictions which may be validated by further experiment: 1) Prostate supports both stem cell like and liver style proliferation; 2) While prostate supports multiple cell types, including basal, luminal and endocrine cell type differentiated from its stem cell, luminal cell is most likely to be transformed malignantly into androgen independent type cancer; 3) Retinoic acid pathway and C/EBPα are possible therapeutic targets.

Deregulation of Bone Forming Cells in Bone Diseases and Anabolic Effects of Strontium-Containing Agents and Biomaterials

Age-related bone loss and osteoporosis are associated with bone remodeling changes that are featured with decreased trabecular and periosteal bone formation relative to bone resorption. Current anticatabolic therapies focusing on the inhibition of bone resorption may not be sufficient in the prevention or reversal of age-related bone deterioration and there is a big need in promoting osteoblastogenesis and bone formation. Enhanced understanding of the network formed by key signaling pathways and molecules regulating bone forming cells in health and diseases has therefore become highly significant. The successful development of agonist/antagonist of the PTH and Wnt signaling pathways are profits of the understanding of these key pathways. As the core component of an approved antiosteoporosis agent, strontium takes its effect on osteoblasts at multilevel through multiple pathways, representing a good example in revealing and exploring anabolic mechanisms. The recognition of strontium effects on bone has led to its expected application in a variety of biomaterial scaffolds used in tissue engineering strategies aiming at bone repairing and regeneration. While summarizing the recent progress in these respects, this review also proposes the new approaches such as systems biology in order to reveal new insights in the pathology of osteoporosis as well as possible discovery of new therapies.

Efficiency, Robustness, and Stochasticity of Gene Regulatory Networks in Systems Biology: λ Switch as a Working Example

Phage λ is one of the most studied biological models in modern molecular biology. Over the past 50 years, quantitative experimental knowledge on this biological model has been accumulated at all levels: physics, chemistry, genomics, proteomics, functions, and more. All of its components are known in great detail. The theoretical task has been to integrate its components to make the organism work quantitatively and in a harmonic manner. This tests our biological understanding, and would lay a solid foundation for further explorations and applications, which is an obvious goal of systems biology. One of the outstanding challenges in doing this has been the so-called stability puzzle of the λ switch; the biologically observed robustness and the difficulty in mathematical reconstruction based on known experimental values. In this chapter, we review the recent theoretical and experimental efforts on tackling this problem. An emphasis is put on the minimum quantitative modeling, where a successful numerical agreement between experiments and modeling has been achieved. A novel method, tentatively named stochastic dynamical structure analysis, emerged from such study, and it is also discussed within a broad modeling perspective.

From molecular interaction to acute promyelocytic leukemia: Calculating leukemogenesis and remission from endogenous molecular-cellular network

Acute promyelocytic leukemia (APL) remains the best example of a malignancy that can be cured clinically by differentiation therapy. We demonstrate that APL may emerge from a dynamical endogenous molecular-cellular network obtained from normal, non-cancerous molecular interactions such as signal transduction and translational regulation under physiological conditions. This unifying framework, which reproduces APL, normal progenitor, and differentiated granulocytic phenotypes as different robust states from the network dynamics, has the advantage to study transition between these states, i.e. critical drivers for leukemogenesis and targets for differentiation. The simulation results quantitatively reproduce microarray profiles of NB4 and HL60 cell lines in response to treatment and normal neutrophil differentiation, and lead to new findings such as biomarkers for APL and additional molecular targets for arsenic trioxide therapy. The modeling shows APL and normal states mutually suppress each other, both in “wiring” and in dynamical cooperation. Leukemogenesis and recovery under treatment may be a consequence of spontaneous or induced transitions between robust states, through “passes” or “dragging” by drug effects. Our approach rationalizes leukemic complexity and constructs a platform towards extending differentiation therapy by performing “dry” molecular biology experiments.

Key network approach reveals new insight into Alzheimer's disease.

Alzheimers disease (AD) is a severe neurodegenerative disorder without curative treatment. Extensive data on pathological molecular processes have been accumulated over the last years. These data combined allows a systems biology approach to identify key regulatory elements of AD and to establish a model descriptive of the disease process which can be used for the development of therapeutic agents. In this study, the authors propose a closed network that uses a set of nodes (amyloid beta, tau, beta-secretase, glutamate, cyclin-dependent kinase 5, phosphoinositide 3-kinase and hypoxia-induced factor 1 alpha) as key elements of importance to the pathogenesis of AD. The proposed network, in total 39 nodes, is able to become a novel tool capable of providing new insights into AD, such as feedback loops. Further, it highlights interconnections between pathways and identifies their combination for therapy of AD.

Endogenous network states predict gain or loss of functions for genetic mutations in hepatocellular carcinoma

Cancers have been typically characterized by genetic mutations. Patterns of such mutations have traditionally been analysed by posteriori statistical association approaches. One may ponder the possibility of a priori determination of any mutation regularity. Here by exploring biological processes implied in a mechanistic theory recently developed (the endogenous molecular–cellular network theory), we found that the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. With hepatocellular carcinoma (HCC) as an example, we found that the normal hepatocyte and cancerous hepatocyte can be represented by robust stable states of one single endogenous network. These stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable predictions on accumulated and preferred mutation spectra in normal tissue. The validation of predicted cancer state mutation patterns demonstrates the usefulness and potential of a causal dynamical framework to understand and predict genetic mutations in cancer.

Kinetic model of metabolic network for xiamenmycin biosynthetic optimisation

Xiamenmycins, a series of prenylated benzopyran compounds with anti-fibrotic bioactivities, were isolated from a mangrove-derived Streptomyces xiamenensis. To fulfil the requirements of pharmaceutical investigations, a high production of xiamenmycin is needed. In this study, the authors present a kinetic metabolic model to evaluate fluxes in an engineered Streptomyces lividans with xiamenmycin-oriented genetic modification based on generic enzymatic rate equations and stability constraints. Lyapunov function was used for a viability optimisation. From their kinetic model, the flux distributions for the engineered S. lividans fed on glucose and glycerol as carbon sources were calculated. They found that if the bacterium can utilise glucose simultaneously with glycerol, xiamenmycin production can be enhanced by 40% theoretically, while maintaining the same growth rate. Glycerol may increase the flux for phosphoenolpyruvate synthesis without interfering citric acid cycle. They therefore believe this study demonstrates a possible new direction for bioengineering of S. lividans.

Core level regulatory network of osteoblast as molecular mechanism for osteoporosis and treatment

To develop and evaluate the long-term prophylactic treatment for chronic diseases such as osteoporosis requires a clear view of mechanism at the molecular and systems level. While molecular signaling pathway studies for osteoporosis are extensive, a unifying mechanism is missing. In this work, we provide experimental and systems-biology evidences that a tightly connected top-level regulatory network may exist, which governs the normal and osteoporotic phenotypes of osteoblast. Specifically, we constructed a hub-like interaction network from well-documented cross-talks among estrogens, glucocorticoids, retinoic acids, peroxisome proliferator-activated receptor, vitamin D receptor and calcium-signaling pathways. The network was verified with transmission electron microscopy and gene expression profiling for bone tissues of ovariectomized (OVX) rats before and after strontium gluconate (GluSr) treatment. Based on both the network structure and the experimental data, the dynamical modeling predicts calcium and glucocorticoids signaling pathways as targets for GluSr treatment. Modeling results further reveal that in the context of missing estrogen signaling, the GluSr treated state may be an outcome that is closest to the healthy state.