In silico multicellular systems biology and minimal genomes

Anakoinosis: Correcting Aberrant Homeostasis of Cancer Tissue-Going Beyond Apoptosis Induction

The current approach to systemic therapy for metastatic cancer is aimed predominantly at inducing apoptosis of cancer cells by blocking tumor-promoting signaling pathways or by eradicating cell compartments within the tumor. In contrast, a systems view of therapy primarily considers the communication protocols that exist at multiple levels within the tumor complex, and the role of key regulators of such systems. Such regulators may have far-reaching influence on tumor response to therapy and therefore patient survival. This implies that neoplasia may be considered as a cell non-autonomous disease. The multi-scale activity ranges from intra-tumor cell compartments, to the tumor, to the tumor-harboring organ to the organism. In contrast to molecularly targeted therapies, a systems approach that identifies the complex communications networks driving tumor growth offers the prospect of disrupting or "normalizing" such aberrant communicative behaviors and therefore attenuating tumor growth. Communicative reprogramming, a treatment strategy referred to as anakoinosis, requires novel therapeutic instruments, so-called master modifiers to deliver concerted tumor growth-attenuating action. The diversity of biological outcomes following pro-anakoinotic tumor therapy, such as differentiation, trans-differentiation, control of tumor-associated inflammation, etc. demonstrates that long-term tumor control may occur in multiple forms, inducing even continuous complete remission. Accordingly, pro-anakoinotic therapies dramatically extend the repertoire for achieving tumor control and may activate apoptosis pathways for controlling resistant metastatic tumor disease and hematologic neoplasia.

Systems biology - the broader perspective

Systems biology has two general aims: a narrow one, which is to discover how complex networks of proteins work, and a broader one, which is to integrate the molecular and network data with the generation and function of organism phenotypes. Doing all this involves complex methodologies, but underpinning the subject are more general conceptual problems about upwards and downwards causality, complexity and information storage, and their solutions provide the constraints within which these methodologies can be used. This essay considers these general aspects and the particular role of protein networks; their functional outputs are often the processes driving phenotypic change and physiological function—networks are, in a sense, the units of systems biology much as proteins are for molecular biology. It goes on to argue that the natural language for systems-biological descriptions of biological phenomena is the mathematical graph (a set of connected facts of the general form <state 1> [process] <state 2> (e.g., <membrane-bound delta> [activates] <notch pathway>). Such graphs not only integrate events at different levels but emphasize the distributed nature of control as well as displaying a great deal of data. The implications and successes of these ideas for physiology, pharmacology, development and evolution are briefly considered. The paper concludes with some challenges for the future.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Automatic inference of multicellular regulatory networks using informative priors

To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

The influence of traditional herbal formulas on cytokine activity

Many of the botanical "immunomodulators", a class of herbal medicines widely recognized in traditional medical systems such as Chinese Medicine (TCM) and Ayurvedic Medicine, alter immune function and may offer clinically relevant therapeutics or leads to therapeutics. Many of these traditional remedies are prepared from combinations of medicinal plants which may influence numerous molecular pathways. These effects may differ from the sum of effects from the individual plants and therefore, research demonstrating the effects of the formula is crucial for insights into the effects of traditional remedies. In this review we surveyed the primary literature for research that focused on combinations of medicinal plants and effects on cytokine activity. The results demonstrate that many extracts of herb mixtures have effects on at least one cytokine. The most commonly studies cytokines were IL-4, IL-6, IL-10, TNF and IFN-γ. The majority of the formulas researched derived from TCM. The following formulas had activity on at least three cytokines; Chizukit N, CKBM, Daeganghwal-tang, Food Allergy Formula, Gamcho-Sasim-Tang, Hachimi-jio-gan, Herbkines, Hochuekki, Immune System Formula, Jeo-Dang-Tang, Juzen-taiho-to, Kakkon-to, Kan jang, Mao-Bushi-Saishin-to, MSSM-002, Ninjin-youei-to, PG201, Protec, Qing-huo-bai-du-yin, Qingfu Guanjieshu, Sambucol Active Defense, Seng-fu-tang, Shin-Xiao-Xiang, Tien Hsien, Thuja formula, Unkei-to, Vigconic, Wheeze-relief-formula, Xia-Bai-San, Yangyuk-Sanhwa-Tang, Yi-fey Ruenn-hou, and Yuldahansotang. Of the western based combinations, formulas with Echinacea spp. were common and showed multiple activities. Numerous formulas demonstrated activity on both gene and protein expression. The research demonstrates that the reviewed botanical formulas modulate cytokine activity, although the bulk of the research is in vitro. Therapeutic success using these formulas may be partially due to their effects on cytokines. Further study of phytotherapy on cytokine related diseases/syndromes is necessary.

The promise of a virtual lab in drug discovery

To date, the life sciences 'omics' revolution has not lived up to the expectation of boosting the drug discovery process. The major obstacle is dealing with the volume and diversity of data generated. An enhanced-science (e-science) approach based on remote collaboration, reuse of data and methods, and supported by a virtual laboratory (VL) environment promises to get the drug discovery process afloat. The creation, use and preservation of information in formalized knowledge spaces is essential to the e-science approach. VLs include Grid computation and data communication as well as generic and domain-specific tools and methods for information management, knowledge extraction and data analysis. Problem-solving environments (PSEs) are the domain-specific experimental environments of VLs. Thus, VL-PSEs can support virtual organizations, based on the changing partnerships characteristic of successful drug discovery enterprises.

Systems-ADME/Tox: resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.

Meeting report: the future and limits of systems biology

Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. And yet, the paradigms that underlie the field have not seen a similar expansiveness. Instead, most of these paradigms have been carried over from other fields like engineering, physics, and mathematics. As a result, a small set of concepts dominate the field. The traditional biologist is seen by many as outmoded and tolerated only as a source of data. In this view, the biologist's ideas may even be considered conceptually and theoretically irrelevant. In this Perspective, we take a critical look at some of the paradigms of systems biology and question whether the biologist's ideas, methods, and theories have really become outmoded. We see the future of systems biology as a tight coupling of in vivo and in vitro methods for bioengineering with in silico multicellular modeling and simulation.

Genome semantics, in silico multicellular systems and the Central Dogma

Genomes with their complexity and size present what appears to be an impossible challenge. Scientists speak in terms of decades or even centuries before we will understand how genomes and their hosts the cell and the city of cells that make up the multicellular context function. We believe that there will be surprisingly quick progress made in our understanding of genomes. The key is to stop taking the Central Dogma as the only direction in which genome research can scale the semantics of genomes. Instead a top-down approach coupled with a bottom-up approach may snare the unwieldy beast and make sense of genomes. The method we propose is to take in silico biology seriously. By developing in silico models of genomes cells and multicellular systems, we position ourselves to develop a theory of meaning for artificial genomes. Then using that develop a natural semantics of genomes.