Modeling Innovation

This research service analyzes the global trends in the systems biology with a focus on the current and prospective applications of this platform. This study brings together expert opinions around the core technological and growth opportunities for systems biology in the 2020 horizon, and the wide range of relevant disciplines converging to systems medicine for diagnostic and therapeutic areas robustly utilizing high throughput –omics technologies in computational and systems biology approaches.

Executive Summary

The spectacular advances in the post-genomic era have come along in the recent years with a significant emergence of new disciplines and lines of research. Propelled by the movement of biomedical sciences from a reductionist approach to a system paradigm, systems biology has positioned as a source of novel solutions for a broad spectrum of application fields, ranging from life sciences services to clinical routine, by intending to understand pathophysiological mechanisms in a more integrated approach leveraging the even increasing amounts of -omics available data.

Strong focus is being evidenced on converting major technological advances in novel solutions for relevant unmet needs. At this point, systems medicine appears as a newly emerging scientific area, concentrated in identifying key challenges for the translation of systems biology to both life sciences research and clinical routine. Importantly, systems medicine is strongly aligned to the new paradigm of personalized medicine.

The present report brings together expert opinions around the core technological and growth opportunities for systems biology in the 2020 horizon, and the wide range of relevant disciplines converging to systems medicine for diagnostic and therapeutic areas robustly utilizing high throughput –omics technologies in computational and systems biology approaches. The current and future scenarios for funding and investing opportunities, as well as, the most remarkable national and regional initiatives across the different technology innovation clusters are also investigated.

Research Methodology

Primary Research
• Engineers
• Technical Architects
• Research Heads
• Strategic Decision Makers
• Technology Policy Heads

Secondary Research
• Technology Journals
• Periodicals
• Market Research Reports
• Technology Policy Information Sites
• Internal Databases
• Thought Leader Briefings

Step 1: To provide a thorough analysis of each topic, Technical Insights’ analysts perform a review of patents to become familiar with the major developers and commercial participants and their processes.
Step 2: Building on the patent search, the analysts review abstracts to identify key scientific and technical papers that provide insights into key industry participants and the technical processes, on which they work.
Step 3: The analysts then create a detailed questionnaire with content created to address the research objectives of the study, which functions as a guide during the interview process. While the analysts use structured questionnaires to guarantee coverage of all the desired issues, they also conduct interviews in a conversational style. This approach results in a more thorough exchange of views with the respondents, and offers greater insight into the relevant issues than more structured interviews may provide.
Step 4: The analysts conduct primary research with key industry participants and technology developers to obtain the required content. Interviews are completed with sources located throughout the world, in universities, national laboratories, governmental and regulatory bodies, trade associations, and end-user companies, among other key organizations. Our analysts contact the major commercial participants to find out about the advantages and disadvantages of processes and the drivers and challenges behind technologies and applications. Our analysts talk to the principal developers, researchers, engineers, business developers, analysts, strategic planners, and marketing experts, among other professionals.
Step 5: The project management and research team reviews and analyzes the research data that are gathered and adds its recommendations to the draft of the final study. Having conducted both published studies and custom proprietary research covering many types of new and emerging technology activities as well as worldwide industry analysis, the management and research team adds its perspective and experience to provide an accurate, timely analysis. The analysts then prepare written final research services for each project and sometimes present key findings in analyst briefings to clients.

Core Technology Applications

antiviral therapy / multidrug resistance
• Viral infection system to achieve a comprehensive understanding of molecular pathogen-host interactions (infectome) and, thus develop novel diagnostic and therapeutic technology platforms.
• Addressing multidrug resistance issues through in silico approaches, leveraging the huge amounts of omics data.

cancer research / drug screening platforms
• Cancer diagnostics and therapeutics can be improved through the in silico, in vitro, in vivo synergistic approach.
• Cell-based technology platforms also tend to merge with micro- and nanotechnologies.
• Nanobio innovations strongly promote the integration of different applications into advanced technology platforms for life sciences research.

cardiovascular development / cellular imaging
• System based approaches at several scales for cardiovascular development represents one of the most targeted fields of research.
• Cellular imaging represents one of the most promising tools for early diagnostics and personalized medicine.
• Enhanced imaging technologies by using quantum dots and nanobiomarkers accelerate the pace of future medicine.

Core Growth Opportunities

bioinformatics / systems medicine
• Integrating new technologies into industry value chain at several levels
• Performing ultrahigh-throughput and parallel analysis at the single-molecule level
• Accelerating the pace of drug discovery using high-throughput techniques
• Enhancing target selection, lead identification, preclinical tests, clinical trials, chemical synthesis, formulations studies and product management

oncology / next generation biomarkers
• Evaluating large number of chemical
• structures against hundreds of
• biological targets through novel
• miniaturization and massively parallel
• experimentation methods
• Integrating different functional
• units for sampling, sample
• pretreatment, sample transport,
• biochemical reactions, analyte
• separation, product isolation and
• analysis in a continuous flow
• manner

high-sensitivity / precision monitoring and surveillance
• Developing nano/microfluidic devices with high analytical throughput rates
• Detecting DNA translocation events through solid-state nanoarray devices as a proof-of-concept that illustrate ultrahigh sensitivity and specificity
• Interfacing micro/nano and macro-technologies through novel, ease- handling, robust, multiplex, and cost effective nanobiochips

Evolution and Prospections

During the past decade systems biology (SB) has become a major force. The advent in molecular biology and omics-based technologies, have allowed emerging novel approaches overcoming certain limitations in the existing models.

While decades ago, the paradigm for predicting phenotype has focused exclusively on single gene defects, the extraordinarily powerful approach of SB has been a major contributor to the in-depth understanding of the function of individual genes and proteins, as well as, their expression levels and silencing, and more importantly, their correlation with health and disease condition.

Systems biology represents a conceptual framework for the analysis of complex biological systems, which derive from interactions among many distinct components in varying contexts. The properties exhibited by the system, including their nonlinear dynamics and emergent behavior, are difficult to be inferred from the isolated study of their components. Systems biology focuses on developing mathematical models and computational techniques that facilitate the generation of hypotheses and help to design new experiments. Then, by iteration between theory and experiment the uncertainties are reduced.

Crucial effect, on that note, have the quantity and quality of data required for develop such methodological approaches. For that reason, the synergy among new technologies and the cross-pollination of different disciplines and research collaborations, acquire especial attention. Regarding technology convergence with life sciences, the principal impact on systems biology relies on the impressive volumes of data obtained via high-throughput screening (HTS) methods for DNA sequencing, in which nanotech, more precisely nanobiochips, mainly used to stretch DNA strands, plays the key role. Similarly, nanofluidic devices are strategically targeted to be used as disposable devices, specially applied to point-of-care (PoC) diagnostics, also requiring a computational model for control and regulation cycles.

Worldwide efforts have converged to the creation and access to large model and organisms database, containing a great number of validated and curated models. Standardization and open programming languages have been crucial in database development. Concerning the technology transfer from academia and research organizations to industry, conjunct efforts involving governmental, industry, and university participation are essential. New opportunities arise from open innovation programs around systems biology and bioinformatics.

Technology Trends and Roadmap

1. Biology Codification
Gene expression/disease correlation
Multi-scale modeling (micro/macro, stochastic/deterministic, hierarchical /architectural integration)
Logical (bio) schemas (ontologies)
Programs (FORET) - Equations (e.g. Lotka-Volterra)
Learning systems
Parallel/multi-core computing
Artificial intelligence

2. Cell/Tissue Modeling
Pathways modeling/simulation for drug response evaluation in drug discovery
Biomarkers development/cancer research In silico, in vitro, in vivo models for clinical trials
Commercial software provide code, applications and components on shared-source basis
Open source developments
Multidisciplinary convergence
Data curation

3. Virtual Patients
Personalized medicine
Virtual populations for clinical trials
Disease prediction and risk management
Smart vaccination
Epigenome-based tech platforms
In silico early diagnostic tests
Pathway regulation tools
Tissue regeneration smart design
Synergy with synthetic biology
Biological parts self-assembly

Table Of Contents

Table of Contents

Executive Summary 3
Core Technology Opportunities 7
Core Growth Opportunities 8
Sectorial Analysis 10
Technology Trends and Roadmap 11
Government Grants and Stimulus Funding for RandD 16
Assessment of Investor Ecosystems 48
Analyst Insights and Recommendations 86
Appendix 95
The Frost and Sullivan Story 111

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