Systems biology: A disruptive biopharmaceutical research paradigm

Available online at www.sciencedirect.com

Technological Forecasting & Social Change 74 (2007) 1643 – 1660

Systems biology: A disruptive biopharmaceutical research paradigm Minna Allarakhia a,⁎, Anthony Wensley b,1 a

b

University of Waterloo, Management Sciences, 200 University Avenue, Waterloo, ON, Canada N2L 3G1 University of Toronto, School of Management, 3359 Mississauga Road, Mississauga, ON, Canada L5L 1C6 Received 4 August 2005; accepted 28 July 2006

Abstract Since the completion of the Human Genome Project a new biological paradigm has emerged, namely systems biology. This paradigm is advancing the view that biology is essentially an information science with information operating on multiple hierarchical levels and in complex networks. A new hierarchical framework for biological knowledge is being constructed to understand the relationships between the various levels of information. Although the goal of finding new medicinal entities is central to drug discovery, the search itself has been dramatically altered in the post Human Genome era. It is our view that systems biology is a disruptive biopharmaceutical research paradigm. Biopharmaceutical knowledge production processes, knowledge dissemination processes, and even knowledge appropriation mechanisms are rapidly evolving to maximize value creation during drug discovery and development. A knowledge framework is used in this paper for conceptualizing and enabling the efficient management of these new complexities in systems biology. Fundamentally important to medical progress is ensuring that multiple innovators can equitably exploit the technological opportunities presented by systems biology. We evaluate the role of academia, government, and industry in preserving these technological opportunities. © 2006 Elsevier Inc. All rights reserved. Keywords: Systems Biology; Research Paradigms; Knowledge Management; Intellectual Property; Research Policy

⁎ Corresponding author. Fax: +1 416 352 5302. E-mail addresses: [email protected] (M. Allarakhia), [email protected] (A. Wensley). 1 Tel.: +1 905 828 5318; fax: +1 905 569 4302. 0040-1625/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2006.07.012

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1. Introduction A new biological paradigm, systems biology, has emerged with the completion of the Human Genome Project. Systems biology aims to develop a systems-level understanding of biological processes. The Human Genome Project has advanced the view that biological information operates on multiple hierarchical levels and is processed in complex networks [1]. It is no longer sufficient to develop a biological model and perform analyses at only one or two levels of biological information. What is required is an understanding of the behavior of molecules within a system and as a function of the characteristics of the system. Biological systems will be understood within a framework of knowledge that is built up from the molecular level to the organism level [2]. System biology knowledge is complex and derives from a variety of scientific and technical disciplines. Progress in genomics, proteomics, computational sciences, and measurement technologies will enable the understanding of biological systems. New analysis techniques, experimental methods, measurement technologies, and software tools will be developed to assist in the experimental decomposition and then in silico (computational) composition of systems [3]. The various interactions between genes and proteins, the mechanisms by which such interactions modulate the structures of a cell, the behavior of systems in normal and disease states, the modulation of systems to minimize malfunctions, and the ability to redesign systems, must be understood to obtain deep insight into biological systems. In this paradigm, the biologist can no longer work in isolation. Collaboration between various disciplines is required as the drug discovery process evolves to incorporate this new paradigm. Networks of collaboration that are supported by information and communication technologies will enable researchers from a variety of disciplines and laboratories to generate and validate systems biology knowledge. The structure and rules associated with systems biology-based networks will support the global production and dissemination of knowledge. It is our view that the systems biology paradigm is a disruptive biopharmaceutical research paradigm. Although the goal of finding new medicinal entities is central to drug discovery, the search itself has been dramatically altered in the post Human Genome era. The molecular level of analysis, the computational nature of discovery research, and the global scale of research support our claim that systems biology is a disruptive paradigm. From a knowledge perspective, biopharmaceutical knowledge production processes, knowledge dissemination processes, and even knowledge appropriation mechanisms are rapidly evolving with the emergence of this paradigm. We develop a knowledge framework in this paper to understand the disruptive nature of the systems biology paradigm from each of these perspectives. We begin with a discussion of the reorganization of early systems-based discovery research to enable cooperative knowledge production. We then discuss how in systems biology, knowledge production and dissemination processes must take into account that knowledge is varied in form and function. Knowledge must be produced in a manner that is efficient and cohesive. Furthermore, knowledge must be codified through the use of standards to meet the needs of researchers functioning across multiple scientific and technical disciplines. Once produced, each scientific or technical field will have its own conventions regarding knowledge appropriation. These conventions may not be identical or stable and it cannot be assumed that the conventions of all members will converge simply through the creation of a cross-disciplinary organizational structure [4]. Measures and signals of success in knowledge generation activities will instead determine the value placed on this knowledge by the various disciplines. Therefore, we discuss that increasingly, the characteristics associated with systems biology knowledge will determine the timing of knowledge appropriation. As accessibility may be

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required for multiple researchers to progress further downstream in the development of diagnostics and therapeutics, the decision to appropriate knowledge must consider the shadow of the future. Systems biology is likely to become the dominant paradigm in biology. Central to the development of medically viable products is ensuring accessibility to systems-based knowledge for multiple researchers who can pursue tomorrow's technological opportunities. Academia, government, and industry will all play a role in shaping policies that will enable cooperative knowledge production and the broad dissemination of systems-based knowledge. 2. An iterative paradigm linking research and computation Dye chemistry, synthetic chemistry, pharmacology, and biochemistry have collectively influenced the development of the pharmaceutical industry. In the chemical paradigm for drug discovery and development, vital body processes are described in chemical terms and diseases are described as measurable deviations from normal chemical processes. From this perspective, drug intervention is merely an attempt to normalize this dynamic equilibrium through the use of chemical substances [5]. Historically, chemical compounds were isolated from living organisms or could be synthesized. In addition to methodologically assured means of production, compounds that were isolated or synthesized needed to be tested for effectiveness. The measurement and classification of the effectiveness of drugs became part of the domain of pharmacology. Pharmacology evolved from a focus on physiology and an understanding of normal body functions into the correction of malfunctioning processes through the use of pharmaceuticals. Animal models exhibiting disease symptoms were used to test the effectiveness and safety of new compounds [5]. Hence, medicine oriented itself toward experimentation and observation, paying less attention to abstract theories and speculation [5]. Molecular biology and genomics have introduced the information paradigm. The genome consists of all the directions for the development and function for an organism. Genetic changes lead to functional loss or alteration of these instructions. Disease results from such genetic changes. Knowledge of the genome will allow for the description and quantification of disease and susceptibility to disease as informational deficits or errors [5]. The treatment of disease from this perspective involves the replacement of information that has been lost or the correction of information that is erroneous in the form of DNA or protein. Knowledge of the structure, function, and interdependencies of these genes and proteins will enable the industry to find more therapeutically relevant targets for drug discovery and then development. With the evolution of the pharmaceutical industry into the biopharmaceutical industry and the shift from a chemistry-based paradigm to an information-based paradigm, discovery research is increasingly being viewed as part of systems biology. Systems biology does not focus on individual genes and proteins one at a time, but focuses on the behavior and relationships of all elements, in a particular biological system, from a functional perspective. These biological systems are fundamentally composed of information: genes, their encoded products, and the regulatory components controlling the expression of these genes [6]. Targets that function across diseases, playing a central role in these diseases, will be chosen to develop drugs that either augment or suppress the associated biological systems, enabling for better disease intervention and blockbuster status on the market. Blockbuster drugs will not simply target one system, but will eventually target multiple systems at a common intervention point. Consequently, drug discovery research has shifted from a large-scale, chemical perspective to a smallscale molecular perspective in the search for new medicines. It is this scale of analysis that warrants the

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inclusion of systems biology and its supporting technology under the domain of nanotechnology. The technologies that will be used in drug discovery research will also focus on both controlling and now redesigning biological systems at a molecular level, with the aim of intervening in disease onset and progression. Although advances in quantitative experimental approaches will continue to be used as in the chemical paradigm, insight into the functioning of biological systems will now be supported by a combination of experimental and computational approaches [3]. In this sense, biological knowledge will become linked to computational knowledge [2,3,6]. 2.1. Computational biology-revolution in the wet laboratory Computational biology involves knowledge discovery, data mining to uncover the patterns from experimental data, and simulation-based analyses that will test hypotheses with in silico experiments. Systems biological is an integrated process of computational modeling or “dry experiments”, system analysis, technology development for experiments, and quantitative “wet” experiments [2,3,6] (Fig. 1). Biological and physiological knowledge enables the development of virtual models of gene networks, biochemical networks, cells, and organs. Computational “dry” experiments test system models and related hypotheses. Data is integrated and displayed graphically and system responses are modeled mathematically to predict the structure and behavior of informational pathways in systems. Experimental techniques are developed and “wet” experiments are used to verify or reject hypotheses from computational experiments. Once sufficient information has been gained about a system, this experimental cycle can be applied to drug discovery research targeting the system [2,3]. Global observations made during discovery research are matched to model predictions or hypotheses in an iterative manner, leading to new patient models, predictions, and methods of patient experimentation [6]. Computational experiments will identify and virtually screen lead compounds. Successful leads will be synthesized and then tested via in vitro (outside of living organisms) and in vivo (inside living organisms) experiments as well as clinical studies [3,7]. The systems biology research cycle and drug discovery and

Fig. 1. Linking systems biology research with drug discovery research. adopted from Kitano, H. Computational systems biology, Nature 420(6912), 207, 2002.

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development cycle are linked to each other through feedback processes that update biological, physiological, system, and patient model information. In silico experiments of drug systems and the screening of lead candidates will play a central role in upstream biopharmaceutical research, with the joint objectives of reducing costs and increasing the success of a drug during human clinical trial testing. 2.2. A cross-disciplinary approach Given the broad scope of systems biology, collective effort is required from multiple research arenas including: molecular biology, cell biology, physiology, mathematics, physics and chemistry, computer science, electrical, mechanical, and biological engineering. Life sciences research has long been dominated by a culture of independent laboratories organized around single principal investigators. However, the need in systems biology for diverse skills and the complexity of the experimental technologies require the formation of interdisciplinary research teams [2,8]. Teams of biologists, engineers, and computational scientists from the public and private sectors will increasingly collaborate to handle the iterative and multi-dimensional aspects of systems biology [8]. Although strategic alliances such as joint ventures and mergers and acquisitions are used to gain access to both tacit and codified molecular knowledge, these alliances tend to be associated with downstream knowledge—knowledge that is used for the purpose of medical application development [9,10]. Upstream knowledge that is far removed from commercial application is typically developed through research-based partnerships between universities, biotechnology, and pharmaceutical companies [11–13]. Furthermore, the era of the Human Genome has highlighted the need for partnerships that are broad and not only cross institutional boundaries, but also cross regional boundaries. The breadth of upstream research to be conducted to ensure successful downstream drug development, particularly in a decade marked by shrinking pipelines and blockbuster drug patent expirations, has reinforced the need for knowledge-based networks. Based on a preliminary analysis of 778 public–private alliances (in the Recombinant Capital Database) formed over the period from 1980 to 2005, it appears that the frequency of alliance formation (including collaborative alliances, research-based alliances, and license-only alliances) between academic or nonprofit organizations and biotechnology or pharmaceutical firms generally increases, with a noticeable increase over the 2001–2005 period. Furthermore, when each alliance is associated with a knowledge paradigm, i.e., chemical, biological, or information, the focus of research across periods favors the biological paradigm, with a gradual increase in the focus towards the information paradigm over the 2001– 2005 period (Table 1). It is anticipated that given the complexity and scale of research required to find relevant drug targets, such alliances will continue to dominate between academic institutions and private firms in the systems (information-based) paradigm [9–13]. Networks of collaboration allow multiple institutions and systems biology researchers to collaborate despite their geographic distance [14]. The creation of a virtual knowledge environment will enable scientists to make new connections between information from diverse sources, and to support educational, collaborative, and community-building efforts. Knowledge production and dissemination in this paradigm will require the development of common data standards for representing complex biological information, and the establishment of efficient communication and knowledge sharing mechanisms across disciplines and geographies [14]. Knowledge networks enable multiple researchers to pool assets, know-how, and expertise for the purpose of knowledge generation, knowledge validation, and new wealth creation [15,16]. However, the ability to generate new wealth depends on the capacity of researchers to learn from other researchers in a

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Table 1 Analyzing discovery and early phase public–private alliances across knowledge paradigms Paradigm Date 1980–1985 1986–1990 1991–1995 1996–2000 2001–2005 Subtotals

Chemistry paradigm

Biological paradigm

Information paradigm

3 12 46 43 93 197

16 22 77 60 112 287

4 11 32 34 102 183

• Chemistry paradigm: Focus of alliance activities on chemical knowledge and on the traditional aspects of drug discovery, including small molecule pharmaceuticals. • Biological paradigm: Focus of alliance activities on biological knowledge, including physiology and molecular biology as well as large molecule biologics. • Information paradigm: Focus of alliance activities on genomics-based or systems-based information development or tool development.

knowledge network or alliance. The structure of the network mechanisms used to enable knowledge transfer and transparency with respect to knowledge production and dissemination, are critical to ensure that collective learning occurs [17].

3. Disrupting the traditional notion of drug discovery research Genes, proteins, biological systems, and their associated patents are strategic knowledge-based assets [12,18–20]. The importance of these knowledge-based assets, combined with the increased complexity of molecular and systems-based knowledge, is encouraging the view that drug discovery and development should be viewed centrally from a knowledge-based perspective [1,16]. Our knowledge framework provides a nuanced understanding of the drug discovery and development process in the post Human Genome Era and analyzes the process by stage: the knowledge production stage, the knowledge dissemination stage, and the knowledge appropriation stage. At each stage, we discuss the major transitions occurring as the paradigm evolves, as well as the management changes required to enable discovery research. Policies stemming from these management changes are then discussed with the perspective that academia, government, and industry each have a role in policy decisions and implementation (Fig. 2). Knowledge identification is the process of locating and recognizing knowledge that is relevant for value creation during drug discovery and/or development. Knowledge can be identified through routine searches, networking with other members participating within the domain, or through accidental, tangential activity. Once identified, new knowledge will be created through research and development activities. Diffusion of the knowledge may involve the sale of and purchase of rights to proprietary knowledge or the transformation of tacit knowledge into codified knowledge. Once produced, knowledge will have to be validated in terms of accuracy and applicability. Other users can easily test knowledge cooperatively shared and placed into the public domain. Knowledge that is subject to challenge and validation in the scientific community lends better credibility to the creator(s) of the knowledge and in future downstream application. The scientific community gives more credence to claims

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Fig. 2. A Knowledge perspective of the systems biology paradigm. P = proposition. T = transition.

backed by publicly disclosed data [21]. In arenas marked by high levels of uncertainty particularly during early discovery research, widespread dissemination of discovery results will be necessary to validate knowledge before progressing further into downstream expensive and regulated development activities. Once property rights are assigned, knowledge can be valued as a good itself and sold in disembodied form in the market for technological knowledge. The decision to sell disembodied knowledge in the form of patents and licenses can complement or substitute for the sale of embodied knowledge in the form of medical products. A changing genomics driven drug discovery and development paradigm as evidenced through the scientific and technical knowledge requirements for the successful development of new medical entities, has prompted biopharmaceutical companies to dramatically change how discovery research is conducted, the upstream partnerships formed to enable this research, and the management of discovery research outcomes. A historical analysis reveals that this industry has transitioned from academic-based discovery research to joint public–private discovery research, from enclosed to broadly disseminated upstream research that is now heavily supported by information-based technologies and languages. Clearly, knowledge production and management requirements in the systems-based paradigm have disrupted the tradition notion of discovery research as being a simple linear process-with research passing from one entity to another in protected form until reaching the marketing [22]. 3.1. Knowledge production Studies indicate that scientific collaboration is a process whereby the relationships between scientists do change according to the stage of the research [23]. Scientific interactions can be viewed as part of a continuum, ranging from full cooperation among all participants, subcollaborations and cooperation within such subcollaborations, to outright secrecy, and competition [23]. A distinct trajectory is thought to exist in biomedical research. Studies that have analyzed the gene races of the 1990s provide evidence of this trajectory. In these races, collaboration is the dominating strategy during the early phases of research. As researchers progress to the point where discovery of the gene becomes imminent, the private gains associated with being first to discover are believed to be greater than the common benefits associated with joint discovery. Despite the claim that competition can hasten discovery, the emergence of the race and possible enclosure of knowledge may not always be an optimal outcome.

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Kollock [24] discusses that cooperation levels are much greater when group members are asked to contribute to a public good that is indivisible. Knowledge that is non-substitutable and indivisible is likely to reinforce a sense of group identity and interdependency among group members [24]. Furthermore, in groups contributing to a public good, free riding is avoided when group members know that the provision of the public good is critically dependent on their actions. If the value of the knowledge increases with joint effort, cooperative behavior is optimal. The shadow of the future in terms of knowledge access and costs to invent should knowledge be fragmented, will also moderate the temptation to defect and encourage cooperation in knowledge development and ownership [24]. In an increasingly complex drug discovery and development paradigm, firms will form cooperative alliances further upstream to spread the risks associated with systems biology-based drug research and to ensure equitable access to upstream biological knowledge. Grant and Baden-Fuller [25] contend that the primary basis for knowledge-based alliances is knowledge access rather than knowledge acquisition. Such alliances contribute to the efficient integration of knowledge into the development of products and the efficient utilization of knowledge. These efficiencies are critical when there is uncertainty as to the role of future knowledge requirements for new product development and where there are early-mover advantages associated with rapid knowledge access and product development [25]. Where products require a broad range of different types of knowledge, efficiency of integration is maximized through separate firms specializing in different knowledge areas that are linked by strategic alliances [10,11,15,25]. As the breadth of knowledge required to generate new products increases, the propensity to form alliances with other firms who have specialized in the requisite knowledge, also increases [10,25]. As the pharmaceutical industry transitions into the systems biology paradigm, the nature of biological knowledge, namely the complementary nature of upstream biological knowledge, its complexity in terms of function, and its breadth of application, will encourage cooperative knowledge production. In this paradigm, strong early-mover advantages in drug development rest on the ability to rapidly identify, access, and integrate new combinations of knowledge [25,26]. Based on these observations we contend that the following transitions are occurring alongside the paradigm shift in this industry: Transition 1: As the complementary nature of upstream biological knowledge, its complexity in terms of function, and its breadth of application increases in the system biology paradigm, firms will increasingly cooperate during knowledge production. Transition 2: Given the complexity of disciplines and technologies required in systems-based drug discovery, cooperation will increasingly move upstream into the drug discovery phase. Transition 3: Cooperative structures will increasingly be in the form of knowledge-based networks including communities of individuals with the objectives of producing and disseminating knowledge. The open network will primarily be concerned with the generation of new, disembodied knowledge. To meet the challenges presented by systems biology, collaboration between disciplines is necessary. Engineers and computer scientists will have to learn that systems biology requires a detailed understanding of biology. Similarly, biologists will come to learn how to apply engineering concepts to biological systems. The development of common data standards, the establishment of efficient communication and knowledge sharing mechanisms across disciplines, and the management of different research and development priorities, are critical for the success of any systems biology project or program. The cultural divide between scientists and engineers during knowledge generation, dissemination, and

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then assignment can be a major source of conflict in any large collaboration. We propose the following then for the management of multi-disciplinary teams pursuing systems-based research: Proposition 1. Structures that maximize the frequency and intensity of contact can increase the chances of learning for researchers. Mechanisms used for knowledge transfer must ensure an equitable and twoway transfer between researchers. Proposition 2. Transparency of motives for cooperation during knowledge production and of appropriation goals will ensure that researchers are jointly willing to contribute to the learning process. Proposition 3. In the management of systems biology teams and partnerships, the research outcomes to be disseminated, the format for dissemination, and the knowledge that should be privatized for appropriation, should be clearly understood by all the participants. Based on the above propositions, several policy options exist to enable cooperative systems-based research and then ensure equitable access to the knowledge generated. Shared research programs, real-time learningby-doing opportunities, attendance at meetings, presentations, joint publications, and the review of written work, all enable for collective learning and create more opportunities to create new knowledge. Collaborative institutes and academic programs are also increasingly seeking to bring together the complementary assets of science and engineering for systems-based research. Within such institutes and academic programs, faculty and industry participants should define the strategies to be used to generate knowledge as well as the mechanisms to be used to disseminate knowledge [27]. The need to manage intellectual property rights assignment is critical when collaboration takes place across different institutions or types of institutions. Given that the assignment of rights to knowledge is a difficult task in large collaborations, technology transfer officers at these the collaborating institutions will have to learn if possible, how to parcel out intellectual property rights fairly across institutions, departments, or laboratories. Funding agency can enable not only for the development of large-scale collaborative systems-based alliances, but also encourage the open dissemination of any research results. By supporting such collaborations, funding agencies can indirectly encourage the norm of disclosure; guarantees of disclosure and descriptions of mechanisms for knowledge dissemination are often components of the grant application. Scientific journals are similarly compelling scientists to submit information to databases prior to publishing and receiving credit for their discoveries. A policy making journal publication contingent on DNA sequence database submission was first implemented by Nucleic Acids Research in 1988, compelling many other journals to follow suit [28]. This policy should be extended to signaling pathways and their structures (Table 2). 3.2. Knowledge dissemination In the systems biology paradigm, knowledge production and dissemination will require the development of common data standards for representing complex biological information. Specifically, the heterogeneity and complexity of systems biology knowledge requires the development of data standards that enable for management of and usability of this knowledge. Many bio-standards groups exist with the objectives to provide researchers with standards for representing and exchanging systemsbased knowledge [29–31]. Currently, data formats for static system structures and dynamic system processes are being developed. Ontologies provide a hierarchical description of systems knowledge and allow for the categorization of knowledge at different levels of abstraction. Ontologies will enable systems biology researchers to specify how system biology data, terminology, and concepts relate to each

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Table 2 Policy implications from the knowledge production phase in systems biology discovery research Proposition

Policy implication

Need for learning and equitable knowledge transfer. Transparency of motives during knowledge production. Management of research outcomes.

Establishment of shared research programs, collaborative institutes, and academic programs. Establishment and enforcement of rules by faculty, industry participants, and/or technology transfer officers. Establishment/enforcement of rules, guarantees of disclosure by federal funding agencies, and/or journal publishers.

other. Various schemas may be used by separate scientific and technical communities to represent biological phenomena with specific contextual biases. The development of a structured representation of pathway information will enable these researchers to collectively analyze and make inferences from biological systems during drug discovery and enable for the development of pathway algorithms and tools. Interoperability between applications and technologies further necessitates the development of a common data and knowledge interchange model [7,29,32]. Various repositories will be used to store codified knowledge. Units of knowledge having significance for its users will be classified, indexed, annotated, and then stored in repositories for the purpose of retrieval and then manipulation by researchers. These repositories will not only store the independent units of knowledge, but will also store the various linkages among the various types of knowledge [32– 34]. As researchers validate and create new systems-based knowledge, these repositories will accommodate changes and additions to the stored knowledge. Experimental feedback processes between systems biology research and drug discovery research will be enabled through linkages between the respective repositories storing the knowledge. From this perspective, upstream repository creation will enable downstream knowledge distribution and use [35]. Knowledge can be actively distributed to various researchers through triggering mechanisms matching a researcher's interests or passively accessed by researchers though criteria based searches. In knowledge networks, rules may be established to determine which researchers should receive or be able to access the knowledge [27,36]. Electronically mediated channels such as central electronic repositories enable researchers from such knowledge networks to effectively and broadly distribute and access knowledge. Experts from all over the world will also contribute to the electronic repositories—sharing their knowledge of the individual molecules and pathways that comprise cellular signaling systems [30,37]. Based on the above, we can summarize the following as the pharmaceutical industry transitions from the chemical-based to systems-based drug discovery paradigm: Transition 4: At a molecular level of analysis, systems-based drug discovery will require access to large genomic, proteomic, biological systems as well as chemical and physiological data sets in the search for new medicines. Transition 5: The interdisciplinary and global nature of discovery research will increasingly require the development of a common and widely accepted language for representing complex biological information. A Case Analysis: The Alliance for Cellular Signaling (AfCS). The Alliance for Cellular Signaling (AfCS) is a multi-disciplinary and multi-institutional consortium, established to study cellular signaling systems. The focus of the AfCS is to study the mechanisms by which cells communicate with each other and

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interpret signals in a context-dependent manner. Data is gathered under standardized conditions on a small number of biological systems, enabling for depth of analysis of the behavior of signaling systems [37,38]. The National Institute of General Medical Sciences (NIGMS) catalyzed the organization of the Alliance. The NIGMS funds large-scale collaborative projects to facilitate integrative biomedical research. Funding was obtained from the NIGMS, the National Institute of Allergy and Infectious Diseases, the National Cancer Institute, a consortium of pharmaceutical companies including Eli Lilly, Aventis, Johnson and Johnson, Novartis, and private donors. The participating investigators of the AfCS comprise a group of approximately 50 scientists at 20 different institutions, predominantly in the United States, with a few from Canada and the United Kingdom [38]. Beyond the physical laboratories that comprise the alliance, the AfCS enables complementary research throughout the signaling community. Membership in the AfCS stands at over 1500 members. The AfCS acknowledges that given the complexity of the problems, by acting alone, the alliance can only acquire a fraction of the necessary system-based information. Therefore, the AfCS facilitates researchers working within independent laboratories to pursue leads by disseminating data publicly and promptly via the Internet. Through the use of the Internet, researchers are connected to one another in an open research network [38]. In the early stages of the alliance's research program, effort was devoted to the identification of the components of cellular signaling networks. Research is now being conducted to address the complex system aspects of biology using methods from systems engineering, computer science, control theory, and circuit design. Close interactions are necessary between the database, informatics, and the systems teams to create data structures and tables appropriate for network analyses and ensure that experimental procedures yield data that are appropriate for network modeling. Tools are also being created to organize and analyze data as well as build models of systems that can be experimentally validated [38]. The AfCS-Nature Signaling Gateway was launched in 2002 and is a unique collaboration between the AfCS and the Nature Publishing Group (NPG). The Signaling Gateway represents a new communitydriven approach to scientific research. The AfCS uses the site to disseminate and update its own vast collection of experimental data and invites the research community to contribute to the process of analysis and discovery with these data. Standardized documents including literature-derived information about a given molecule, information from the alliance laboratories and linkages between maps of signaling pathways, literature, and other databases, comprise this electronic repository [38]. Applying the lessons from the AfCS, we propose the following: Proposition 4. Information and communication technologies will be required to bridge together laboratories and teams that are geographically separated but are connected by open research networks. Proposition 5. Given the iterative nature of systems biology discovery research and need for validation by multiple researchers, the deposit of data into repositories will require the development of data warehousing and methods for query of multiple heterogeneous data. Proposition 6. The use of large data sets will require the development of a common knowledge electronic interchange model to enable interoperability between applications and technologies. Policy implications based on the above propositions include: providing federal support for large-scale collaborative projects such as the Alliance for Cellular Signaling and systems biology programs at academic institutions, enabling the development of supporting technology for data management through such funding initiatives, and enabling knowledge interchange communities with the expressed intention of developing open data standards. Several initiatives that are currently underway bridge together the scientific and

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engineering disciplines through teaching, research, and other outreach events. A central component of these initiatives is the management of knowledge that is cooperatively developed physically on-campus and/or off -campus at partner locations. Management of knowledge includes the development of supporting data management systems that use biologically-based, XML standards for codification (Table 3). 3.3. Knowledge appropriation From the one-dimensional information of the gene, to the three-dimensional information of proteins, biological research has moved into the fourth dimension of observing and mapping the time variant information present in biological systems [1]. Of particular importance will be the ability to follow the flow of information along the pathways that comprise such systems. To map this flow of information, complex systems will be divided into subsystems whose properties and interactions are observable. From each subsystem, the structures within each subsystem will be identified along with their interconnections with other structures. Ultimately, the flow of information from subsystem to subsystem will be mapped and modeled, thereby enabling researchers to decipher the structure of the informational pathway and the resulting system properties. From the perspective of knowledge appropriation, the challenge is to determine whether or not systems as a whole are patentable. In any system, there are one-dimensional and three-dimensional structures as well as their time variant interconnections. These elements themselves are individually patentable. If prior patents on such structures or subsystems exist, what is then the impact on the patent filed to cover the entire system? Researchers must consider that individual structures and subsystems in isolation do not provide complete information about a system, its properties, and role in disease, and that a system must be mapped in its entirety for value creation during the drug discovery process. Complicating the matter is the hierarchical nature of biological information in a system. At any level in this hierarchy, patents may exist. Depending on the breadth of patents filed at a particular level, these patents can dominate other hierarchical levels of biological information [1]. Dominance of patents filed earlier in time, at the lowest levels of the biological information hierarchy, can therefore significantly hinder the incentive to conduct research into the higher levels of the hierarchy where appropriation may not be possible. Furthermore, if multiple researchers own patents over the structures or subsystems comprising a system, the system may become so fragmented that other researchers may no longer be able to exploit the system in its entirety. The transaction costs associated with recombining appropriate rights to the patented elements that comprise the system, for downstream exploitation, may be too high for a downstream developer [14,39]. This problem will be exacerbated by “reach-through” licenses in which the owners of system structures and

Table 3 Policy implications from the knowledge dissemination phase in systems biology discovery research Proposition

Policy implication

Need for enabling large-scale, global research projects. The need for supporting communication and data management technologies. The need for common knowledge interchange models such as those based on XML.

Federal funding encouraging the development of global teams with participants from the public and private sectors. Federal funding encouraging the development of global and publicly accessible data repositories. Enabling the development of Systems Biology-Based Markup Languages (SBML).

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subsystems seek control of and royalties on the downstream uses of these structures or subsystems developed by follow-on innovators [40]. Based on the above, we can state the following: Transition 6: Biological knowledge has become hierarchical in nature. Each level of information builds on information found at lower levels in this hierarchy. As such, systems biology uses cumulative knowledge. Transition 7: As the drug discovery and development paradigm transitions, the focus of intellectual property rights will shift to the patenting of complementary, hierarchical, systems-based information. For many industries, concern has been expressed where the research process is primarily knowledge based, the process of invention may be cumulative and iterative, with downstream research dependent on upstream research [14,39,42,48]. A patent system that was developed for a discrete model of innovation and an essentially linear relationship between knowledge elements may no longer be optimal for a knowledgebased, cumulative model of innovation. The notion of biological entities as being composition of matter from the chemistry perspective tends to support the view that extending patent protection to biotechnological inventions is nothing new but simply a matter of expanding an existing logical patent category. Systems biology however attempts to understand the interactions and informational flow between structures in the cell. Data from various hierarchical levels of biological information will be incorporated into the modeling of systems. Each level of information builds on information found at lower levels in this hierarchy—providing positive externalities to researchers who can use this knowledge to generate and embody the knowledge in new medical products. Given the uncertainty associated with the function of the structures that comprise a system and the role of a system in disease, the incentives to find the full breadth of a system's properties and functions should be preserved for multiple researchers. These complexities will be exacerbated as multiple disciplines increasingly work together in the systems biology paradigm. Each discipline will have its own priorities and conventions regarding knowledge dissemination and knowledge appropriation [4]. One discipline may signal its success during knowledge generation through enclosure and the sale of disembodied knowledge. Another discipline may measure its success exclusively by the embodiment of knowledge in medical products. As collaborations cross institutional boundaries, the parceling out of intellectual property rights may be too difficult a task. With the assignment of property rights, the role of the patent holder in providing broad versus narrow access to the knowledge will then depend on the original incentives for producing the knowledge. Therefore, we propose the following in terms of knowledge appropriation in this paradigm: Proposition 7. To ensure that multiple technologies opportunities are available for the development of systems-based medicines, narrower claims should be protected and non-exclusive licensing of appropriated knowledge should be encouraged. Proposition 8. Open networks of collaboration can enable rapid access to complementary expertise and information. Proposition 9. Rules need to be established within such networks regarding the appropriation of disembodied knowledge versus embodied knowledge. Several policy implications stem from these propositions (Table 4). The United States Patent and Trademark Office (USPTO) can influence the likelihood that property rights are assigned to knowledge. New guidelines can raise the threshold for patenting, as such, postponing the assignment of property rights to when

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Table 4 Policy implications from the knowledge appropriation phase in systems biology discovery research Proposition

Policy implication

New perspective for patent subject matter Technological opportunities across diseases

Informational view to patenting at the USPTO Narrower claims; Avoid reach-through to medical products Networks of collaboration; Cross-licensing Early establishment of rules to manage knowledge activities

Need for access to complementary expertise and information Differing conventions regarding knowledge dissemination and appropriation

genuine novelty, inventive step, and value exist [41]. Academic institutions also need to reconsider their role in disseminating knowledge. The benefits from assignment of an exclusive license versus the costs to downstream knowledge generation should be weighed before these institutions issue such licenses. Federal funding agencies may have a role here to advocate public institutions to keep upstream systems knowledge in the public domain or issue non-exclusive licenses to enable for broad dissemination of the knowledge [42]. Open source discovery initiatives are enabling companies to access disembodied knowledge-based resources critical to downstream drug development. The objective of these cooperative strategic alliances is to preserve the downstream technological opportunities for multiple firms. When upstream discovery research cannot yield commercial products and when the costs associated with excessive upstream competition are too high, companies jointly benefit from cooperative knowledge production and open knowledge dissemination [43,44]. For example, the entire AfCS research community is encouraged to make use of the results in their own investigations and publications. Furthermore, the researchers in the Alliance forgo any potential intellectual property rights. Protocols, sequences, reactions, and reagents are deposited into the public domain with no obligation to the AfCS. The hope is to level the playing field for all researchers regardless of participation in the Alliance. Information is released as soon as it has been verified and neither AfCS investigators nor sponsors have access to data before it is made available to the public [37,38]. 4. Discussion and conclusion The Human Genome Project has dramatically changed how researchers view and practice biology. The Human Genome Project has strengthened the view that biology is an information science with both static and dynamic elements. Systems biology seeks to understand the various hierarchies of biological information, the complex networks of genes and proteins, and the key nodes in a system where perturbations can have a profound impact during medical intervention. Platform technologies will take advantage of central intervention nodes and pathways found in multiple systems. The Human Genome Project emphasized the need for interdisciplinary researchers. To complete the sequence map of the Human Genome required breakthroughs in understanding computational sciences, measurement technologies, statistics, and data management [1,2]. Tools enabling high throughput quantitative measurements of biological information were developed from this collective understanding. Computer science, mathematics, and statistics were also employed to handle, store, disseminate as well as analyze biological information. With the completion of the Human Genome Project, a new paradigm has emerged that analyzes the biological information revealed from the project from a holistic perspective. Biologists are no longer satisfied with the simple inventory of genomic structures, but are working toward understanding how these structures collectively work together in biological systems that control the

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development and function of organisms. Biological systems are being analyzed in silico and in real time experiments. The objective is to dissect these models in the search for common pathways and mechanisms that can be controlled or redesigned for medical intervention. Systems biology will transform biological research into a more quantitative discipline, needing even more sophisticated tools to measure biological processes and manage the resulting data. Discoveries are made at the intersection of once disparate disciplines [45]. The intellectual and technological challenges associated with understanding biological systems require collective effort from multiple research arenas. As such, the scope of these interactions has broadened considerably since the completion of the Human Genome Project. Managing the various scientific and technical cultures of systems biology is in itself a challenge. New initiatives and programs dedicated to systems biology are enabling for networking and collaboration between the disparate disciplines. Institutions are physically bringing together scientists and engineers from various laboratories located within their boundaries. Other networks are enabling for virtual collaboration between researchers dispersed globally through the use of information technologies. These institutions and networks are charged with the responsibility of breaking down the traditional cultural and bureaucratic barriers associated with the disciplines. At the heart of the matter is the need to enable for knowledge production, communication between disciplines based on a common scientific language, and the need for knowledge dissemination. Within communities of interdisciplinary researchers, all participants must understand conventions regarding knowledge production and dissemination. The International Human Genome Project catalyzed the open-source movement in genomics-based research. Globally dispersed laboratories jointly collaborated to map and sequence the Human Genome. The resulting data were rapidly deposited into the public domain to ensure an open and level playing field for all researchers [46,47]. These laboratories were obligated to deposit any new data within 24 hours into a centrally accessible electronic repository enabling other researchers to validate the knowledge [46]. As the Human Genome Project progressed through various models, comparative analyses across models were supported through linkages between the associated data and literature repositories. Efforts in the public sector to enable large-scale genomics research through open source initiatives have also encouraged the private sector to participate in these initiatives as evidenced by the Single Nucleotide Polymorphism Consortium. The development of other such open-source initiatives can be traced from the time of the 1990s gene races to present day involving researchers, institutions, and organizations from across the world. Table 5 provides an overview of systems biology consortia, the participants in each consortium, the focus of knowledge in each consortium, the characteristics of the knowledge produced, the dissemination and/or enclosure of knowledge generated, and timing of appropriation of knowledge. These consortia use rules and binding agreements to defer appropriation until the characteristics of knowledge warrant patenting to ensure that downstream products are developed. Where tools, reagents, or biological materials are produced or contributed, rules are established with respect to the licensing of this knowledge for noncommercial use. These rules and agreements essentially seek to level the playing field for all downstream researchers in an increasingly complex knowledge environment. We conclude that an understanding of upstream knowledge characteristics should enable managers to better understand the need for strategic alliances and determine which firms are likely to join as alliance partners. In determining alliance structure, managers should examine the goals of knowledge production, namely knowledge discovery versus knowledge application. These goals can be mapped onto the physical form of knowledge to be generated and then optimal alliance structure. Our analysis should hopefully enable academics, managers, and policy-makers to begin to view the drug discovery process from a knowledge perspective and better inform these participants on how to manage biological knowledge

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Table 5 Analyzing open source initiatives in the human genome era Alliance

Type Focus

Knowledge Knowledge Knowledge production dissemination appropriation

Alliance for Cellular Signaling Biocarta Cell Migration Consortium

PPP Cellular signaling PPP Cellular signaling PUB Cell migration (reagents, technologies and data) PUB Pancreatic Islet Cell Development PPP Genomics-based technology PUB Carbohydrate-protein interactions PPP Methylation variable positions (MVPs) PR In silico ADME analysis/ tool exchange PPP Gene expression analysis (cancer tissue) PUB Molecular data exchange

UP UP UP/DN

PD PD PD

DA DA DA

UP/DN

PD

DA

UP/DN UP

PD PD

DA UA/DA

UP

PD

UA/DA

DN

EN

DA

UP

PD

DA

UP

PD

Unknown

UP

Unknown

Unknown

UP

PD

DA

UP/DN

PD

DA

UP

PD

DA

UP

PD

DA

UP

PD

DA

UP/DN UP

PD PD

DA DA

UP

PD

DA

Beta Cell Biology Consortium Consortium for Functional Genomics Consortium for Functional Glycomics Human Epigenome Consortium IDEA Bioinformatics Consortium International Genomics Consortium International Molecular Exchange Consortium International Regulome Consortium

PUB Regulatory networks (gene expression map) Merck-Washington University, PPP Human genome sequence St. Louis Initiative and mapping MitoCheck Consortium PUB Genome-wide siRNA-based Gene Search Novartis Institutes for Biomedical Research- PPP Genetic Basis for Diabetes 2 Broad Institute Alliance Public Population Project in Genomics PUB Gene-Disease Association in Populations Research Collaboratory for PUB 3-D Structure of Biological Structural Bioinformatics Macromolecules RNAi Consortium PPP Toolset for Drug Discovery SNP Consortium PPP Human Genome Variation Analysis Structural Genomics Consortium PPP Three-Dimensional Structure of Proteins

Type: PUB = public participants only; PR = private participants only; PPP = public–private partnership; Knowledge Production: UP = upstream, discovery-based knowledge production; DN = downstream, application-based knowledge production; Knowledge Dissemination: PD = public domain; EN = enclosed within private domain; Knowledge Appropriation: UA = upstream appropriation (including knowledge produced within the consortium); DA = downstream appropriation (downstream beyond the scope of the consortium).

assets through the cooperative structures discussed in this article. Finally, through the correct valuation of knowledge by a firm, given a firm’s product development goals, managers will be in a more strategic position to correctly and efficiently appropriate knowledge. Developing the capability to conceptualize and efficiently manage these new complexities in systems biology research is fundamentally necessary to ensure that researchers can exploit the technological opportunities presented by this new paradigm.

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