In This Issue
Fall Issue of The Bridge on the Convergence of Engineering and the Life Sciences
October 1, 2013 Volume 43 Issue 3

Systems Biology and Systems Pharmacology

Wednesday, September 25, 2013

Author: Douglas A. Lauffenburger and Kathleen M. Giacomini

Definitions of systems biology are as broad ranging as the field itself. An early offering was from the Institute for Systems Biology:

Systems biology does not investigate individual genes or proteins one at a time, as has been the highly successful mode of biology for the past 30 years. Rather, it investigates the behavior and relationships of all the elements in a particular biological system while it is functioning. (Ideker et al. 2001)

The National Institute of General Medical Sciences offered a different perspective:

Systems biology seeks to predict the quantitative behavior of an in vivo biological process under realistic perturbation, where the quantitative treatment derives its power from explicit inclusion of the process components, their interactions, and local states. (Anderson 2003)

These two definitions, perhaps representing extreme ends of a conceptual spectrum, focus on aspiration for genomewide comprehension on the one hand and quantitative prediction on the other, and the two are not readily coincident. A central unifying concept, emphasized in two academic endeavors at MIT ( and Harvard Medical School (, calls for understanding how complex biological entities function by integrating quantitative information about multiple molecular- and cellular-level components and properties via computational modeling in order to generate hypotheses, predictions, and insights.

Systems Biology
The Role of Integration: Horizontal, Vertical, and Dynamic

The notion of integration as central to bioscience and bioengineering is set forth in the 2009 National Research Council report A New Biology for the 21st Century (NRC 2009). But there are multiple dimensions of integration along which the analysis of biological systems must be pursued, as illustrated in Figure 1 (Lauffenburger 2012).

Figure 1

One axis represents horizontal integration, moving from the study of individual components (e.g., a single molecule) to that of multiple components (in principle, up to the full genomewide complement). This axis is manifested in -omic biology.1

A second axis represents vertical integration (often associated with physiology, whether mammalian or microbial), moving from the study of system operation (i.e., phenotype, essentially), in the simplest contexts and at the smallest space and time scales, to more complex contexts involving larger space and/or time scales. In the mammalian realm, this integration may feature studies of cell-level behavior dependence on molecular properties, tissue- or organ-level behavior dependence on cell properties, organism-level behavior dependence on tissue/organ properties, and population-level behavior dependence on organism properties; analogies exist in the microbial realm as well. Integrating across more than one of these spatiotemporal scale interfaces is an overarching goal, and experimental studies of this kind coupled with computational modeling yield the swiftly growing field of multiscale modeling.

A third axis represents dynamic integration, characterized by the depth or intensity of information addressed in measurement and modeling efforts to understand complex molecular “machines” or “circuits” at the deepest scale. This axis moves from sequence and structure at the most basic degrees, through thermodynamic information, to kinetic and transport information, to comprehensive dynamical systems operation. It thus spans bioinformatics, biochemistry, and biophysics.

The definitions associated with these three axes may help explain the emergence of systems biology at the fore of bioscience and bioengineering, as they incorporate extensive and quantitative multicomponent identification and measurement at molecular and cellular levels.

The Roles of Genomic Biology and Engineering

Technologies for identifying particular molecular constituents of biological entities (e.g., gene cloning and monoclonal antibodies) can be traced to the molecular biology revolution of the 1970s–1980s. These molecular technologies fostered quantitation as well as identification and, with later developments such as RNA interference, also enabled fairly specific manipulation of constituents.

But the capacity to measure or manipulate molecular constituents in a highly multicomponent, or “high-throughput,” manner largely arose from the genomic biology revolution starting in the late 1980s and accelerating into this century. This revolution was launched by the human genome sequencing effort, which attracted an unprecedented avalanche of financial and intellectual resources and stimulated the application of physics, chemistry, mathematics, and engineering to build instruments and algorithms to address the challenge.

In addition to producing high-throughput DNA sequencing machines and generating sequence data that enabled new approaches to cataloguing other molecular constituents, the sequencing of the human genome broke down perceived technical and intellectual barriers between basic biological science and the other disciplines noted above. The technical facet was perhaps less of a barrier, because it can be argued that physics, chemistry, mathematics, and engineering have regularly played key roles in aiding the work of biologists. More unconventional is the emergence of new thinking about biology that incorporates the other disciplines, especially engineering.

The traditional engineering disciplines—civil, mechanical, and electrical engineering, based on various branches of physics; and chemical and materials engineering, based on branches of chemistry—build on science to make useful things for society. They are effective especially when there is incomplete knowledge about complex systems that must be analyzed in terms of the quantitative properties of their mechanistic constituents to enable predictive synthesis. Biology is now at a stage where useful things can be made from its mechanistic constituents (molecules, cells), but there remains a vast realm of incomplete knowledge about its full complement of constituents and about the properties and principles that explain their dynamic operation. The intellectual approach of engineers is singularly suitable for advancing the understanding and utility of biology.

Correspondingly, together with the development of research and education programs in systems biology during the past decade, a new kind of bioengineering discipline has emerged in molecular/cellular bioscience. It embraces bioscience’s full -omics complexity, extending beyond the classical medical engineering field of traditional physics- and chemistry-based engineering disciplines in support of clinical medicine applications (Ideker et al. 2006).

Systems Pharmacology

Systems pharmacology, a cousin of systems biology, is poised to make major contributions to drug discovery and to the understanding of pharmacological mechanisms. The genomic biology revolution has promised “personalized” or “precision” medicine, but how to translate this notional breakthrough into practical patient treatment is a serious challenge (Giacomini et al. 2012). Systems pharmacology seeks to integrate drug discovery with biological systems to increase the potential to discover effective medications with few side effects.

Systems Approaches toward Improving Drug Discovery

Drug discovery has typically taken a highly reductionist approach, generally beginning with target identification and validation in isolated cell systems or genetically engineered mice. After target validation, candidate drugs that modulate the target are discovered, usually through high-throughput screening methods in cell-based assays. Unfortunately, this molecular approach to drug discovery ignores the biological system and leads to unanticipated effects of the candidate drugs upon in vivo administration, including lack of efficacy and myriad toxicities.

The successful use of systems pharmacology in drug discovery requires the development of new technologies and approaches, including computational methods. For example, whole-organism phenotyping techniques in zebrafish are being developed and applied to the discovery of new drugs with fewer side effects (Laggner et al. 2011; Macrae 2013; Peterson and Macrae 2012). Moreover, coupled with computational analysis (Roguev et al. 2013; Shoichet 2013), whole-organism methods can also be used to determine likely biological targets and pathways.

Multivariate computational modeling and quantitative experiment can, in fact, reveal promising targets that would not be readily ascertained from genetic mutations or variations. For example, a kinetic mass action model for multipathway signaling network dynamics in the ErbB receptor/ligand system was constructed across diverse tumor cell types to gauge critical parametric sensitivity with respect to molecular properties, and the ErbB3 receptor was identified as especially useful to target although it is not generally overexpressed or mutated (Schoeberl et al. 2009). This prediction has been validated and is being pursued in clinical trials for a variety of cancer applications (McDonagh et al. 2012).

An especially exciting prospect is the use of systems analysis approaches in vivo to reveal molecular and cellular processes involved in pathophysiology and response to intervention. Quantitative measurement of intracellular signal activities, extracellular cyto- and chemokines, and immune cell types in mouse model tissue under diverse perturbation conditions, integrated via multivariate computational regression analysis, has enabled the elucidation of complex mechanisms governing intestinal inflammation and the successful prediction of effects of kinase inhibitors and antibodies in vivo (Lau et al. 2012).

The concept of biomarkers offering indication of drug effects is similarly moving to become multivariate (Prat et al. 2011), although reliance on pure “signatures” remains fraught with uncertainty when not substantively connected to underlying mechanism. This is especially problematic with signatures at the transcriptomic level (Venet et al. 2011). But mechanistic understanding of complex -omic signatures is possible through computational integration of heterogeneous data—i.e., measurements at multiple levels of cellular regulation (e.g., proteomic, phosphoproteomic, metabolomic, epigenomic, transcriptomic) (Huang et al. 2013; Miller et al. 2013; Miraldi et al. 2013; Winter et al. 2012). Development of a capability for linking gene sequence information to molecular pathway consequences would be of great value, given the increasing ease of garnering sequence data (Yee et al. 2013).

Systems Approaches for Understanding Pharmacologic Mechanism

Knowledge of pharmacologic mechanism includes understanding the molecular mechanisms of drug interactions with their targets as well as the pathways and network that lead to whole-organism drug response phenotypes.

For the past two decades, the field of pharmacology has been dominated by molecular pharmacology, which has focused largely on target identification. This focus has led to many discoveries of pharmacologically important targets, including the identification of the entire G protein–coupled reactor (GPCR) family, the discovery of druggable kinases, and the characterization of many other families such as the cytochrome P450s (CYPs), major drug-metabolizing enzymes, and the adenosine triphosphate (ATP) binding cassette (ABC) superfamily of efflux pumps that restrict drug access to target tissues and tumors.

But although molecular pharmacology has enjoyed many triumphs, it has provided an inadequate understanding of the in vivo pharmacological behavior of small molecules. These molecules interact with multiple targets, which in turn perturb various pathways and networks, leading to a cascade of events that result in the pharmacological—and toxicological—action of drugs. Methods to identify the multiple targets and the affected pathways and networks are needed to enhance knowledge of the pharmacological mechanisms that mediate therapeutic drug response.

Computational methods that use ligand similarities to determine which receptors interact with identical ligands have recently been developed (Lin et al. 2013; Lounkine et al. 2012). These “ligand similarity maps” can be used to predict off-target effects of drugs and to discover receptors that, in addition to the known target, are modulated by the drug in vivo.

Other powerful techniques reveal important information about pharmacologic mechanism. For instance, identification of molecular signatures of drugs sheds light on pharmacologic mechanisms, including those of drug combinations, at a systems level (Lee et al. 2012).

Multiple types of data and technology platforms can be integrated to determine drugs’ molecular signatures, which can then be categorized and used to predict and understand drug responses (Pritchard et al. 2013). Similar integrative experimental and/or computational multivariate approaches are likewise promising for immune system therapeutic interventions such as vaccines (Katze 2013) and infectious disease antibiotics (Roemer and Boone 2013).

Systems Approaches for Addressing Adverse Drug Effects

It has been estimated that each year about 100,000 deaths in the United States occur because of serious adverse drug reactions (Giacomini et al. 2007). In fact, many drugs are withdrawn from the market because of such reactions. Therefore a major goal of systems pharmacology is to understand and predict drug toxicities, which often occur because of off-target effects of drugs.

A paradigmatic study used ligand similarity maps to predict such effects of various drugs (Keiser 2007). As one example, the anti-emetic agent motilium was withdrawn from the market because of adverse events related to cardiac arrest–mediated sudden death. Drug interactions with dopamine receptors were thought to contribute to the cardiac events, and subsequent experiments showed that motilium interacted with these receptors at unusually high affinities.

As another example, increase in heart rate upon withdrawal from selective serotonin reuptake inhibitor antidepressants was predicted to be related to rebound tachycardia, a known effect of withdrawal of beta-adrenergic blocking agents such as propranolol. Indeed, the antidepressant agents fluoxetine and paroxetine were shown to interact with beta-adrenergic receptors at concentrations achieved clinically.

Collectively, such studies suggest important, but previously unknown, off-targets that explain drugs’ adverse effects.

But significant issues remain. A 2011 National Institutes of Health (NIH) white paper on systems pharmacology described a crisis in drug discovery and pharmacology and called for “Systems pharmacology [that] applies…systems biology methods combining large-scale experimental studies with model-based computational analysis, to study drug activity, targets and effects with the aim of understanding the system as a whole, rather than the behavior of its individual parts” (QSP Workshop Group 2011). Clearly, further development and application of systems biology are needed to address problems in drug discovery and to enhance understanding of pharmacologic and toxicologic mechanisms.

Regulatory Challenges

Regulatory science is an emerging research area that will benefit enormously from systems pharmacology approaches. For example, the regulatory path forward for the development of combination drugs, a potential product of systems pharmacology approaches to drug discovery, has been a matter of concern among drug developers. A primary focus of past drug approvals has been single drugs rather than drug combinations. For the latter, the US Food and Drug Administration (FDA) has focused chiefly on fixed-dose combinations, wherein each component must have demonstrated therapeutic effect. The development of combination therapies thus required lengthy and costly clinical trials.

To address these concerns, update practices, and facilitate the development of novel drug combinations, FDA issued a guidance that offers some flexibility to sponsors seeking to codevelop two or more novel agents together in a single program (FDA 2010). The guidance notes the possibility of waiving the requirement that each compound undergo a clinical trial alone and then in combination, particularly in cases where multiple monotherapy trials would not be possible. The guidance also provides advice on data types that would provide support for the effects of individual drugs in the combination.

Such forward thinking will enhance the implementation of systems pharmacology approaches to drug discovery, development, and regulatory sciences (Iyengar et al. 2012). The capacity to predict adverse drug-drug interactions from computational integration of heterogeneous datasets likewise promises an important benefit for clinical pharmacology and regulatory sciences (Tatonetti et al. 2012).

Cell- and Tissue-Level Systems Medicine

Although this article has focused primarily on applications of systems biology to molecular medicine, comment on relevance to cell- and tissue-based therapeutics is germane. A substantial number of scientists and engineers have pursued technologies in this field, but they have been hampered by the same problems as in molecular therapeutics in terms of inadequate understanding of fundamental principles for predictive design—in fact, even more so because of the greater complexity of the biological system involved in a higher level of organization.

For example, great excitement exists about the promise of stem cell technologies for disease treatment, whether by tissue replacement/regeneration or by facilitation of in vitro experimental models for molecular pharmacology/toxicology studies (Griffith and Naughton 2002; Soldatow et al. 2013). The central problem in stem cell technologies is the difficulty of reliable, reproducible generation of well-characterized differentiated cell types; it remains a largely trial-and-error pursuit because of insufficient understanding of stem cell fate regulation, even as the toolkit of molecular techniques for manipulating cell fate continues to expand (Wörsdörfer et al. 2013).

Information about the programming of stem cells, or the reprogramming of somatic cells for the production of stem cells via control of gene expression, is continuing to accumulate. This gene expression–level control, however, needs to be connected to proteomic signaling networks that are generally responsible for transducing tissue-level contextual conditions (e.g., the growth factors, cytokines, and extracellular matrix components present in the cell surroundings) into cell behaviors, including cell fate decisions (Buganim et al. 2013; Cosgrove et al. 2009; Mazzoni et al. 2011). Systems-oriented approaches—which integrate quantitative multivariate experimental measurement across different cellular control levels (e.g., gene expression, protein signaling pathways) via computational modeling—are amenable to stem cell studies in a manner analogous to those described above for molecular therapeutics (Prudhomme et al. 2004).

Because cell fate decisions are strongly influenced by microenvironmental conditions, the most powerful such studies generally involve high-throughput modulation of biomaterials in a fashion that enables characterization of cell phenotypic behavior and associated intracellular and extracellular molecular activities (Onishi et al. 2012; Roccio et al. 2012). How to accomplish this high-throughput environmental modulation while providing the most physiologically realistic and relevant context, which typically requires three-dimensional constructs involving fluid flow and mechanical deformations, is a challenge for bioengineering technology (Griffith and Swartz 2006). Indeed, an unusual partnership among US government agencies—the Defense Advanced Research Projects Agency (DARPA), FDA, and NIH—to learn how to establish and use “microphysiological systems” has recently been created to address this challenge, with the explicit goal of enabling more effective therapeutics research.2 This endeavor is pioneering an important new avenue of integration, that of systems biology (and pharmacology/toxicology) with cell-based tissue engineering (Cosgrove et al. 2008).


Anderson J. 2003. National Institute of General Medical Sciences Centers for Systems Biology.

Buganim Y, Faddah DA, Jaenisch R. 2013. Mechanisms and models of somatic cell reprogramming. Nature Reviews Genetics 14:427–439.

Cosgrove BD, Griffith LG, Lauffenburger DA. 2008. Fusing tissue engineering and systems biology toward fulfilling their promise. Cellular and Molecular Bioengineering 1:33–41.

Cosgrove BD, Sacco A, Gilbert PM, Blau HM. 2009. A home away from home: Challenges and opportunities in engineering in vitro muscle satellite cell niches. Differentiation 78:185–194.

FDA [US Food and Drug Administration]. 2010. Guidance for Industry Codevelopment of Two or More Unmarketed Investigational Drugs for Use in Combination. Silver Spring MD: FDA Office of Communications. Available online at GuidanceComplianceRegulatoryInfo rmation/Guidances/UCM236669. pdf.

Giacomini KM, Krauss RM, Roden DM, Eichelbaum M, Hayden MR, Nakamura Y. 2007. When good drugs go bad. Nature 446:975–977.

Giacomini KM, Yee SW, Ratain MJ, Weinshilboum RM, Kamatani N, Nakamura Y. 2012. Pharmacogenomics and patient care: One size does not fit all. Science Translational Medicine 4:153ps18.

Griffith LG, Naughton G. 2002. Tissue engineering: Current challenges and expanding opportunities. Science 295:1009–1014.

Griffith LG, Swartz MA. 2006. Capturing complex 3D tissue physiology in vitro. Nature Reviews Molecular Cell Biology 7:211–224.

Huang SS, Clarke DC, Gosline SJ, Ladadorf A, Chouinard CR, Gordon W, Lauffenburger DA, Fraenkel E. 2013. Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling. PLoS Computational Biology 9:e1002887.

Ideker T, Galitski T, Hood L. 2001. A new approach to decoding life: Systems biology. Annual Review of Genomics and Human Genetics 2:343–372.

Ideker T, Winslow LR, Lauffenburger DA. 2006. Bioengineering and systems biology. Annals of Biomedical Engineering 34:1226–1233.

Iyengar R, Zhao S, Chung SW, Mager DE, Gallo JM. 2012. Merging systems biology with pharmacodynamics. Science Translational Medicine 4:126ps7.

Katze MG, ed. 2013. Systems Biology (Current Topics in Microbiology and Immunology). New York: Springer.

Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. 2007. Relating protein pharmacology by ligand chemistry. Nature Biotechnology 25:197–206.

Laggner C, Kokel D, Setola V, Tolia A, Lin H, Irwin JJ, Keiser MJ, Cheung CY, Minor DL Jr, Roth BL, Peterson RT, Shoichet BK. 2011. Chemical informatics and target identification in a zebrafish phenotypic screen. Nature Chemical Biology 8:144–146.

Lau KS, Cortez-Retamozo V, Philips SR, Pittet MJ, Lauffenburger DA, Haigis KM. 2012. Multi-scale in vivo systems analysis reveals the influence of immune cells on TNFa-induced apoptosis in the intestinal epithelium. PLoS Biology 10:e1001393.

Lauffenburger DA. 2012. The multiple dimensions of integrative biology. Integrative Biology 4:9.

Lee MJ, Ye AS, Gardino AK, Heijink AM, Sorger PK, MacBeath G, Yaffe MB. 2012. Sequential application of anti-cancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149:780–794.

Lin H, Sassano MF, Roth BL, Shoichet BK. 2013. A pharmacological organization of g protein-coupled receptors. Nature Methods 10:140–146.

Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Cote S, Shoichet BK, Urban L. 2012. Large-scale prediction and testing of drug activity on side-effect targets. Nature 486:361–367.

Macrae CA. 2013. Recent advances in in vivo screening for antiarrhythmic drugs. Expert Opinion on Drug Discovery 8:131–141.

Mazzoni EO, Mahony S, Iacovino M, Morrison CA, Mountoufaris G, Closser M, Whyte WA, Young RA, Kyba M, Gifford DK, Wichterle H. 2011. Embryonic stem cell-based mapping of developmental transcriptional programs. Nature Methods 8:1056–1058.

McDonagh CF, Huhalov A, Harms BD, Adams S, Paragas V, Oyama S, Zhang B, Luus L, Overland R, Nguyen S, Gu J, Kohli N, Wallace M, Feldhaus MJ, Kudla AJ, Schoeberl B, Nielsen UB. 2012. Anti-tumor activity of a novel bispecific antibody that targets the ErbB2/ErbB3 oncogenic unit and inhibits heregulin-induced activation of ErbB. Molecular Cancer Therapeutics 11:582–593.

Miller MA, Meyer AS, Beste MT, Lasisi Z, Reddy S, Jeng KW, Chen CH, Han J, Isaacson K, Griffith LG, Lauffenburger DA. 2013. ADAM-10 and -17 regulate endometriotic cell migration via concerted ligand and receptor shedding feedback on kinase signaling. Proceedings of the National Academy of Sciences U S A 110:E2074–E2083.

Miraldi ER, Sharfi H, Friedline RH, Johnson H, Zhang T, Lau KS, Ko HJ, Curran TG, Haigis KM, Yaffe MB, Bonneau R, Lauffenburger DA, Kahn BB, Kim JK, Neel BG, Saghatelian A, White FM. 2013. Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice. Integrative Biology 5:940–963.

NRC [National Research Council]. 2009. A New Biology for the 21st Century. Washington: National Academies Press. Available online at

Onishi K, Tonge PD, Nagy A, Zandstra PW. 2012. Microenvironment-mediated reversion of epiblast stem cells by reactivation of repressed JAK-STAT signaling. Integrative Biology 4:1367–1376.

Peterson RT, Macrae CA. 2012. Systematic approaches to toxicology in the zebrafish. Annual Review of Pharmacology and Toxicology 52:433–453.

Prat A, Ellis MJ, Perou CM. 2011. Practical implications of gene expression-based assays for breast oncologists. Nature Reviews Clinical Oncology 9:48–57.

Pritchard JR, Bruno PM, Hemann MT, Lauffenburger DA. 2013. Predicting cancer drug mechanisms of action using molecular network signatures. Molecular BioSystems 9:1604–1619.

Prudhomme W, Daley GQ, Zandstra PW, Lauffenburger DA. 2004. Multi-variate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling. Proceedings of the National Academy of Sciences U S A 101:2900–2905.

QSP Workshop Group [Quantitative and Systems Pharmacology Workshop Group]. 2011. Quantitative and systems pharmacology in the post-genomic era: New approaches to discovering drugs and understanding therapeutic mechanisms. Available online at A43411811AB1/0/SystemsPharmaWPSorger2011.pdf.

Roccio M, Gobaa S, Lutolf MP. 2012. High-throughput clonal analysis of neural stem cells in microarrayed artifical niches. Integrative Biology 4:391–400.

Roemer T, Boone C. 2013. Systems-level anti-microbial drug and drug synergy discovery. Nature Chemical Biology 9:222–231.

Roguev A, Talbot D, Negri GL, Shales M, Cagney G, Bandyopadhyay S, Panning B, Krogan NJ. 2013. Quantitative genetic-interaction mapping in mammalian cells. Nature Methods 10:432–437.

Schoeberl B, Pace EA, Fitzgerald JB, Harms BD, Xu L, Nie L, Linggi B, Kalra A, Paragras V, Bukhalid R, Grantcharova V, Kohli N, West KA, Leszczyniecka M, Feldhaus MJ, Kudla AJ, Nielsen UB. 2009. Therapeutically targeting ErbB3: A key node in ligand-induced activation of the ErbB receptor-PI3K axis. Science Signaling 2:ra31.

Shoichet BK. 2013. Drug discovery: Nature’s pieces. Nature Chemistry 5:9–10.

Soldatow VY, Lecluyse EL, Griffith LG, Rusyn I. 2013. In vitro models for liver toxicity testing. Toxicology Research 2:23–39.

Tatonetti NP, Ye PP, Daneshjou R, Altman RB. 2012. Data-driven prediction of drug effects and interactions. Science Translational Medicine 4:125ra31.

Venet D, Dumont JE, Detours V. 2011. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Computational Biology 7:e1002240.

Winter GE, Rix U, Carlson SM, Gleixner KV, Grebien F, Gridling M, Mueller AC, Breitweiser FP, Bilban M, Colinge J, Valent P, Bennett KL, White FM, Superti-Furga G. 2012. Systems pharmacology dissection of a drug synergy in imatinib-resistant CML. Nature Chemical Biology 8:905–912.

Wörsdörfer P, Thier M, Kadari A, Edenhofer F. 2013. Roadmap to cellular reprogramming: Manipulating transcriptional networks with DNA, RNA, proteins, and small molecules. Current Molecular Medicine 13:868–878.

Yee SW, Mefford JA, Singh N, Percival ME, Stecula A, Yang K, Witte JS, Takahashi A, Kubo M, Matsuda K, Giacomini KM, Andreadis C. 2013. Impact of polymorphisms in drug pathway genes on disease-free survival in adults with AML. Journal of Human Genetics 58:353–361.


1 “-Omic” refers to a highly multicomponent perspective on biology; e.g., “genomics” for the concomitant study of many genes, “proteomics” for the study of many proteins, “metabolomics” for the study of many metabolites, and so forth.

2 Information about the program is available on the DARPA website, at Systems.aspx.

About the Author:Douglas A. Lauffenburger (NAE) is Ford Professor of Biological Engineering, Chemical Engineering, and Biology and Head of the Department of Biological Engineering at the Massachusetts Institute of Biology. Kathleen M. Giacomini (IOM) is Professor and Chair of the Department of Bioengineering and Therapeutic Sciences at the University of California, San Francisco.