Systems biology ideally seeks to understand complex biological systems in their entirety by integrating all levels of functional information into a cohesive model. That stands in contrast to the reductionist approaches that became standard in the twentieth century, with biologists teasing out functional information on organisms one gene or one protein at

a time.

“Systems Biology! Incorporated” by Karl Thiel, Portland, Oregon - Nature Biotechnology, Volume 24,

Number 9, September 2006

Systems Biology at a conceptual level refers to an integrated view of a biological system for

which the individual components are well understood through research and related ap-

proaches. The term “Systems Biology” is quite often used interchangeably with terms like

“in-silico design” and “computational modeling and simulation” though they may in all cases

not indicate the same intent or context. While the definition of Systems Biology is well un-

derstood; the end application or usage context varies as it is confused with all aspects of

computational modeling deployed in drug discovery. The key distinction between bio-

informatics techniques and Systems Biology approach is that the former is targeted to repre-

sent existing or known data in an efficient structured way for further statistical and other

analysis. The Systems Biology approach is targeted towards predictive hypothesis genera-

tion and that is the key differentiator compared to other bioinformatics approaches.

Systems Biology or computational modeling or the in-silico approach can and is deployed in

different flavors. At the structural level – it enables screening of library of compounds

against biological targets to analyze and study drug docking and binding properties. Here

the objective is to screen or identify compounds which bind to the specified biological target

and falls in the domain of chemistry. At the clinical level which is at the animal or human

testing level – pharmacokinetic based modeling studies enable understanding of Absorp-

tion, Distribution, Metabolism and Excretion (ADME) properties of the drug in the context of

efficacy and associated toxicity. Different patient sub-types based studies or analysis can be

done to understand ADME variations impact for the drug. This approach enables determin-

ing the optimum concentration of drug for maximum efficacy with tolerable side-effects. At

the human clinical trial stage modeling is used for optimization of clinical trial programs with

the objective of reducing the complexity and size of the trial protocol.

At the molecular functional level – the Systems Biology flavor or abstraction level involves

identifying relevant cellular processes associated with disease of interest and then captur-

ing all functionality of protein level interactions. The functional relationships between these

proteins are represented through mathematics which is the key to convert this system from

a static map or atlas to a Global Positioning System. The functional relationship representa-

tion is what makes the system dynamic and really meaningful. This approach enables true

“target aware” drug discovery by allowing research teams access to a “intra” and “inter”

cellular processes integrated physiology aligned system for control and disease conditions.

This system can be assayed for significant nodes, understand and identify bio-markers, un-

derstand disease and drug mechanisms and the like. Also various “what-if” analyses can be

performed to guide and support in “in vitro” and “in vivo” experimentation. This “what-if”

analysis makes the system really predictive and allows new hypothesis to be generated and

qualified. For example when doing “what-if” knockout and/or knock-in (over-expression)

studies in traditional in vitro and in vivo systems; it is not practically feasible to do varied %

knock-out or knock-in studies. Another in vitro and in vivo system limitation is that all nodes

are not assayable and neither can be studied the integration of two cell-types in the context

of a disease.

The “omics” (genomic and proteomics) approaches are a high-throughput technique which

enables identification of key molecular endpoints which are associated with disease patho-

genesis and progression. These molecular endpoints translate into biological drug targets

and bio-markers for toxicity, drug efficacy and monitoring during clinical trials. The omics

approach enables a major productivity improvement in the “target discovery and research”

phase of drug discovery. But knowing the potential constituent molecular components does

not enable direct mapping to disease and drug mechanism of action which is needed to

predict relevant bio-markers. The Systems Biology approach leverages the information gene-

rated through the omics approach in identifying the key players involved and along with the

S Y S T E M S B I O L O G Y

A P P L I C A T I O N S 101010111

Another contributing factor is that many companies tend to pursue the same targets, owing to the lack of good drugable and validated targets, with the result that if the target fails the collective loss of resources across the industry is substantial. With the physiology-based approach, this risk is probably lower because companies have different

starting points in the

chemical structures used, and even if the compounds are screened in the same complex models to obtain the same physiological effects the drug programs in different companies will develop in

different directions.

Frank Sams-Dodd, DDT • Volume 10, Number 2 •

January 2005

Cellworks Group Inc.

We, at FDA, envision

progress in medical

product development as

having new predictive

tools to identify early

those product candidates

of greatest efficacy

against molecular and

biological processes and

new evaluative tools to

improve the performance

of clinical trials and

treatment choices.

- FDA Critical Path Report

2006

Physiology aligned Systems Biology or “in-silico” platforms can be deployed for a wide variety of applications across the

drug discovery process. This is analogous to having “virtual cell lines” customizable for specific/relevant diseases which

can be used as a virtual experimental system (VES).

Traditional experimental systems have limited transparency due to practical limitations. Activation of important nodes is

caused by various levels of the trigger. Such activation raises the levels of the node, but due to experimental limitations

these are undetectable. Using virtual experimental system the key sensitive nodes to be assayed through “in-vitro” means

can be pruned down from the big maze. Having a predictive system aligned to human physiology greatly helps to qualify

and streamline hypothesis, which will lead to much reduced experimentation for target qualification. This also enables to

get a much better insight into the biology related toxicity landscape at the initial stages of discovery before much more $

are spent in advancing the molecule.

Listed below are the key applications of these “in-silico” platforms in context of the different discovery process stages.

Based on discovery project type – New Molecular Entity (NME), NCE (New Chemical Entity) or New Drug Formulation (NDF)

the discovery stages will vary.

A P P L I C A T I O N S O V E R V I E W

2 S Y S T E M S B I O L O G Y A P P L I C A T I O N S 1 0 1

mechanistic protein level interaction data; ties this information into an integrated transparent view. This approach of Sys-

tems Biology is a dynamic in-silico view of the integrated metabolic and bio-chemical pathways showing the interaction of

the different biological components like proteins/enzymes/receptors/adaptors/transcription factors etc. in the system.

The relationship between the biological components also includes all the cross-talk and known feedback paths in the sys-

tem and the relationship is represented using mathematics. This system is analogous to a “virtual experimental system”

which can be used in parallel to “in-vitro” and “in-vivo” studies but with the added capability of transparency and visibility

into every node in the system.

3

Target ID & Analysis: This involves identifying the biological target/receptor which upon manipulation shows the desired response at the biological end-point with minimum biological toxicity effect. The VES can also be used to analyze a cou-ple of different target options which cause the same effect at the physiological endpoint but through different mecha-

nisms and corresponding implications can be understood.

Biology Based Toxicity Analysis: Toxicity understanding of a given molecule is a very important consideration factor for its approval and adoption. At a broad level toxicity is due to two main reasons – Biology and Chemistry. The chemistry toxic-ity is related to non-specific drug binding to other than desired receptors and causing some undesired effect. It is also caused due to process of drug pharmacokinetic properties related to absorption; distribution etc. Systems Biology based platforms enables analysis of toxicity related to biology. Since a complete integrated transparent view of the system is available, one can look at the impact of manipulation of a particular receptor on other nodes and endpoints in the sys-

tem and understand possible toxicity effects.

Cumulative toxicity studies would be a combination of understanding the biology and chemistry impact in the system.

Drug Mechanism Understanding: VES can be used for understanding the mechanism and pathways through which the

drug impacts the relevant clinical endpoint.

Virtual Cell-Line: Similar to how cell-lines are used for experimental “in-vitro” work, the virtual cell-lines are “in-silico” models of cell types which may be customized for specific disease types and used as a “virtual experimental system” in

conjunction with the traditional in-vitro experiments.

Bio-markers Identification and Cell Assays: The VES can be configured for disease state and control state and analyzed for with and without drug under various states of disease. Also different types of disease triggers can be implemented in the platform. Based on these different configurations detailed sensitivity analysis can be performed to understand key

bio-markers for different conditions.

ADME Interface: Through ADME studies one can determine the concentration of the drug at the plasma level and ap-proximately at the receptor level. This information can be back-annotated in the disease platform to show dose-drug re-

sponse analysis at the receptor level.

Adaptive Clinical Program Design: Current clinical protocol designs do not support built-in adaptation when unpredicted side-effects and efficacy data are discovered. This causes the clinical design changes in the middle of trials. Using these platforms, one can design clinical programs which can pre-emptively plan based on bio-markers what can go wrong and

correspondingly build in a decision tree in the protocol design.

Drug Re-Purposing Opportunities Identification: With an integrated transparent physiologically aligned VES – one can identify other potential opportunities for a given drug action across diseases. Many block-buster drugs in the market were originally created for a different application than the one for which it got finally approved and deployed for. Also on similar lines combination drug therapy can be planned out to maximize efficacy and reduce side-effects for a given drug

candidate.

C E L L W O R K S S O L U T I O N S

Cellworks provides a comprehensive range of disease focused drug discovery and research ap-plications and solutions to empower every aspect of your research. These solutions enable “Predictive” and “What-If” Analysis to identify early those product candidates of greatest efficacy

against molecular and biological processes.

Using this approach you gain the visibility to pinpoint inefficiencies in selected target and com-pound – and the capabilities to transform them into competitive advantage. The foresight to identify new opportunities; the agility to change directions and the functionality to optimize your

discovery flow.

Cellworks current disease focus area includes – Oncology, Inflammation (Rheumatoid Arthritis and Inflammatory Bowel Disease), Diabetes Type 2, Skin pigmentation, Uterine-Endocrine envi-

ronment (Pre-Term Labor) and Parkinson’s Disease.

We, at FDA, envision

progress in medical

product development as

having new predictive

tools to identify early

those product

candidates of greatest

efficacy against

molecular and

biological processes

and new evaluative

tools to improve the

performance of clinical

trials and treatment

choices.

- FDA Critical Path

Report 2006

4 S Y S T E M S B I O L O G Y A P P L I C A T I O N S 1 0 1

D I S E A S E P R O G R E S S I O N ( M I L D , M O D E R A T E A N D S E V E R E ) A N D A S S O C I A T E D

A N A L Y S I S

In a virtual experimental system, an initial trigger sets off the disease process. The many responses of a host to a disease

process occur on markedly different time frames ranging from seconds to years (e.g. the recurrent fevers and cycles hemo-

lytic anemias associated with specific types of malaria). Uniquely, the Cellworks Virtual Experimental System can repre-

sent these temporally discontinuous responses to diseases. The underlying technology permits tracking of multiple conver-

gent and divergent signaling pathways and cellular responses that can be adjusted to obtain different severity levels of the

disease. For examples LPS or Anti-CD3 antibodies can be used to trigger inflammation in a RA (Rheumatoid Arthritis) dis-

ease model. Also once triggered, having a visibility of all the nodes and pathways enables analysis of which pathways are

sequentially activated and which nodes (key cytokines, kinases, transcription factors etc) are significantly varying as the

disease progresses from mild to severe state. This allows predictive identification of biomarkers in the system and a bet-

ter understanding of the disease physiology.

U N D E R S T A N D I N G O F D R U G M E C H A N I S M

In the CW computational virtual experimental model, influence of pathways or key nodes can be enhanced or knocked out

with ease and semi-quantitative trends on endpoints can be monitored.

CASE STUDY 1 – Drug A at it’s highest conc., inhibits NFkB node by ~20%, while validation end points like IL-1 β and TNF-α

and a host of other pro-inflammatory cytokines and chemokine levels are more dramatically reduced than can be ex-

plained by Drug A’s direct effect on NFkB. How does inhibition of NFkB by 20% lead to such a marked reduction in these

cytokines and chemokines?

Analysis – A dynamic set of signaling pathways represented in the Cellworks Virtual Experimental System demonstrate that

IL1 and TNF are inhibited at the transcription level by NFkB, and additional transcription factors including STAT6, STAT3,

GR (Glucocorticoid receptor) complex and PPAR gamma. Drug A increases levels of GR complex by more than 10-fold,

while levels of STAT6 are increased by 35.15%. Thus, glucocorticoids produce effects on additional signaling pathways that

converge with NFkB to produce a greater than anticipated inhibition of IL1 and TNF transcriptional expression.

CASE STUDY 2 – Doxorubicin (trade name Adriamycin) or hydroxyldaunorubicin is a DNA-interacting drug widely used in

chemotherapy. The exact mechanisms of action of doxorubicin are complex and cell type-dependent. Doxorubicin is known

to interact with DNA by intercalation and inhibition of macromolecular biosynthesis. Also known is that it inhibits the en-

zyme topoisomerase II. The Cellworks virtual experimental system can be used to examine the regulation of levels of mes-

senger RNA and proteins as affected by doxorubicin. In a hepatocyte cell line, doxorubicin (5uM) reduces the CyclinD1

levels by 2-fold. Doxorubicin (0.5uM), in combination with 25uM Celecoxib (Celebrex) reduces cyclinD1 levels by the same

amount. The above results were derived from the virtual experimental system with perturbation at the level of NFkB medi-

ated transcription and predicts that combining low doses of celebrex and doxorubin synergistically produces the same de-

sired anti-proliferative affect while reducing the potential side-effects of either drug.

I D E N T I F I C A T I O N O F M A S T E R N O D E S / T A R G E T S I N D I S E A S E A N D D R U G - I N D U C E D

C O N D I T I O N S

Using Cellworks virtual experimental system one can manipulate a node and observe its impact on the functionality of the

pathway and all associated nodes in the cross talk.

Like a traditional experimental setup based on cell lines and animal studies, the virtual ex-

perimental system supports experimental protocols like assay of nodes, knockout studies,

high-throughput aligned endpoints analysis based on disease trigger or drugs and associ-

ated analysis. Being a virtual experimental system – it does not come with the inherent limi-

tations of traditional ‘in-vitro’ and ‘in-vivo’ systems. The entire system is transparent so all

nodes in the platform can be assayed. Also the virtual cell system being a computational

model allows easy manipulation of any target node, creation of disease trigger condition etc.

Listed below are some examples of questions that can be posed and answered utilizing

simulations generated by the Cellworks Virtual Experimental System.

Systems biology is essential if we are ever to make sense of biological complexity, as intuitive ‘conceptual’ models quickly reach their limits beyond simple linear

pathways.

Nature Reviews – Molecular Cell Biology,

November 2006

V I R T U A L E X P E R I M E N T A L S Y S T E M — Q U E S T I O N S &

A N A L Y S I S

5

For example – Which cytokine levels would get affected by manipulation of a specific pathway? Which key nodes are sig-

nificantly changing under different conditions? This would be analogous to a genomic or proteomics dataset but a more

relevant subset since only the pathways and proteins involved in the specific disease process under investigation are in-

cluded in the system. This eliminates a lot of the noise one generally encounters in the high throughput data analysis.

P R E D I C T I O N O F C H A N G E S I N C Y T O K I N E S ( E G . , W O U L D T A R G E T I N G T N F R E D U C E

L E V E L S O F I N F L A MM A T O R Y M A R K E R S , S U C H A S C R P / E S R , I L 6 , E T C . –

Such questions and hypothesis can be tested in the virtual experimental system.

U N D E R S T A N D I N G O F A S S O C I A T E D C R O S S T A L K T O U N D E R S T A N D T O X I C I T Y A N D

P O S S I B L E R E A S O N S F O R D I S E A S E R E L A P S E

CASE STUDY - A20 plays a critical role in regulating inflammation. Rapid expression of A20 is absolutely essential for down-

regulating the inflammatory response and averting the damage unrestrained inflammation can cause in different tissues.

Using Cellworks virtual experimental system the study of Drug A which inhibits NF-kB, shows the level of A20 reduced in

the system, thereby affecting the negative regulation of TNF-alpha, TLRs and IL1. This shows that in the presence of Drug

A, normal regulatory molecule gets affected.

I N D I V I D U A L C O N T R I B U T I O N O F V A R I O U S F A C T O R S T O W A R D S T H E L E V E L S O F A N

E N D - P O I N T

CASE STUDY - IL-4 produced in T cell alone is taken as the end-point. IL-4 is transcribed by NFATC2, NFKB and AP-1 and its

production is inhibited by STAT5. The virtual experimental system under diseased state can be simulated under the follow-

ing conditions and the concentration of IL-4 and results collated:

i. NFATC2 alone transcribing IL-4

ii. NFKB alone transcribing IL-4

iii. AP-1 alone transcribing IL-4

iv. All the three transcription factors NFATC2, NFKB and AP-1 together transcribing IL-4

v. All the three transcription factors NFATC2, NFKB and AP-1 together transcribing IL-4 and STAT inhibiting the transcrip-

tion

Contribution of NFATC2 to IL-4 levels = (0.034/0.04)*100 = 85%

Contribution of NFKB to IL-4 levels = (0.003/0.04)*100 = 7.5%

Contribution of AP-1 to IL-4 levels = (0.00325/0.04)*100 = 8.125%

% inhibition of STAT5 on the IL-4 levels generated by the transcription factors NFATC2, NFKB and AP-1 = ((0.04-

0.0325)/0.04)*100 = 18.75%

P R U N I N G D O W N O F K E Y A S S A Y S T O P E R F O R M I N - V I T R O B A S E D O N V I R T U A L A S -

S A Y I N G O F A L L N O D E S

Traditional experimental systems have limited transparency due to practical limitations. Activation of important nodes is

caused by various levels of the trigger. Such activation raises the levels of the node, but due to experimental limitations

these are undetectable. Using Cellworks virtual experimental system the key sensitive nodes to be assayed through “in-

vitro” means can be pruned down from the big maze.

Having a predictive system aligned to human physiology greatly helps to qualify and streamline hypothesis, which will lead

to much reduced experimentation for target qualification. This also enables to get a much better insight into the biology

related toxicity landscape at the initial stages of discovery before much more $ are spent in advancing the molecule.

FACTORS NFATC2 NF-kB AP-1 NF-kB+NFAT+AP1 NF-kB+NFAT+AP1

+ STAT5

CONCENTRATION OF

IL-4 (uM) 0.034 0.003 0.00325 0.04 0.0325

P R O F I L I N G O F D R U G A C T I O N S O N T H E W H O L E S Y S T E M I N T E R M S O F E F F I C A C Y

A N D T O X I C I T Y

CASE STUDY - The goal in Diabetes is to reduce plasma glucose. Glucose is phosphorylated by Glucokinase (GK) present in

the liver and the pancreatic beta-cell. Activating GK reduces plasma glucose. Some drugs act only on the liver GK, while

some act at both locations. Drugs acting only on liver GK do not increase glucose uptake in other insulin-sensitive tissues

while drugs acting at both locations also activate beta-cell insulin release besides increasing liver glucose uptake. This

increases glucose uptake in the skeletal muscle and adipocytes and other insulin sensitive tissues also. Using Cellworks

Virtual Experimental System – drug actions can be analyzed and compared in context of efficacy and toxicity for two differ-

ent drugs (Drug A and Drug B) at their maximum concentration of 10 uM.

I D E N T I F I C A T I O N O F A C T I V A T O R S / I N H I B I T O R S F O R M A S T E R N O D E S

HIF-1alpha is a master node which can be controlled by several factors like p38MAPk which phosphorylates the former

and ERK1/2, AMPK, AKT help in mTOR regulation which regulates HIF. Using the Cellworks virtual experimental system

one can predict a relative contribution of several activators and inhibitors converging at the same node and directly im-

pacting the master node or by affecting some node slightly upstream and thereby indirectly impacting it.

C O N T R O L V S . D I S E A S E S I G N I F I C A N T N O D E S U N D E R S T A N D I N G

The transparency of Cellworks virtual experimental system allows all nodes in the system to be assayed. The system can

be configured in normal physiological mode and disease condition by adding appropriate disease trigger. Comparison of

corresponding nodes in the two systems can then be made and % variation tracked between control and disease condi-

tion. This allows significant nodes to be identified.

A N A L Y S I S O F S E Q U E N T I A L T U R N I N G O N O F D I F F E R E N T P A T H W A Y S A S T H E T R I G -

G E R I S T U R N E D O N

Not all cascades operate at the same time point despite being influenced by a common trigger. There is usually a temporal

difference in activation of various cascades, the significance of which can be attributed to the biological end-points associ-

ated with each.

For example: UVB acts as a trigger leading to two key biological end-points- Inflammation or erythema and melanogenesis.

Erythema is an early response and requires the activation of the oxidative stress pathway, NFkB activation mediated via

DNA damage and induction of TNF-α and IL-1/IL6. With a round of UVB stimulus, the above mentioned pathways are acti-

vated within a matter of few minutes. However, with the same UVB trigger, the induction of melanogens like alpha MSH

takes on an average 3-7 days depending on the skin types. This behavior is extremely important in view of therapeutic in-

terventions and can be studied using the Cellworks virtual experiment system.

D I F F E R E N T I A L B I N D I N G K I N E T I C S D U E T O D I F F E R E N T I S O F O R M S I N P A T I E N T S U B -

T Y P E S

In disease like Rheumatoid Arthritis, TNF-α levels in the system vary across patients. Some patients with RA have very low

levels of TNF-α. This could be due to differential quenching capabilities in the different patients. This can be represented

in our virtual experimental system through differential binding kinetics of TNF-α to its receptors. If TNF-α has higher bind-

ing affinity to its receptor, the concentration of assayed TNF-α is lower. The higher binding affinity will also affect the end-

points in the system which can be studied easily. We can have variant systems with differential binding kinetics represent-

ing different patient subtypes for analysis. Different mutations of receptors and oncogenes can also be represented in the

system.

R E L E V A N C E O F O V E R - E X P R E S S I O N S T U D I E S

CASE STUDY - The remodeling of the extracellular matrix (ECM) is crucial for the proper development and maintenance of

cellular structure. Controlled degradation of proteinaceous compounds in the ECM is mediated largely through matrix met-

alloproteinases (MMPs) and their inhibitors -- tissue inhibitors of metalloproteinases (TIMPs). Proper balance of MMPs

6 S Y S T E M S B I O L O G Y A P P L I C A T I O N S 1 0 1

Cases Reduction in Glucose (efficacy) Increase in plasma Triglycerides (Potential Side effects)

Drug A (Activates Liver GK) 22% 36 % reduction

Drug B (Activates Liver GK and Insulin) 67% 257 % increase

and TIMPs is essential for normal cellular function. Over expression studies with these molecules using Cellworks virtual

experimental system, have demonstrated that proper control of MMP activity is required. It is well known that there is very

tight stoichiometric control between TIMP2, MT1-MMP, ProMMP2 in 1:1:1 ratio. TIMPs together with MT-MMPs also facili-

tate the activation of some MMPs like MMP9. Therefore, any quantitative difference in TIMPs can drastically impact a sys-

tem thereby pushing it from a normal physiological condition to a full blown diseased condition of metastasis & abnormal

phenotypes. Ratios of nodes can be assayed in the system.

K N O C K O U T S T U D I E S

In about 60% of malignant gliomas, PTEN (Phosphatase and Tensin homologue) is mutated with a loss of function. Using

the virtual experimental system one can evaluate the tonic activation of Akt with various levels of PTEN knockouts. AKT

tonic activation is directly responsible for mTOR mediated HIF-1 activation and results in increased transcription of genes

leading to altered glycolysis and enhanced substrate and product transport in transformed cells. Graded knockout analysis

helps examine the role of key nodes in cancer initiation and progression.

C O M B I N A T O R I A L D R U G / C O M P O U N D A N A L Y S I S

A multi-drug regimen can be designed and implemented for disease remission or for circumvention of drug associated tox-

icities. Using the virtual experimental system, such scenarios can be studied and analyzed.

For example, inhibitors targeting two different pathways affecting the same endpoint can be used at lower concentrations

and combination is more synergistic and effective than a single inhibitor targeting only one pathway. Another example case

study could be combination drugs such as an anti-proliferative drug targeting cell cycle regulation in combination with anti-

metastatic or anti-inflammatory agents.

Using one drug to restore the programmed cell death mechanism or eliminate the proliferation mechanism and a second

drug to trigger the process might reduce or eliminate chemotherapy-resistance and be an effective strategy for treating

cancer. In another case, enhanced reliance on non-oxidative glycolysis by cancer cells having PTEN mutations has been

modeled. The simulations predict that cancer cells having PTEN mutations would have enhanced sensitivities to agents

that activate AMPK. AMPK, when activated, inhibits mTOR and ERK and blunt transcriptional activation by HIF to counter-

act the tonic activation of AKT produced by the PTEN mutation. Furthermore, the simulations predict that reduced glycoly-

sis can lead to AMPK activation with increased resistance to small molecule inhibitors of EGFR.

The underlying informational storage structure of the Cellworks platform permits optimization of inhibition of multiple sig-

naling pathways such that a multi-drug regimen can be proposed for pre-clinical testing that employ multiple existing phar-

macological agents in a unique way.

7

Cellworks System Biology Initiative (CSBI) mission is to be the hub for R&D and deployment of Systems Biology based

applications in the domain of drug discovery. The mission reach is global through collaborations and partnerships. The

eco-system consists of pharmaceutical and biotechnology companies, leading international academic institutions, govern-

ment representatives and regulatory bodies, Venture Capitalists and Investment Funds and country specific industry bod-

ies.

C E L L W O R K S S Y S T E M B I O L O G Y I N I T I A T I V E ( C S B I )

Cellworks Group Inc.Cellworks Group Inc.Cellworks Group Inc.Cellworks Group Inc.

Cellworks develops and provides technology based solutions to enable drug discovery research and development

(R&D). This approach is based on Systems Biology and involves development of a disease physiology aligned

“virtual experimental system”. This system is a representation of all the different cellular processes relevant to the

disease and associated assayable endpoints at the cell signaling and biochemical pathways level. The develop-

ment is bottom-up methodology based in which the relevant proteins, enzyme, receptor, adaptors and chaper-

ones; functionality and interaction are modeled and the relationships represented computationally using mathe-

matics. The complete disease specific platform is an integrated transparent view of human cellular physiology

available as a virtual experimental system.

The research and development leverages a combination of published literature on node level data and alignment

of system with drug data and clinical endpoints. This technology provides an integrated transparent view of the

physiological system with many applications to address drug discovery challenges.

8 S Y S T E M S B I O L O G Y A P P L I C A T I O N S 1 0 1

Corporate Headquarters

Cellworks Group Inc.

13962 Pierce Road

Saratoga

California 95070

Phone: 408-406-7156

www.cellworksgoup.com

C E L LW O R K S G R O U P I N C .

R&D Center

Cellworks Group Inc.

#303, A Block, 3rd Floor

60 Feet Road, AECS Layout

Marathahalli Post

Bangalore 560 037

Phone: +91-80-41158733

Fax: +91-80-41158734

The core component of this initiative is hands-on training programs and disease aligned virtual experimental system kits

which are made available to the initiative members. SBE-101 is one of the entry level training modules with focus on

usage and applications of this approach to disease physiology understanding and drug discovery.