About Us

Board of Directors

Thomas Zawacki
Thomas Zawacki
Director of Rocket Fuel Inc.
Thomas ZawackiRocket Fuel Inc. An accomplished business leader, motivator and innovator with a successful track record in both established and...
John Edwards
John Edwards
Director of Breed’s Hill Capital, LLC
John EdwardsBreed’s Hill Capital, LLC John Edwards is the President & CEO of Breed's Hill Capital, LLC that provides tailored wealth...
V.A. Shiva Ayyadurai
V.A. Shiva Ayyadurai
Chairman and Chief Executive Officer, CytoSolve, Inc.
Dr. V.A. Shiva Ayyadurai, Ph.D., an MIT systems scientist is the inventor of email and creator of EchoMail. He is also Chairman & CEO of...
Sonu Mathews Abraham
Sonu Mathews Abraham
Sonu Abraham brings to the board his wealth of experience in the fields of corporate governance, administrative management

Scientific Advisory Board

Anirban Maitra
Anirban Maitra
Chairman Scientific Advisory Board; Co-director and Scientific Director, Sheikh Ahmed Bin Zayed Al Nahyan Center for Pancreatic Cancer Research, MD Anderson
Dr. Anirban Maitra is Professor of Pathology and Translational Molecular Pathology at MD Anderson Cancer Center.
V.A. Shiva Ayyadurai
V.A. Shiva Ayyadurai
Chairman and Chief Executive Officer, CytoSolve, Inc.
Dr. V.A. Shiva Ayyadurai, Ph.D., an MIT systems scientist is the inventor of email and creator of EchoMail. He is also Chairman & CEO of...
Sunil Krishnan
Sunil Krishnan
Director of the Center for Radiation Oncology Research, MD Anderson
Dr. Sunil Krishnan is Professor in the Department of Radiation Oncology, Division of Radiation Oncology at The University of Texas MD Anderson...

About Us

CytoSolve, Inc. was founded in 2011 by Dr. V.A. Shiva Ayyadurai, Ph.D., an MIT systems scientist, the Inventor of email and creator of EchoMail. CytoSolve has developed the world’s first computational platform for scalable integration of molecular pathway models. The platform provides an important contribution to the field of Systems Biology. Using CytoSolve™, complex molecular pathway models have been tested and built for EGFR, nitric oxide and Interferon (IFN) pathways. Without CytoSolve™, the integration of complex molecular pathway models is largely manual, time-consuming and in many cases, not possible.

At CytoSolve, Inc., we use our platform for drug development in the fields of multiple disease areas. The platform is also used for measuring the efficacy of health supplements and ‘nutraceuticals’. We provide opportunities for commercial companies such as biotech and pharmaceutical companies to work with us to use the CytoSolve™ platform for supporting their internal drug development process.

Our Publications

Information about development of CytoSolve™ has evinced keen interest among the research community world-wide. A number of papers have been written on the CytoSolve™ platform and its components. Researchers in the fields of computational biology and development of drug and treatment plans are using CytoSolve™ to build their thesis.

You can view and read papers published on CytoSolve™ and papers published by other researchers that cite CytoSolve™ on this page.

Papers

In-Silico Analysis & In-Vivo Results Concur on Glutathione Depletion in Glyphosate Resistant GMO Soy, Advancing a Systems Biology Framework for Safety Assessment of GMOs
This study advances previous efforts towards development of computational systems biology, in silico, methods for biosafety assessment of genetically modified organisms (GMOs). C1 metabolism is a critical molecular system in plants, fungi, and bacteria. In our previous research, critical molecular systems of C1 metabolism were identified and modeled using CytoSolve?, a platform for in silico analysis. In addition, multiple exogenous molecular systems affecting C1 metabolism such as oxidative stress, shikimic acid metabolism, glutathione biosynthesis, etc. were identified. Subsequent research expanded the C1 metabolism computational models to integrate oxidative stress, suggesting glutathione (GSH) depletion. Recent integration of data from the EPSPS genetic modification of Soy, also known as Roundup Ready Soy (RRS), with C1 metabolism predicts similar GSH depletion and HCHO accumulation in RRS. The research herein incorporates molecular systems of glutathione biosynthesis and glyphosate catabolism to expand the extant in silico models of C1 metabolism. The in silico results predict that Organic Soy will have a nearly 250% greater ratio of GSH and GSSG, a measure of glutathione levels, than in RRS that are glyphosate-treated glyphosate-resistant Soy versus the Organic Soy. These predictions also concur with in vivo greenhouse results. This concurrence suggests that these in silico models of C1 metabolism may provide a viable and validated platform for biosafety assessment of GMOs, and aid in selecting rational criteria for informing in vitro and in vivo efforts to more accurately decide in the problem formulation phase whose parameters need to be assessed so that conclusion on “substantial equivalence” or material difference of a GMO and its non-GMO counterpart can be drawn on a well-grounded basis.

Do GMOs Accumulate Formaldehyde and Disrupt Molecular Systems Equilibria? Systems Biology May Provide Answers
Safety assessment of genetically modified organisms (GMOs) is a contentious topic. Proponents of GMOs assert that GMOs are safe since the FDA’s policy of substantial equivalence considers GMOs “equivalent” to their non-GMO counterparts, and argue that genetic modification (GM) is simply an extension of a “natural” process of plant breeding, a form of “genetic modification”, though done over longer time scales. Anti-GMO activists counter that GMOs are unsafe since substantial equivalence is unscientific and outdated since it originates in the 1970s to assess safety of medical devices, which are not comparable to the complexity of biological systems, and contend that targeted GM is not plant breeding. The heart of the debate appears to be on the methodology used to determine criteria for substantial equivalence. Systems biology, which aims to understand complexity of the whole organism, as a system, rather than just studying its parts in a reductionist manner, may provide a framework to determine appropriate criteria, as it recognizes that GM, small or large, may affect emergent properties of the whole system. Herein, a promising computational systems biology method couples known perturbations on five biomolecules caused by the CP4 EPSPS GM of Glycine max L. (soybean), with an integrative model of C1 metabolism and oxidative stress (two molecular systems critical to plant function). The results predict significant accumulation of formaldehyde and concomitant depletion of glutathione in the GMO, suggesting how a “small” and single GM creates “large” and systemic perturbations to molecular systems equilibria. Regulatory agencies, currently reviewing rules for GMO safety, may wish to adopt a systems biology approach using a combination of in silico, computational methods used herein, and subsequent targeted experimental in vitro and in vivo designs, to develop a systems understanding of “equivalence” using biomarkers, such as formaldehyde and glutathione, which predict metabolic disruptions, towards modernizing the safety assessment of GMOs.

Services-Based Systems Architecture for Modeling the Whole Cell: A Distributed Collaborative Engineering Systems Approach
Modeling the whole cell is a goal of modern systems biology. Current approaches are neither scalable nor flexible to model complex cellular functions. They do not support collaborative development, are monolithic and, take a primarily manual approach of combining each biological pathway model’s software source code to build one large monolithic model that executes on a single computer. What is needed is a distributed collaborative engineering systems approach that offers massive scalability and flexibility, treating each part as a services-based component, potentially delivered by multiple suppliers, that can be dynamically integrated in real-time. A requirements specification for such a services-based architecture is presented. This specification is used to develop CytoSolve, a working prototype that implements the services-based architecture enabling dynamic and collaborative integration of an ensemble of biological pathway models, that may be developed and maintained by teams distributed globally. This architecture computes solutions in a parallel manner while offering ease of maintenance of the integrated model. The individual biological pathway models can be represented in SBML, CellML or in any number of formats. The EGFR model of Kholodenko with known solutions is first tested within the CytoSolve framework to prove it viability. Success of the EGFR test is followed with the development of an integrative model of interferon (IFN) response to virus infection using the CytoSolve platform. The resulting integrated model of IFN yields accurate results based on comparison with previously published in vitro and in vivo studies. A open web-based environment for collaborative testing and continued development is now underway and available on www.cytosolve.com. As more biological pathway models develop in a disparate and decentralized manner, this architecture offers a unique platform for collaborative systems biology, to build large-scale integrative models of cellular function, and eventually one day model the whole cell.

Multiscale Mathematical Modeling to Support Drug Development
It is widely recognized that major improvements are required in the methods currently being used to develop new therapeutic drugs. The time from initial target identification to commercialization can be 10–14 years and incur a cost in the hundreds of millions of dollars. Even after substantial investment, only 30–40% of the candidate compounds entering clinical trials are successful. We propose that multiscale mathematical pathway modeling can be used to decrease time required to bring candidate drugs to clinical trial and increase the probability that they will be successful in humans. The requirements for multiple time scales and spatial scales are discussed, and new computational paradigms are identified to address the increased complexity of modeling.

Pericytes of the neurovascular unit: key functions and signaling pathways
Pericytes are vascular mural cells embedded in the basement membrane of blood microvessels. They extend their processes along capillaries, pre-capillary arterioles and post-capillary venules. CNS pericytes are uniquely positioned in the neurovascular unit between endothelial cells, astrocytes and neurons. They integrate, coordinate and process signals from their neighboring cells to generate diverse functional responses that are critical for CNS functions in health and disease, including regulation of the blood–brain barrier permeability, angiogenesis, clearance of toxic metabolites, capillary hemodynamic responses, neuroinflammation and stem cell activity. Here we examine the key signaling pathways between pericytes and their neighboring endothelial cells, astrocytes and neurons that control neurovascular functions. We also review the role of pericytes in CNS disorders including rare monogenic diseases and complex neurological disorders such as Alzheimer’s disease and brain tumors. Finally, we discuss directions for future studies.

In Silico Modeling of Shear-Stress-Induced Nitric Oxide Production in Endothelial Cells through Systems Biology
Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.

OREMPdb: A Semantic Dictionary Of Computational Pathway Models
The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web.

CytoSolve™: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models
A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer.

A Distributed Computational Architecture for Integrating Multiple Biomolecular Pathways
Biomolecular pathways are building blocks of cellular biochemical function. Computational biology is in rapid transition from diagrammatic representation of pathways to quantitative and predictive mathematical models, which span time-scales, knowledge domains and spatial-scales. This transition is being accelerated by high-throughput experimentation which isolates reactions and their corresponding rate constants.

Integrating an Ensemble of Distributed Biochemical Network Models
A new system for integrating an ensemble of distributed biochemical network models is presented. Rapid growth in the number of biochemical network models, created in different formats, across different computing systems, with minimal input and output information, necessitates the need for such a system in order to build large scale models in a flexible and scalable manner.

Citations

Nature Publishing Group – Combinatorial Drug Therapy for Cancer
Computational protocols, such as CytoSolve, allow the combination of alternative models and generation of consensus hypotheses.

Setting a Research Agenda for Progressive MS: The International Collaborative on Progressive MS
Indeed, computational biology is shifting from diagrammatic representation of pathways to mathematical models. These techniques hold promise to provide the tools for interpreting genetic data across different knowledge domains.

Contribution of Genome-Wide Association Studies to Scientific Research: A Pragmatic Approach to Evaluate Their Impact
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets.

The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models
The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.