Laboratory of Systems Pharmacology at Harvard https://hits.harvard.edu/the-program/laboratory-of-systems-pharmacology/research-program/

Research Program

The LSP is an NIH National Center for Systems Biology and an NIH LINCS Project Data and Signature Generation Center... LSP Investigators are also involved in collaborations with industry partners that apply a combination of multiplex measurement and computational modeling to understand mechanisms of drug action.

• The HMS Laboratory of Systems Pharmacology

The Harvard Medical School (HMS) Laboratory of Systems Pharmacology (LSP) is a NIGMS Center for Systems Biology multi-disciplinary effort within HiTS to reinvent the fundamental science underlying the development of new medicines and their use in individual patients.

• Pharmaco-Response Signatures & Disease Mechanism – The HMS LINCS Center

The Harvard Medical School Library of Integrated Network-based Cellular Signatures (LINCS) Center was established in October 2010 to create libraries of measurable cellular responses or signatures that describe how cells respond to perturbation. As of March 2017, six centers across the U.S. participate in the NIH LINCS program. The aim of the HMS LINCS Center is to create signatures that measure the responses of cells derived from different human tissues to therapeutic drugs. Much of the work focuses on tumor cells (from breast, liver and colon), but we also study primary human cells from normal and diseased patients. As perturbing agents, our focus is on small molecule kinase inhibitors, which are a leading class of therapeutic agents for treatment of cancer, autoimmune and other diseases.

The HMS Center for Cancer Systems Pharmacology – CCSP

The HMS Center for Cancer Systems Pharmacology (CCSP Center) is a NCI Cancer Systems Biology Consortium Center that constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). By systematically dissecting how resistance to targeted therapies and ICIs arises, we aim to understand and overcome resistance mechanisms using new drugs or drug combinations, while simultaneously predicting and balancing potential toxicities.

• Communicating with Computers: Active Context

The DARPA Communicating with Computers (CwC) program develops technologies for a new generation of human-machine interaction in which machines act as proactive collaborators rather than merely problem solving tools.

• World Modelers: Global Reading and Assembly for Semantic, Probabilistic World Models

The DARPA World Modelers program aims to develop automated information collection and computational modeling techniques to understand the complex dynamics of global processes such as food security, migration and public health. We are developing the INDRA-GEM (Integrated Network and Dynamical Reasoning Assembler for Generalized Ensemble Modeling) automated model assembly system, which integrates information from diverse sources and implements novel probabilistic assembly techniques that can account for the uncertain nature of information in models.

• An Automated Scientific Discovery Framework (ASDF) for Mechanistic Reasoning Across Complex Data

The DARPA Automated Scientific Discovery Framework program (ASDF) will develop algorithms and software for reasoning about complex mechanisms operating in the natural world, explaining large-scale data, assisting humans in generating actionable, model-based hypotheses and testing these hypotheses empirically.

2015 Research Statement http://www.pitt.edu/~liubing/research.pdf

Given this outlook, the central theme of my research is on the development of computational modeling

and analysis techniques to study biological systems at the systems level. My work builds mathematical

models to describe biological systems and employs artificial intelligence and formal verification techniques

to analyze their dynamical behaviors. I use probabilistic frameworks to address the stochasticity in biological

systems, and develop algorithms to construct model structure, estimate unknown parameters, discover new

biology, as well as design precision medicine. I also leverage the power of high-performance computing

techniques to enable the modeling and analysis of large-scale multicellular systems. As an integral part of

my research, I collaborate closely with biologists and clinicians to study various systems and tackle realworld biological problems that are crucial to medicine and healthcare. I believe that my research will help

move the state-of-the-art of systems biology forward and will have a substantial impact on our healthcare,

food supplies and many other issues that are essential to our survival.

..Innate immune system. Complement system is the frontline of human immune system, which quickly

detects invading microbes and alerts the host to eliminate the hostile substances. Inadequate or excessive

complement activities may lead to immunerelated diseases. I led a team consists of computer scientists and

biologists and developed a detailed computational model of the human complement system [5]. Using our

DBN approximation techniques, we found that C4BP induces differential inhibition on the classical and

lectin complement pathways and acts mainly by facilitating the decay of the C3 convertase. Our results also

highlighted the importance of infection-mediated microenvironmental perturbations, which alter the pH and

calcium levels. All these predictions were validated empirically [5].

..The insights we gained through the above works help to elucidate the regulatory mechanisms of the

innate immune system and potentially contribute to the development of immunomodulation therapies.

Cell death & disease. Cellular stresses or intrinsic/extrinsic signals can induce different forms of cell

death such as apoptosis, necroptosis, and ferroptosis, which are governed by multiple signaling pathways

and their crosstalks. The modulation of cell death has been identified as an important therapeutic target for

diverse diseases, including radiation diseases, neurodegenerative diseases, liver diseases, cancers, etc.

For instance, developing pharmacological strategies for controlling ionizing radiation (IR)-induced cell

death is important for both mitigating radiation damage and alleviating the side effects of anti-cancer radiotherapy manifested in surrounding tissue morbidity. Exposure cells to ionizing radiation (IR) often triggers

the onset of p53-dependent apoptotic pathways. In collaboration with radiation oncologists at University

of Pittsburgh Medical Center (UPMC), I built a stochastic model of p53 induced apoptosis comprised of

coupled modules of nuclear p53 activation, mitochondrial cytochrome c release and cytosolic caspase activation [16]. Our model analysis shows that immediate administration of PUMA inhibitors following IR

exposure effectively suppresses excessive cell death, provided that there is a strong caspase/Bid feedback

loop; however, the efficacy of the treatment diminishes with increasing delay in treatment implementation.

In contrast, the combined inhibition of Bid and Bax elicits an anti-apoptotic response that is effective over a

range of time delays.

IR exposure also causes necroptosis, a newly discovered non-apoptotic cell death. The cell fate decision

between apoptosis and necroptosis is governed by a complex and intertwined signaling network. In a followup work [17], we collaborated with clinicians at UPMC and developed the first calibrated ODE model for

apoptosis and necroptosis pathways and their crosstalk mediated by damaged associated molecular patterns (DAMPs). Our results highlight the role of FLIP in regulating cell fate and suggested that inhibiting caspase8 and cytochrome c could effectively suppress excessive cell death. These results provide novel insights into the development of drug combinations for mitigating the severe radiation damage.

It is known that autophagy can protect cells by maintaining cellular homeostasis and relieving various

cytotoxic stresses. In order to selectively prevent the death of normal cells and induce the death of cancer

cells, we developed a unified model of autophagy-apoptosis signaling network that involves mTOR signaling, inositol signaling, G-protein signaling, PI3K-AKT signaling, calcium signaling, intrinsic apoptosis

pathways and the crosstalks among them [18]. We found that cytoplasmic Ca2+ fine tunes autophagy and

apoptosis responses and its role is conferred by CaMKKβ. Our results reveal a time-dependent dual role

of p53 in regulating the cell-fate determination. We also predicted drug combinations for improving the

efficacy of cancer therapies.

Autophagy specific to the elimination of damaged mitochondria is called mitophagy. Our collaborators

at the Department of Environmental and Occupational Health identified that cardiolipin externalization to the

outer mitochondrial membrane acts as an elimination signal for mitophagy in neuronal cells. The collapse

of asymmetric distribution of CLs may also lead to apoptosis depending on the stress level. To understand

the role of CL in regulating mitochondrial homeostasis in response to cellular stresses, I built a validated

rule-based model of the dynamics of cardiolipin pathways. Our model reveals the complexed role of H2O2

in cell fate determination. This work might result in a platform for the drug development for the early stages

of radiation diseases.

..Synaptic signaling A human brain contains 100 billion neurons which make trillions of connections

though synapses. Neural networks are formed dynamically, which facilitate the information processing and

storage. Long-lasting synaptic plasticity is an essential mechanism for brain plasticity, which serves learning and memory. We are constructing a computational model to capture the dynamics of synaptic signaling

network, in the collaboration with neurologists at CalTech.

Future Research

An Integrative Modeling Platform for Polypharmacological Strategy Development

I will adopt global optimization methods such as evolutionary strategy for optimizing the drug delivery schedules. The platform also allows the user to incororpate experimental datasets to monitor the dynamics of biomarkers after drug treatment, and adaptively optimize therapeutic strategy.

A Virtual Immune System for Personalized Medicine:

Despite huge recent advances in medical sciences, including many drugs that target the immune system,

scientist still do not fully understand this complex system. It not only orchestrates the processes by which

our bodies fight invading pathogens, but also cause autoimmune disease such as diabetes and rheumatoid

arthritis. I have been collaborating with immunologist for years to study innate immune systems such as

the complement system [5] and Toll-like receptors pathways [15].

http://www.pitt.edu/~liubing/cv.pdf

B Liu, P S Thiagarajan. Modeling and Analysis of Biopathways Dynamics.

Publications Journal Papers

1. F Pei, H Li, B Liu, I Bahar. Quantitative Systems Pharmacological Analysis of Drugs of Abuse Reveals the Pleiotropy of Their Targets and the Effector Role of mTORC1. Frontiers in Pharmacology, 10:191(2019).
2. B Liu, B Gyori and P S Thiagarajan. Statistical Model Checking based Analysis Techniques of Biological Networks. Automated Reasoning for Systems Biology and Medicine, SpringerNature, to appear (2019).
3. Y Zhang, B Liu, L Wang and X Xie, Dissection of CB2 receptor endocytosis and trafficking suggests that ligand-dependent receptor recycling confers functional selectivity. Bioinformatics, submitted (2018).
4. J Steinman, M Epperly, Wen Hou, J Willis, H Wang, R Fisher, B Liu, I Bahar, T McCaw, V Kagan, H Bayir, J Yu, P Wipf, S Li, M S Huq, J S Greenberger. Improved Total Body Irradiation Survival by Delivery of Two Radiation Mitigators that Target Distinct Cell Death Pathways. Radiation Research, 189(1):68-83 (2018).
5. B Liu, Z Oltvai, H Bayir, G Silverman, S Pak, D Perlmutter, I Bahar. Quantitative Assessment Of Cell Fate Decision Between Autophagy And Apoptosis, Nature Scientific Reports, 7:17605 (2017).
6. V E Kagan, G Mao, F Qu, J P F Angeli, S Doll, C S Croix, H Dar, B Liu, V A Tyurin, V B Ritov, O A Kapralov, A A Amoscato, J Jiang, T Anthonymuthu, D Mohammadyani, Q Yang, J Klein-Seetharaman, S Watkins, I Bahar, J Greenberger, R Mallampalli, B R Stockwell, Y Y Tyurina, M Conrad, H Bayir. Oxidized Arachidonic/Adrenic Phosphatidylethanolamines Navigate Cells to Ferroptosis. Nature Chemical Biology, 13:81-90 (2017).
7. B Liu, Q Liu, S Palaniappan, I Bahar, P S Thiagarajana and J L Ding. Innate Immune Memory and Homeostasis May Be Conferred Through crosstalk between TLR3 and TLR7 Pathways, Science Signaling, 9(436) ra70 (2016).
8. B Liu, D Bhatt, Z N Oltvai, J S Greenberger, I Bahar. Significance of p53 dynamics in regulating apoptosis in response to ionizing radiation, and polypharmacological strategies. Scientific Reports, Nature Publishing Group 4:6245 (2014).
9. H Zhou, S Gao, N N Nguyen, M Fan, J Jin, B Liu, L Zhao, G Xiong, M Tan, S Li, L Wong. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv proteinprotein interactions. Biology Direct, 9(5):1-52 (2014).
10. A Hagiescu, B Liu, Z Cui, B Chattopadhyay, P S Thiagarajan and W F Wong. GPU code generation for ODE-based applications with phased shared-data access patterns. ACM Transactions on Architecture and Code Optimization, 10(4):55 (2013).
11. B Liu, A Hagiescu, S K Palaniappan, B Chattopadhyay, Z Cui, W F Wong, P S Thiagarajan. Approximate Probabilistic Analysis of Biopathway Dynamics. Bioinformatics, 28(11):1508- 1516 (2012).
12. S K Palaniappan, S Akshay, B Liu, B Genest, P S Thiagarajan. A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(5):1352-1365 (2012).
13. B Liu, P S Thiagarajan. Modeling and Analysis of Biopathways Dynamics. Journal of Bioinformatics and Computational Biology, 10(4):1231001 (2012).
14. B Liu, J Zhang, P Y Tan, D Hsu, A M Blom, B Leong, S Sethi, B Ho, J L Ding, P S Thiagarajan. A Computational and Experimental Study of the Regulatory Mechanisms of the Complement System. PLoS Computational Biology, 7(1):e1001059 (2011).
15. B Liu, D Hsu, P S Thiagarajan. Probabilistic Approximations of ODEs based Bio-Pathway Dynamics. Theoretical Computer Science, 412(21):2188-2206 (2011).
16. T Zhu, P Korshunov, B Liu, W T Ooi. Plasma: A Scripting Language for Processing Media Streams. ACM/SPIE Multimedia Computing and Networking (MMCN), Proceedings of SPIE:6504, pp. 65040Q (2007). Conference Proceeding Papers
17. B Liu and J R Faeder. Parameter Estimation of Rule-based Models using Statistical Model Checking. Formal Methods for Biological and Biomedical Systems FMBBS), IEEE, pp. 1458- 1464 (2016).
18. Q Wang, N Miskov-Zivanov, B Liu, J R Faeder, M Lotze and E M Clarke. A Multicellular Model of Pancreatic Cancer Microenvironment. Computational Methods in Systems Biology (CMSB), Springer, LNCS:9859, 289-305 (2016).
19. B Gyori, B Liu, S Paul, R Ramanathan and P S Thiagarajan. Probabilistic Approximation and Analysis of Hybrid Systems. Hybrid Systems Biology (HSB), Springer, LNCS:9271, pp. 1-15 (2015).
20. B Liu, S Kong, S Gao, P Zuliani and E M Clarke. Towards Personalized Prostate Cancer Therapy Using Delta-Reachability Analysis. Hybrid Systems: Computation and Control (HSCC), ACM New York, pp. 227-232 (2015).
21. B Liu, S Kong, S Gao, P Zuliani and E M Clarke. Parameter Synthesis for Cardiac Cell Hybrid Models Using Delta-Decisions. Computational Methods in Systems Biology (CMSB), Springer, LNCS:8859, pp. 99-113 (2014).
22. S K Palaniappan, B Gyori, B Liu, D Hsu, and P S Thiagarajan. Statistical Model Checking Based Calibration and Analysis of Bio-pathway Models. Computational Methods in Systems Biology (CMSB), Springer, LNCS:8130, pp 120-134 (2013).
23. B Liu, P S Thiagarajan, D Hsu. Probabilistic Approximations of Signaling Pathway Dynamics. Computational Methods in Systems Biology (CMSB), Springer, LNCS:5688, pp. 251-265 (2009).
24. B Liu and W T Ooi. SLIME: A Tool for Composing Live and Stored Media. In Proc. of the 11th NUROP Congress, Singapore (2006). Book Chapters
25. B Liu and I Bahar. Radiation Biologic Pathways Intersection and Integration with Other Pathways. Radiobiology and Methods, to appear (2018).

Book

25. Bing Liu. Computational Modeling of Biological Pathways: Probabilistic Approximation and

Analysis Techniques. LAP LAMBERT Academic Publishing, ISBN-10: 3847372114, 2012.

Technical Reports

26. Bing Liu, S Kong, S Gao, and E M Clarke. Parameter Identification Using Delta-Decisions

for Biological Hybrid Systems. CMU SCS Technical Report, CMU-CS-13-136 (2013).

27. Bing Liu. Statistical analysis of partially calibrated bio-pathway models. HPC E-newsletter

Issue No. 24. 2009.

Grants and

Scholarships

NIH Mitochondrial Targeting Against Radiation Damage (Co-I) 2015-2020

NIH Causal Modeling and Discovery of Biomedical Knowledge from Big Data (Co-I) 2014-2018

NIH New Therapies For Liver Fibrosis and Hyperproliferation in a1-AT Deficiency (Co-I) 2012-2017

NSF Computational Modeling and Analysis for Complex Systems (PDA) 2009-2014

Grant from Google Inc. for open-source software development 2010