virus

Alison L Hill


Summary

The overarching goal of our research is to determine the underlying rules governing the complex dynamics of infectious diseases. We develop mathematical and computational models for this task, and work in close collaboration with experimental labs and clinical researchers. Quantitative models are a powerful tool to test hypotheses about the biological mechanisms responsible for the observed trends in data, and, to integrate existing biological data in a formal way to predict the outcomes of experiments that have not yet, or could never, be performed. Our work bridges a range of data sources, including pathogen genetic data, in vitro microbial growth, time-series characterization of infections and immunity within infected individuals, in vivo lineage tracing, pharmacokinetics and pharmacodynamics, and human behavior.

Click on any of the research themes below to learn more

HIV burden

Since HIV/AIDS emerged around the world nearly forty years ago, it has become one of the deadliest pandemics ever. Incredible progress has been made in understanding and treating the infection, but we still don’t have a cure and haven’t controlled the epidemic. We develop analytic methods, in close collaboration with experimentalists to 

  • understand the basic biology of the virus, including how it is transmitted, how it evades the immune system, how to maintains latency, and how it causes AIDS
  • understand why antiretroviral therapy sometimes fails, for example due to the evolution of drug resistance, and help design optimal treatment regimens
  • evaluate the potential of new investigation HIV therapies, such as latency-reversing agents, immunotherapy, and gene therapy
  • use HIV as a model system to understand basic evolutionary dynamics

Representative papers:

Rosenbloom DIS*, Hill AL*, Rabi SA*, Siliciano RF, Nowak MA (2012). Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nature Medicine, 18 (9) , 1378-1385. (PubMed)

Hill AL*, Rosenbloom DIS*, Fu F, Nowak MA, Siliciano RF (2014). Predicting the outcomes of treatments to eradicate the latent reservoir for HIV-1. PNAS, 111 (37), 13475-13480. (Arxiv, PubMed)

Hill AL, Rosenbloom DIS, Nowak MA, Siliciano RF (2018). Insight into treatment of HIV infection from viral dynamics models. Immunological Reviews. Sep 1;285(1):9–25. +details

This review is part of a special issue on "Modeling Viral Infection and Immunity", organized by Alan Perelson and Ruy Ribiero. The odds of living a long and healthy life with HIV infection have dramatically improved with the advent of combination antiretroviral therapy. Along with the early development and clinical trials of these drugs, and new field of research emerged called viral dynamics, which uses mathematical models to interpret and predict the time-course of viral levels during infection and how they are altered by treatment. In this review, we summarize the contributions that virus dynamics models have made to understanding the pathophysiology of infection and to designing effective therapies. This includes studies of the multiphasic decay of viral load when antiretroviral therapy is given, the evolution of drug resistance, the long-term persistence latently infected cells, and the rebound of viremia when drugs are stopped. We additionally discuss new work applying viral dynamics models to new classes of investigational treatment for HIV, including latency-reversing agents and immunotherapy.

Related papers:

Hill AL (2018). Modeling HIV persistence and cure studies. Current Opinion in HIV and AIDS, 13 (5), 428-434 (PubMed)

Lim S-Y, Osuna CE, Hraber PT, Hesselgesser J, Gerold JM, Barnes TL, Sanisetty S, Seaman MS, Lewis MG, Geleziunas R, Miller MD, Cihlar T, Lee WA, Hill AL, Whitney JB (2018). TLR7 agonists induce transient viremia and reduce the viral reservoir in SIV-infected rhesus macaques on antiretroviral therapy. Science Translational Medicine. May 2;10(439):eaao4521 (PubMed).

Wang Z, Gurule EE, Brennan TP, Gerold JM, Kwon KJ, Hosmane NN, Kumar M, Beg S, Capoferria AA, Ray SC, Ho YC, Hill AL, Siliciano JD, Siliciano RF (2018). Expanded clones carrying replication-competent HIV-1 persist, wax, and wane. PNAS. February: 201720665. doi:10.1073/pnas.1720665115 (PubMed)

Hill AL (2017). Mathematical Models of HIV Latency. In: HIV Latency. Current Topics in Microbiology and Immunology. Springer.

Kirtane AR, Abouzid O, Minahan D, Bensel T, Hill AL, Selinger C, Bershteyn A, Mo SS, Craig M, Mazdiyasni H, Cleveland C, Rogner J, Lee YAL, Booth L, Javid F, Wu SJ, Grant T, Bellinger AM, Nikolic B, Hayward A, Wood L, Eckhoff PA, Nowak MA, Langer R, Traverso G (2017). Development of an oral once-weekly drug delivery system for HIV antiretroviral therapy. Nature Communications, 9: 2. doi:10.1038/s41467-017-02294-6.

Rosenbloom DIS, Hill AL, Laskey SB, Siliciano RF (2017). Re-evaluating evolution in the HIV reservoir. Nature, Brief Communication Arising, 2017;551(7681):E6 (PubMed, Biorxiv)

Borducchi EN, Cabral C, Stephenson KE, Liu J, Abbink P, Nkolola JP, Brinkman AL, Peter L, Lee BC, Jimenez J, Jetton D, Mondesir J, Mojta S, Chandrashekar A, Molloy K, Alter G, Gerold JM, Hill AL, Lewis MG, Pau MG, Schuitemaker H, Hesselgesser J, Geleziunas R, Kim JH, Robb ML, NL, Barouch DH (2016). Ad26/MVA therapeutic vaccination with TLR7 stimulation in SIV-infected rhesus monkey. Nature, 540 (7632), 284-287: doi:10.1038/nature20583 (PubMed)

Hill AL , Rosenbloom DIS, Siliciano JD, Siliciano RF (2016). Insufficient evidence for rare activation of latent HIV in the absence of reservoir-reducing interventions. PLoS Pathogens. Aug 25;12(8):e1005679. doi: 10.1371/journal.ppat.1005679. (PubMed)

Hill AL, Rosenbloom DIS, Goldstein E, Hanhauser E, Kuritzkes DR, Siliciano RF, Henrich TJ (2016) Real-time predictions of reservoir size and rebound time during antiretroviral therapy interruption trials for HIV. PLoS Pathogens, 12(4):e1005535. doi:10.1371/journal.ppat.1005535. ( BioRxiv, PubMed )

Rosenbloom DIS, Elliott O, Hill AL, Henrich TJ, Siliciano JM, Siliciano RF (2015). Designing and interpreting limiting dilution assays: general principles and applications to the latent reservoir for HIV-1. Open Forum InfectiousDiseases, 2(4):ofv123. doi:10.1093/ofid/ofv123. (BioRxiv, PubMed )

Laird GM, Bullen CK, Rosenbloom DIS, Martin AR, Hill AL, Durand CM, Siliciano JD, Siliciano RF (2015). Ex vivo analysis identifies effective HIV-1 latency–reversing drug combinations. Journal of Clinical Investigation. doi:10.1172/JCI80142 (PubMed)

Whitney JB, Hill AL, Sanisetty S, Penaloza-MacMaster P, Shetty M, Parenteau L, Cabral C, Shields J, Blackmore S, Smith JY, Brinkman AL, Peter LE, Mathew SI, Smith KM, Borducchi EN, Rosenbloom DIS, Lewis MG, Hattersley J, Li B, Hesselgesser J, Geleziunas R, Robb ML, Kim JH, Michael NL, Barouch DH (2014). Rapid establishment of the viral reservoir prior to systemic viremia following mucosal SIV infection of rhesus monkeys. Nature. 512 (7512): 74-77 (PubMed)

Henrich TJ, Hanhauser E, Marty MF, Sirignano MN, Keating S, Lee T-H, Robles YP, Li JZ, Heisey A, Hill AL, Busch MP, Armand P, Soiffer RJ, Altfeld M, Kuritzkes DR (2014). Antiretroviral-free HIV-1 remission and viral rebound following allogeneic stem-cell transplantation: implications for HIV-1 cure research. Annals of Internal Medicine, 161(5):319-327 (PubMed)

Hill AL, Rosenbloom DIS, Nowak MA (2012). Evolutionary dynamics of HIV at multiple spatial and temporal scales. J Mol Med, 90 (5), 543-561. (PubMed)

Multilevel selection

Many microbial infections undergo continuous, rapid evolution in response to host defenses and medical interventions, making them difficult to prevent or treat. We use evolutionary theory to understand the factors that influence infectious disease evolution both within and between individuals. In particular, we examine realistic aspects of the environment pathogens are exposed to - such as temporal and spatial heterogeneity in selection pressures, and co-evolution with host defenses. We aim to explain observed patterns of genotypic and phenotypic diversity in pathogens, but also to hypothesize about yet-unobserved trends.

Our goals of this work are two-fold. Firstly, we hope to help design better interventions and reduce the public health burden of infectious disease. Secondly, we aim to exploit the rapid and repeatable nature of pathogen adaptation to inform the more general study of evolutionary dynamics.

Representative papers:

Leventhal GE*, Hill AL*, Nowak MA, Bonhoeffer S (2015). Evolution and emergence of infectious diseases in theoretical and real world networks. Nature Communications, 6 (6101); doi:10.1038/ncomms7101 (PubMed)

Related papers:

Neagu IA, Olejarz J, Freeman M, Rosenbloom DIS, Nowak MA, Hill AL (2018). Life cycle synchronization is a viral drug resistance mechanism. PLoS Computational Biology. 14: e1005947. doi:10.1371/journal.pcbi.1005947 (PubMed)

Moreno-Gámez S*, Hill AL*, Rosenbloom DIR, Petrov D, Nowak MA, Pennings P (2015). Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance. PNAS, 112(22):E2874-E2883. doi:10.1073/pnas.1424184112. (BioRxiv, PubMed)

Humplik J, Hill AL, Nowak MA (2014). Evolution of infectious diseases in finite populations. Journal of Theoretical Biology, 360 (149-162) (PDF, PubMed)

Rosenbloom DIS*, Hill AL*, Rabi SA*, Siliciano RF, Nowak MA (2012). Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nature Medicine, 18 (9) , 1378-1385. (PubMed)

Hill AL, Rosenbloom DIS, Nowak MA (2012). Evolutionary dynamics of HIV at multiple spatial and temporal scales. J Mol Med, 90 (5), 543-561. (PubMed)

drug resistance

Drugs that specifically target pathogens have been one of the major medical breakthroughs of the last century, but their success is continually under threat due to the evolution of drug resistant strains. We use evolutionary theory combined with data from a range of sources to determine factors that increase or decrease the risk of drug resistance, to evaluate the predictability of resistance evolution, or to suggest new lab experiments or clinical trials of drugs.

Representative papers:

Moreno-Gámez S*, Hill AL*, Rosenbloom DIR, Petrov D, Nowak MA, Pennings P (2015). Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance. PNAS, 112(22):E2874-E2883. doi:10.1073/pnas.1424184112. (BioRxiv, PubMed)

Neagu IA, Olejarz J, Freeman M, Rosenbloom DIS, Nowak MA, Hill AL (2018). Life cycle synchronization is a viral drug resistance mechanism. PLoS Computational Biology. 14: e1005947. doi:10.1371/journal.pcbi.1005947 (PubMed)

Related papers:

Hill AL, Rosenbloom DIS, Nowak MA, Siliciano RF (2018). Insight into treatment of HIV infection from viral dynamics models. Immunological Reviews. Sep 1;285(1):9–25. +details

Kirtane AR, Abouzid O, Minahan D, Bensel T, Hill AL, Selinger C, Bershteyn A, Mo SS, Craig M, Mazdiyasni H, Cleveland C, Rogner J, Lee YAL, Booth L, Javid F, Wu SJ, Grant T, Bellinger AM, Nikolic B, Hayward A, Wood L, Eckhoff PA, Nowak MA, Langer R, Traverso G (2017). Development of an oral once-weekly drug delivery system for HIV antiretroviral therapy. Nature Communications, 9: 2. doi:10.1038/s41467-017-02294-6.

Rosenbloom DIS*, Hill AL*, Rabi SA*, Siliciano RF, Nowak MA (2012). Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nature Medicine, 18 (9) , 1378-1385. (PubMed)

Hill AL, Rosenbloom DIS, Nowak MA (2012). Evolutionary dynamics of HIV at multiple spatial and temporal scales. J Mol Med, 90 (5), 543-561. (PubMed)

HIV Latency

Many different viral, bacterial, and parasitic infections can enter “latent” phases or maintain chronic infections by other means. This is in contrast to most infections which can be rapidly cleared by the immune system or antimicrobial therapy. We are broadly interested in the mechanisms by which infections achieve long-term persistence. This is particularly important for HIV, since latency is one of the main reasons we still cannot cure the infection. It is also critical for cancer-causing viruses, like the Kaposi’s sarcoma virus. We develop methods to decipher the cellular and viral processes maintaining latency, and build models to evaluate the potential of different strategies to perturb latency and clear infections.

Representative paper:

Hill AL*, Rosenbloom DIS*, Fu F, Nowak MA, Siliciano RF (2014). Predicting the outcomes of treatments to eradicate the latent reservoir for HIV-1. PNAS, 111 (37), 13475-13480. (Arxiv, PubMed)

Related papers:

Hill AL, Rosenbloom DIS, Nowak MA, Siliciano RF (2018). Insight into treatment of HIV infection from viral dynamics models. Immunological Reviews. Sep 1;285(1):9–25.

Hill AL (2018). Modeling HIV persistence and cure studies. Current Opinion in HIV and AIDS, 13 (5), 428-434 (PubMed)

Lim S-Y, Osuna CE, Hraber PT, Hesselgesser J, Gerold JM, Barnes TL, Sanisetty S, Seaman MS, Lewis MG, Geleziunas R, Miller MD, Cihlar T, Lee WA, Hill AL, Whitney JB (2018). TLR7 agonists induce transient viremia and reduce the viral reservoir in SIV-infected rhesus macaques on antiretroviral therapy. Science Translational Medicine. May 2;10(439):eaao4521 (PubMed).

Wang Z, Gurule EE, Brennan TP, Gerold JM, Kwon KJ, Hosmane NN, Kumar M, Beg S, Capoferria AA, Ray SC, Ho YC, Hill AL, Siliciano JD, Siliciano RF (2018). Expanded clones carrying replication-competent HIV-1 persist, wax, and wane. PNAS. February: 201720665. doi:10.1073/pnas.1720665115 (PubMed)

Hill AL (2017). Mathematical Models of HIV Latency. In: HIV Latency. Current Topics in Microbiology and Immunology. Springer.

Rosenbloom DIS, Hill AL, Laskey SB, Siliciano RF (2017). Re-evaluating evolution in the HIV reservoir. Nature, Brief Communication Arising, 2017;551(7681):E6 (PubMed, Biorxiv)

Borducchi EN, Cabral C, Stephenson KE, Liu J, Abbink P, Nkolola JP, Brinkman AL, Peter L, Lee BC, Jimenez J, Jetton D, Mondesir J, Mojta S, Chandrashekar A, Molloy K, Alter G, Gerold JM, Hill AL, Lewis MG, Pau MG, Schuitemaker H, Hesselgesser J, Geleziunas R, Kim JH, Robb ML, NL, Barouch DH (2016). Ad26/MVA therapeutic vaccination with TLR7 stimulation in SIV-infected rhesus monkey. Nature, 540 (7632), 284-287: doi:10.1038/nature20583 (PubMed)

Hill AL , Rosenbloom DIS, Siliciano JD, Siliciano RF (2016). Insufficient evidence for rare activation of latent HIV in the absence of reservoir-reducing interventions. PLoS Pathogens. Aug 25;12(8):e1005679. doi: 10.1371/journal.ppat.1005679. (PubMed)

Hill AL, Rosenbloom DIS, Goldstein E, Hanhauser E, Kuritzkes DR, Siliciano RF, Henrich TJ (2016) Real-time predictions of reservoir size and rebound time during antiretroviral therapy interruption trials for HIV. PLoS Pathogens, 12(4):e1005535. doi:10.1371/journal.ppat.1005535. ( BioRxiv, PubMed )

Rosenbloom DIS, Elliott O, Hill AL, Henrich TJ, Siliciano JM, Siliciano RF (2015). Designing and interpreting limiting dilution assays: general principles and applications to the latent reservoir for HIV-1. Open Forum InfectiousDiseases, 2(4):ofv123. doi:10.1093/ofid/ofv123. (BioRxiv, PubMed )

Laird GM, Bullen CK, Rosenbloom DIS, Martin AR, Hill AL, Durand CM, Siliciano JD, Siliciano RF (2015). Ex vivo analysis identifies effective HIV-1 latency–reversing drug combinations. Journal of Clinical Investigation. doi:10.1172/JCI80142 (PubMed)

Whitney JB, Hill AL, Sanisetty S, Penaloza-MacMaster P, Shetty M, Parenteau L, Cabral C, Shields J, Blackmore S, Smith JY, Brinkman AL, Peter LE, Mathew SI, Smith KM, Borducchi EN, Rosenbloom DIS, Lewis MG, Hattersley J, Li B, Hesselgesser J, Geleziunas R, Robb ML, Kim JH, Michael NL, Barouch DH (2014). Rapid establishment of the viral reservoir prior to systemic viremia following mucosal SIV infection of rhesus monkeys. Nature. 512 (7512): 74-77 (PubMed)

Henrich TJ, Hanhauser E, Marty MF, Sirignano MN, Keating S, Lee T-H, Robles YP, Li JZ, Heisey A, Hill AL, Busch MP, Armand P, Soiffer RJ, Altfeld M, Kuritzkes DR (2014). Antiretroviral-free HIV-1 remission and viral rebound following allogeneic stem-cell transplantation: implications for HIV-1 cure research. Annals of Internal Medicine, 161(5):319-327 (PubMed)

HIV model with immune response

Infectious diseases display complex non-linear dynamics, and we are often interested in knowing what the underlying mechanisms generating these dynamics are or what the value of particular parameters are and how they change in different settings. We develop and employ methods to infer mechanistic dynamic models from a variety of data sources. We are working on Bayesian methods for deterministic and stochastic models, tools to identify treatment effects, unifying predictive techniques of statistical and mathematical models, harnessing ideas from machine learning for dynamical systems, and integrating various data sources in principled ways. We are interested in applying these methods beyond the infection context, including to understand dynamics in the immune system and properties of cell turnover.

Related papers:

Lim S-Y, Osuna CE, Hraber PT, Hesselgesser J, Gerold JM, Barnes TL, Sanisetty S, Seaman MS, Lewis MG, Geleziunas R, Miller MD, Cihlar T, Lee WA, Hill AL, Whitney JB (2018). TLR7 agonists induce transient viremia and reduce the viral reservoir in SIV-infected rhesus macaques on antiretroviral therapy. Science Translational Medicine. May 2;10(439):eaao4521 (PubMed).

Borducchi EN, Cabral C, Stephenson KE, Liu J, Abbink P, Nkolola JP, Brinkman AL, Peter L, Lee BC, Jimenez J, Jetton D, Mondesir J, Mojta S, Chandrashekar A, Molloy K, Alter G, Gerold JM, Hill AL, Lewis MG, Pau MG, Schuitemaker H, Hesselgesser J, Geleziunas R, Kim JH, Robb ML, NL, Barouch DH (2016). Ad26/MVA therapeutic vaccination with TLR7 stimulation in SIV-infected rhesus monkey. Nature, 540 (7632), 284-287: doi:10.1038/nature20583 (PubMed)

Real-world contact networks

The standard models of infection dynamics assume that all hosts are equal - equally susceptible to infection, able to spread disease to other contacts at equal rates, and equally likely to be treated. In reality, this is rarely the case, and heterogeneities in host populations can lead to very different dynamics and evolution of infections than would arise by just considering an “average” individual. We develop models to understand these effects and methods to infer population structure from data.

Related papers:

Leventhal GE*, Hill AL*, Nowak MA, Bonhoeffer S (2015). Evolution and emergence of infectious diseases in theoretical and real world networks. Nature Communications, 6 (6101); doi:10.1038/ncomms7101 (PubMed)

Moreno-Gámez S*, Hill AL*, Rosenbloom DIR, Petrov D, Nowak MA, Pennings P (2015). Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance. PNAS, 112(22):E2874-E2883. doi:10.1073/pnas.1424184112. (BioRxiv, PubMed)

Hill AL, Rand DG, Nowak MA, Christakis NC (2010) Infectious disease modeling of social contagion in networks. PLoS Computational Biology, 6 (11). (PubMed)

Hill AL*, Rand DG*, Nowak MA, Christakis NC (2010) Emotions as infectious diseases in a large social network: the SISa model. Proc Roy Soc B, 277 (1701). (PubMed)

HIV PKPD model

Developing new drugs is extremely expensive and error prone. "Quantitative systems pharmacology” (QSP) is an approach that uses mechanistic mathematical models to describe the interaction between drug molecules and human/animal physiology. It combines the study of pharmacokinetics (the spatial and temporal changes in drug levels in the body due to absorption, metabolism, distribution, and clearance), pharmacodynamics (the drug-concentration-dependent therapeutic effect of the drug), and biomathematical models of disease pathogenesis. QSP can help optimize the drug-development process at all stages. We apply this approach to developing and optimizing therapies for HIV and other infectious diseases.

Related papers:

Hill AL, Rosenbloom DIS, Nowak MA, Siliciano RF (2018). Insight into treatment of HIV infection from viral dynamics models. Immunological Reviews. Sep 1;285(1):9–25. +details

Kirtane AR, Abouzid O, Minahan D, Bensel T, Hill AL, Selinger C, Bershteyn A, Mo SS, Craig M, Mazdiyasni H, Cleveland C, Rogner J, Lee YAL, Booth L, Javid F, Wu SJ, Grant T, Bellinger AM, Nikolic B, Hayward A, Wood L, Eckhoff PA, Nowak MA, Langer R, Traverso G (2017). Development of an oral once-weekly drug delivery system for HIV antiretroviral therapy. Nature Communications, 9: 2. doi:10.1038/s41467-017-02294-6.

Rosenbloom DIS*, Hill AL*, Rabi SA*, Siliciano RF, Nowak MA (2012). Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nature Medicine, 18 (9) , 1378-1385. (PubMed)

SISa emotions

Individuals influence the behavior of their social contacts, and sometimes behaviors, ideas, or even non-communicable health states can “spread” between individuals in ways that resemble the spread of infections. In the past, we have used methods from mathematical epidemiology and network theory to study the effect of social networks on the spread of health-related behaviors in large cohort studies. This led to new ways to quantify the effects of social contagion, to predict future trends, to understand changes in past incidence, and to assess interventions. More generally, we are interested in the inter-personal dynamics of disease risk modifying behavior.

Related papers:

Hill AL, Rand DG, Nowak MA, Christakis NC (2010) Infectious disease modeling of social contagion in networks. PLoS Computational Biology, 6 (11). (PubMed)

Hill AL*, Rand DG*, Nowak MA, Christakis NC (2010) Emotions as infectious diseases in a large social network: the SISa model. Proc Roy Soc B, 277 (1701). (PubMed)