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Automated Reasoning for Decision Making in Mosquitoes: Combating Malaria Spread

Project Description

This project aims at developing and applying automated reasoning techniques to analyse decision-making processes in mosquitoes. Malaria mosquitoes are highly effective disease vectors that have caused ~400,000 deaths in 2017 alone. They pose a high risk for public health not only for malaria-endemic areas, but also for Europe and the US – both areas have mosquitoes that can transmit malaria. This project will focus on the olfactory system of mosquito larvae and its response to artificial and natural repellents. This is important since mosquitoes crucially depend on their sense of smell to seek humans and nourishment, and to evade toxic and repellent substances. In this project we will develop computational models of the olfactory system of mosquito larvae that enable us understanding and predicting the larvae’s behavioural responses, with the ultimate aim to control the spread of malaria.

This project is a joint effort between Dr Paolo Zuliani (Newcastle) and Dr Olena Riabinina (Durham), who are recognised leaders in the field of automated reasoning for systems biology and on insect neuroscience, respectively.

Very little is currently known about how individual olfactory neurons of mosquitoes encode attractive and repellent stimuli. To address this problem Dr Riabinina has developed the first transgenic malaria mosquitoes with genetically targeted olfactory neurons and described the anatomy of these neurons. Trustworthy olfactory models are required when using genetically-modified mosquitoes in combating the spread of malaria: e.g., what is the probability that a mosquito will be repelled by a given odour? Automated reasoning algorithms can answer these questions with great accuracy, shortening significantly the delay between bench and field use.

The project would fit candidates with a strong computational/mathematical background (eg, computer science, mathematics, physics) and with a keen interest in multidisciplinary research. Any necessary training in the biosciences will be provided.

The three main objectives of this project are:
1. Development of mechanistic models of the olfactory system of mosquito larvae using stochastic modelling;
2. Automated reasoning algorithms for stochastic models;
3. In vivo model validation.

The student will join the ICOS group in the School of Computing, a vibrant group hosting ~60 members (10 academic staff, 15 postdocs, ~35 PhD students) working at the interface between complex biological systems and computer science. ICOS features weekly seminars, and offers plentiful opportunities of interaction with researchers with widely different skills sets from a number of disciplines, including synthetic biology, computational neuroscience and neuroinformatics, bioinformatics, automated reasoning, etc.

Applications should be made by emailing with a CV (including contact details of at least two academic (or other relevant) referees), and a covering letter – clearly stating your first choice project, and optionally 2nd and 3rd ranked projects, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University. Applications not meeting these criteria will be rejected.
In addition to the CV and covering letter, please email a completed copy of the Additional Details Form (Word document) to . A blank copy of this form can be found at:
Informal enquiries may be made to

Funding Notes

This is a 4 year BBSRC studentship under the Newcastle-Liverpool-Durham DTP. The successful applicant will receive research costs, tuition fees and stipend (£15,009 for 2019-20). The PhD will start in October 2020. Applicants should have, or be expecting to receive, a 2.1 Hons degree (or equivalent) in a relevant subject. EU candidates must have been resident in the UK for 3 years in order to receive full support. Please note, there are 2 stages to the application process.


Stochastic Rate Parameter Inference using the Cross-Entropy Method. (2019) CMSB, LNCS volume 11095, pp. 146-164.

SMT-based Synthesis of Safe and Robust PID Controllers for Stochastic Hybrid Systems. (2017) HVC, LNCS volume 10629, pp. 131-146.

Probabilistic Hybrid Systems Verification via SMT and Monte Carlo Techniques. (2016) HVC, LNCS volume 10028, pp. 152-168.

ProbReach: Verified Probabilistic Delta-Reachability for Stochastic Hybrid Systems. (2015) HSCC, pp. 134-139.

BioPSy: An SMT-based Tool for Guaranteed Parameter Set Synthesis of Biological Models. (2015) CMSB, LNCS volume 9308, pp. 182-194,

Commonly used insect repellents hide human odors from Anopheles mosquitoes. (2019) Current Biology, 29, 1-12.

Split-QF system for fine-tuned transgene expression in Drosophila. (2019) Genetics, 212, 1, 53-63.

Organization of olfactory centers in the malaria mosquito Anopheles gambiae. (2016) Nature Communications, 7, 13010.

Olfactory behaviors assayed by computer tracking of Drosophila in a four-quadrant olfactometer. (2016) Journal of Visualized Experiments, 114, e54346.

Improved and expanded Q-system reagents for genetic manipulations. (2015) Nature Methods, 12, 219-222.

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