MaCSBio Science Days 2020
Over the past 5 years the MaCSBio Science Day has become the annual event for researchers and students active in systems biology and bioinformatics in the Maastricht region. Join us to catch up with old friends, meet new people, and share the great science that is conducted in this area.
The theme of this this year’s meeting is: Celebrating five years of MaCSBio: Looking back, looking forward
We will celebrate 5 years of MaCSBio with 5 online sessions open to a broad audience of students, scientists, and policy makers and the general public in the week of Monday July 6 - Friday July 10 from 15.00-17.00 CET.
Our programme contains something interesting for everybody:
- an introduction to what Systems Biology actually is
- Master’s students thesis presentations
- two great keynotes
- engaging workshops
- and talks from MaCSBio PhD alumni
Monday 06 July | Presentations for the general public
What is Systems Biology? What does the Maastricht Centre for Systems Biology do? Join this event to find out!
Tuesday 07 July | Master Systems Biology selected students presentations
Our master's students will present the research they performed during their internship.
Wednesday 08 July | Keynote and PhD alumni: Computational Neuro-Genetics
Join for a keynote by Prof. Klaas Enno Stephan (Director Translational Neuromodeling Unit, University of Zurich, Switzerland), followed by talks of our MaCSBio alumni Isma Zulfiqar and Maryam Karimian.
Thursday 09 July | Workshops
Get hands on experience with network modelling, genome scale metabolic modeling, or machine learning.
Friday 10 July | Keynote and PhD alumni: Systems Medicine of Chronic Diseases
Join for a keynote by Prof. Natasa Pržulj (Professor of Biomedical Data Science at Computer Science, University College London, and ICREA Research Professor at Life Sciences Department, Barcelona Supercomputing Center) followed by talks of our MaCSBio alumni Dr. Samar Tareen and Shauna O’Donovan
This year the COVID-19 pandemic is disrupting the world as we know it. In-person meetings are not possible but science goes on. Instead of cancelling or postponing the MaCSBio Science Day, we will be taking this anniversary edition online using the virtual platform Crowdcast.
Tuesday 07 July
Dr. Julia Massimelli Sewall | Director of master's programmes Sciences
|15:05||Predicting the effect of single-point missense variants in the binding site on protein-ligand binding affinities: A machine learning approach
Ammar Ammar | Systems Biology student
|15:20||The visual cortex as a network of phase-oscillators
Kris Evers | Systems Biology student
|15:35||Assessing outcomes and risk factors of atrial fibrillation through a lifetime population-level markov model
Cristian Barrios Espinosa | Systems Biology student
|15:50||Towards understanding complex behaviour in goal-oriented systems
Raphael Stolpe | Systems Biology student
Predicting the effect of single-point missense variants in the binding site on protein-ligand binding affinities: A machine learning approach
A key concept in drug design is how natural variants, especially the ones occurring in the binding sites of drug targets, affect the inter-individual drug responses and efficacies by altering binding affinities. These effects have been reported in literature on very limited and small datasets that are not suitable for machine learning prediction models. Ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed to build a machine learning (ML) model predicting these effects.
However, to the best of our knowledge, such a dataset did not exist yet. To solve this, a reference dataset of ligands binding affinities to proteins with all their reported binding site variants was constructed using a molecular docking approach. A numerical vector representation of protein, binding pocket, mutation, and ligand information was encoded using a total of 256 extracted features to describe the protein-ligand pair. Using this dataset, two designs of machine learning regression models were trained and evaluated on the chosen features to predict the binding affinity, and six different scenarios to split training and test data were evaluated. The models trained on datasets based on ligand molecular weight split reported the best performance. The best performance model reported an RMSE value of 0.57 kcal/mol-1 on an independent test set with an R-squared value of 0.86 and a prediction speed of 29 bound compounds affinities per second. We report an improvement in the protein-ligand binding affinity prediction performance of the ML models compared to several published models. The obtained models can be used in early-stage drug discovery to rapidly and accurately obtain a better overview of the ligand binding affinity variability across genetic variants. This may benefit applicability of medicine across ethnic groups as well as in personalized medicine.
The Visual Cortex as a Network of Phase-Oscillators
The human brain is capable of extracting information from visual scenes. Mathematical models of the visual system can be used to link biological structure to function. Neurons in the early visual cortex are sensitive to oriented stimuli, and local synaptic connections are biased towards neurons with collinear receptive fields. This suggests a role for lateral connections in the early visual cortex in contour integration. Human psychophysical experiments (Field et al., 1993) suggest that a local association field is expressed by the visual system. This local association field suggests a relatively high association between collinear and proximal oriented stimuli.
The higher association between collinear stimuli allows for grouping of smooth contours. In this project the local association field is implemented in a phase-oscillator network model assuming binding-by-synchrony. The phase-oscillators represent neuron populations of the early visual cortex, the lateral connections between the oscillators are based on the local association field theory, and grouping of contours is assumed to take place through synchrony between phase-oscillators. The frequency- and phase code can be used by the brain to interpret a visual scene. Experiments are performed by simulating the phase update in the Kuramoto model. The first experiment confirms that the model is capable of performing contour integration through synchrony. New experiments are designed to generate testable predictions and hypotheses. These predictions, hypotheses and future experiments and extensions of the model are discussed.
Assessing Outcomes and Risk Factors Of Atrial Fibrillation Through A Lifetime Population-Level Markov Model
Cristian Alberto Barrios Espinosa
Atrial fibrillation (AF) is characterized by abnormal electrical and mechanical activity of the atria and presents a major global health burden. Computational models are increasingly being used to study AF and guide its treatment. However, current computational models and epidemiological studies are unable to bridge the gap of knowledge between mechanisms of AF pathophysiology and long-term clinical outcomes.
Aim: To develop a novel computational model to bridge this knowledge gap by simulating a virtual population of patients over a lifetime period. Methods: A Markov model with 5 states for combinations of AF, sleep-disordered breathing (SDB) and death was developed. The stochastic transitions between states are controlled by clinical risk factors (age, sex, SDB) and mechanisms of AF pathophysiology (shortening of effective refractory period and slowing of conduction velocity). Results: The model was calibrated to reproduce real-world data with respect to: 1) epidemiology of AF and SDB, 2) mortality in the general population and in AF patients, 3) patterns of AF paroxysms, and 4) relation between clinical subtypes of AF and atrial fibrosis. Finally, the calibrated model was used to analyze the potential therapeutic effects on AF of different types of risk-factor management for SDB. Conclusion: We successfully developed a model that connects mechanistic and epidemiological research in AF and show how this model can be used to perform a ‘virtual clinical trial’ to generate new hypotheses that can be used to guide new clinical research and ultimately lead to better AF management. Impact: This novel patient-level model will be used to study the effect of new policies on the burden caused by AF.
Towards understanding complex behaviour in goal-oriented systems
Understanding complex, goal-oriented behaviour remains an ongoing challenge in neuroscience. Meaningful progress has been made through intelligently designed experiments. Visual and sensory information processing as well as action generation were found to be a function of the frontoparietal network spanning the visual, sensory and motor cortices. Computational neuroscientists successfully devised models to explain neural phenomena encountered in the frontoparietal network. However, unlike other natural sciences computational neuroscience must not only account for neural phenomena, but also address information processing in the brain. Therefore, an ideal model must be capable of generating complex, goal-oriented behaviour while also accounting for neural phenomena. Dynamical systems have been widely applied to model neural dynamics, but also to generate control policies in robotics. This work proposes to leverage computing with dynamical systems to understand neural dynamics and complex behaviour expressed as control policies in goal-oriented systems.
To that end, this work utilises recent advances in deep reinforcement learning. Agents are trained to solve a variety of reinforcement learning problems including control of an anthropomorphic hand. Afterwards, dynamics recorded while the agent performed the task are critically assessed. Attractor networks and central pattern generators are found as general computational strategies to solve for final state goals and periodic tasks (e.g. walking), respectively. These findings demonstrate the capability of computing with dynamical systems to account for neural dynamics and information processing. Computing with dynamical systems when applied to more complex tasks could pave the way to generating ever more sophisticated hypotheses about complex behaviour in artificial and biological systems.
Wednesday 08 July
Prof. Elia Formisano | Research line leader MaCSBio
|15:05||Keynote: Translational Neuromodeling, Computational Psychiatry and Computational Psychosomatics
Prof. dr. Klaas Enno Stephan | Director Translational Neuromodeling Unit, University of Zurich, Switzerland
|15:50||Predicting neuronal response properties from hemodynamic responses in the auditory cortex
Isma Zulfiqar | PhD alumnus MaCSBio, Researcher, Maastricht University
|16:20||The Behavioral Arnold Tongue and the Perception and Learning of Figure-ground Distinction
Maryam Karimian | PhD alumnus MaCSBio, Researcher, Maastricht University
Translational Neuromodeling, Computational Psychiatry and Computational Psychosomatics
Klaas Enno Stephan | Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich
For many brain diseases, particularly in psychiatry, we lack clinical tests for differential diagnosis and cannot predict optimal treatment for individual patients. This presentation outlines a translational neuromodeling framework for inferring subject-specific mechanisms of brain disease from non-invasive measures of behaviour and neuronal activity. Guided by clinical theories of maladaptive cognition and aberrant brain-body interactions, generative models can be developed that have potential as “computational assays”.
Evaluating the clinical utility of these assays requires prospective patient studies that address concrete clinical problems, such as treatment response prediction. If successful, computational assays may help provide a formal basis for differential diagnosis and treatment predictions in individual patients and, ultimately, facilitate the construction of mechanistically interpretable disease classifications.
Predicting neuronal response properties from hemodynamic responses in the auditory cortex
Isma Zulfiqar | PhD alumnus MaCSBio, Researcher, Maastricht University
The differences in neuronal response properties between the rostral and caudal streams in the auditory cortex are thought to support the specialized functions of ‘what’ and ‘where’ (or ‘how’) processing, respectively. While the responses in rostral and caudal auditory belt regions have also been examined in the human auditory cortex using fMRI, it has been challenging to relate observed differences in fMRI responses along the rostral-caudal axis of the human temporal lobe to fundamental neuronal response properties.
To bridge this gap, we presented a forward model combination of neuronal model of the auditory cortex with physiological model of hemodynamic BOLD response. Our simulations showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results suggest two parallel streams of complimentary information processing in human auditory cortex.
The Behavioral Arnold Tongue and the Perception and Learning of Figure-ground Distinction
We combined psychophysical experimentation with Kuramoto phase oscillator modeling approaches to investigate the role of neural gamma oscillations in figure-ground distinction in visual images. Thus, a set of stimuli constituted from Gabor annuli is presented to a group of human subjects in the experiments and also, used as the input for the computational model. Our main hypotheses are as follows:
1- The likelihood of figure-ground distinction would be a function of contrast variance and spacing scale between the Gabor annuli in the stimuli. That is investigated by constructing the behavioral Arnold tongue.
2- Through learning, the likelihood of figure-ground distinction would increase. In other words, the region in the behavioral Arnold tongue that indicates highly probable figure-ground distinction would grow.
3- Perceptual learning is specific for the position of the figure in the visual field. That means our findings would support the idea of bottom-up learning, which involves plastic changes in lower-level cortical areas such as V1.
Thurday 09 July
Friday 10 July
Prof. Ilja Arts | Scientific director MaCSBio
|15:05||Keynote presentation 2
Prof. Natasa Pržulj | Professor of Biomedical Data Science at Computer Science, University College London, and ICREA Research Professor at Life Sciences Department, Barcelona Supercomputing Center
|15:50||Stratifying cellular metabolism during weight loss
Dr. Samar Tareen | PhD alumnus MaCSBio, Postdoc, The Brabaham Institute, Cambridge, UK
|16:20||A computational model of postprandial adipose tissue lipid metabolism
Shauna O’Donovan | PhD alumnus MaCSBio, Researcher Postdoc Wageningen University
Prof. Klaas Enno Stephan
Klaas Enno Stephan is a computational neuroscientist and medical doctor. He is presently Full Professor for Translational Neuromodeling at UZH and ETH Zurich. Additionally, he is Honorary Principal at the Wellcome Centre for Neuroimaging, London, and External Scientific Member of the Max Planck Institute for Metabolism Research, Cologne.
Prof. Nataša Pržulj
Prof. Przulj initiated extraction of biomedical knowledge from the wiring patterns (topology, structure) of "Big Data" real-world molecular (omics) and other networks.