You are here: TUCS > RESEARCH > Research Units > Computational Biomodeling Laboratory (Combio Lab)
Computational Biomodeling Laboratory (Combio Lab)
The research of the laboratory centers on computational methods for modelling biochemical systems. The general interest of the laboratory is gaining an understanding of the fundamental computational and information-processing principles behind the functioning of bio-systems. We have considerable expertise in building discrete models, based on combinatorics, graph theory, and stochastic processes. We are also experts in evaluating such models against experimental data, discovering their control structure, quantitative model comparison and quantitative model refinement.
The laboratory has hosted in the last 5 years 5 postdoctoral students and has graduated 3 TUCS PhD students, 2 of them receiving their degrees with honors. The scientific volume of the unit has been consistently very good, both in quality and in quantity. The laboratory is actively involved in the editorial boards of several journals and the program committees of the most relevant conference in its field of research.
Research Unit Web Page: http://combio.abo.fi/
Leader of the unit
Ion PetreResearchers
Cristian Gratie Vladimir Rogojin Eugen Czeizler Dwitiya TyagiDoctoral Students
Bogdan Iancu Sepinoud Azimi Diana-Elena Gratie Charmi Panchal Muhammad Usman Krishna KanhaiyaUndergraduate Students
Tolou Shadbahr Fatimah Shokri Nebiat IbssaProjects
Quantitative strategies for the self-assembly of intermediate filaments
In our research we concentrate on the process of in vitro self-assembly of intermediate filaments from tetrameric vimentin. We investigate different plausible strategies for filament elongation through mathematical modelling, model fitting, model validation and sensitivity analysis. In the assessment of the potential variants the focus is on properties such as scalability, robustness and ability to explain experimental data. This systematic approach enables the formulation of certain hypotheses about how the still little-known process of filament self-assembly is executed. Based on this hypotheses future biological experiments that would verify them are proposed. This project is an example of a hypothesis-driven research in the field of systems biology.
Network Controllability (Academy of Finland, 2013–2017)
Networks are all around us. The first example crossing one’s mind might be the World Wide Web, but probably not the only one. Our world is full of social structures, networks, where individuals are connected with each other by different means such as mobile phones or transportation. A network can be represented by nodes and by edges, where edges describe connections between the nodes. Imagine airports with flight connections. There is continuous flow of people travelling through the cities, and the biggest airports are the system hubs. Networks can also be microscopic, for example metabolic networks or gene regulatory networks in a cell. Common to all is that the state of the system typically changes with time. Hence, we call them dynamic networks. With many systems, it would be in our interest to steer them or even control them toward some desired state. However, this has turned out to be a very challenging task.
Within this project we aim to better understand the fundamental principles of network controllability. We develop novel and extend the existing methods on controlling networks. In particular, we are interested in large and complex biological networks, which are extremely difficult to control. Therefore, parallel to full control we are investigating also how such systems could be controlled partially. This kind of approach might, indeed, enable many practical applications, which otherwise would be infeasible simply from engineering perspective.
We exploit the control principles in order to predict the best way to engineer the networks, which we aim to control. In particular, we apply the methodology on two biological systems, cancer and renewable energy production. Malignant growth tends to arise when there is ‘too much energy in a system’. Hence, controlling cancer would mean steering the tumor towards non-growing state. Our ambition is to re-program cancer networks by identifying cell’s own pathogenic addictions and other vulnerabilities. Multi-targeting those identified nodes can improve anti-cancer therapeutics. In renewable energy production we optimize the metabolic flux to obtain maximal fuel yield. While the practical goals with energy aspect are rather opposite, the ultimate goal in both systems is to be able to control them. Obviously, the generic approaches and tools can be used in other applications. They also serve as a complement to traditional micro-scale engineering.
Funding instrument: Academy of Finland, Synthetic Biology Programme (FinSynBio) 2013-2017.
Quantitative model refinement (Academy of Finland, 2013–2017)
Much effort is currently invested in developing larger, more finely-grained computational models in many branches of science, supported by developments in computing infrastructure and by advances in quantitative experimental measuring techniques. This is supported by developments in the computing infrastructure and by advances in quantitative experimental techniques.
We focus in this project on computational techniques allowing the quantitative refinement of a model without altering its numerical fit and validation. Our research addresses two main problems in the design of mathematical models in systems biology: (i) the quantitative fit and validation of a large model is a computationally difficult problem; (ii) changing a model (e.g., adding details to it) implies redoing the work on the numerical fit and validation of the model. Our proposed methodology builds on the expertise gained in computer science in (qualitative) program refinement, extending it in a fundamental way to the realm of quantitative biomodels.
Funding instrument: Academy of Finland 2013-2017.
Quantitativa modeling of protein self-assembly
In our research we concentrate on the process of in vitro self-assembly of intermediate filaments from tetrameric vimentin. We investigate different plausible strategies for filament elongation through mathematical modelling, model fitting, model validation and sensitivity analysis. In the assessment of the potential variants the focus is on properties such as scalability, robustness and ability to explain experimental data. This systematic approach enables the formulation of certain hypotheses about how the still little-known process of filament self-assembly is executed. Based on this hypotheses future biological experiments that would verify them are proposed.
Boolean and multi-value logic for biological network analysis
The aim is to develop a methodology for the functional analysis of biological networks with a focus on describing the contribution of their various modules to the global quantitative behavior of the networks. The formalism we develop aims to describe how a (quantitative) property of a biological network in terms of combinations of modules being knocked-in or –out (through Boolean logic) or in terms of combination of different levels of activity for the modules (through multi-valued logic). The approach is highly relevant for reverse engineering of biological networks, in an effort to identify their functional motifs, as well as for synthetic biology, for engineering a desired behavior from a given library of modules. Our analysis will first consider two case studies: (i) EGFR (Epidermal growth factor receptor) signaling pathway which is essential for normal cell growth and development; (ii) a computational platform for high-throughput vaccine development.
Computational gene assembly
The process of gene assembly has the attention of the Biocomputing community for several years already. It is by now clear that the process of gene assembly in ciliates is highly computational: it turns out that ciliates "know" one of the basic data structures of Computer Science - the linked list - and use it in a very elegant pattern matching manner in the process of gene assembly! We are investigating a set of three molecular operations that accomplishes the gene assembly through the "fold and recombine" paradigm. We introduced the mathematical model of pointer reduction systems to formalize the micronuclear gene patterns (through permutations, strings and graphs) and the gene assembly process. Our investigation of these systems resulted in a uniform explanation of all known experimental results concerning gene assembly in ciliates.
Computational modelling with reaction systems
A biochemical network consists of a great number of reactions that cooperate with one another with the fundamental goal of keeping the cell alive. Reaction systems consider only two possible regulatory mechanisms: facilitation and inhibition. A biochemical reaction concerns a finite set of reactants provided that all the reactants involved in that particular reaction are present in a given state and all of its inhibitors are absent. We demonstrate that reaction systems are rich enough to capture the essential characteristics of quantitative models (e.g., ODE-based models) and also show the expressive power of reaction systems when dealing with wide range of problems from biology to computer science (e.g., exact pattern matching in strings). Our aim is also to introduce a methodology that enables us to translate a quantitative model to a qualitative reaction system based one without losing any essential characteristics of the former one.
Large-scale modelling
It is common practice in the field of biomodelling to represent different aspects of the same biological phenomena with different formalisms and at different abstraction levels. The choice of a particular modeling approach and abstraction level depends on the initial understanding of the phenomena, availability of the relevant initial and experimental data, scale, mathematical and computational complexity of the model, the researcher’s own competence, etc. On the other hand, individual research teams usually focus in depth at some particular aspects of a cellular biology, while leaving the “great picture” mostly untouched. This is due to the fact that building a comprehensive model for a living cell would require a tremendous amount of experimental data, deep and extremely versatile knowledge of the cell’s biology and processes, serious theoretical and computational challenges and large interdisciplinary research team. In our project, rather than attempting to build large and detailed computational model for a living cell from scratch, we are developing approaches to integrate various modelling/simulating techniques into a comprehensive modeling framework and implement it as a public software platform.