Hybrid automata as coalgebras

Publicado em "Theoretical aspects of computing - ICTAC 2016: 13th International Colloquium, Taipei, Taiwan, ROC, October 24–31, 2016, Proceedings". ISBN 978-3-319-46749-8Able to simultaneously encode discrete transitions and continuous behaviour, hybrid automata are the de facto framework for the formal specification and analysis of hybrid systems. The current paper revisits hybrid automata from a coalgebraic point of view. This allows to interpret them as state-based components, and provides a uniform theory to address variability in their definition, as well as the corresponding notions of behaviour, bisimulation, and observational semantics.FCT grants SFRH/BD/52234/2013, SFRH/BSAB/ 113890/2015ERDF - European Regional Development Fund, through the COMPETE Programme, and by National Funds through FCT within project PTDC/EEI-CTP/4836/201

Conservation of Mass Analysis for Bio-PEPA

AbstractThis paper describes a static analysis for Bio-PEPA models based on the notion of conservation of mass. Failure to obey the law of mass conservation can be an indication that there is an error in the model description. Here we focus on the use of invariant analysis to identify such potential flaws in models. We extend the basic technique to consider open models, in which it is possible to automatically ignore some causes of mass production or consumption that are unlikely to be errors. Our approach is an improvement on direct application of invariant analysis because it does not depend on a deep understanding of the model and prior expectations of the sets of components which should have conserved mass. We demonstrate the use of our technique on a published model from the literature and explain how our analysis can be used to uncover potential problems in the model description. Of course, not all models which fail to conserve mass are flawed. Nevertheless, this represents an important method of model verification which can be applied before the model itself is evaluated — since the analysis does not depend on accurate dynamics it can be undertaken early in the model development process, before the model has been fully parameterised

The rule-based model approach. A Kappa model for hepatic stellate cells activation by TGFB1

International audienc

Parameter inference for stochastic biological models

PhD ThesisParameter inference is the field concerned with estimating reliable model parameters from data. In recent years there has been a trend in the biology community toward single cell technologies such as fluorescent flow cytometry, transcriptomics and mass cytometry: providing a rich array of stochastic time series and temporal distribution data for analysis. Deterministically, there are a wide range of parameter inference and global optimisation techniques available. However, these do not always scale well to non-deterministic (i.e., stochastic) settings — whereby the temporal evolution of the system can be described by a chemical master equation for which the solution is nearly always intractable, and the dynamic behaviour of a system is hard to predict. For systems biology, the inference of stochastic parameters remains a bottleneck for accurate model simulation. This thesis is concerned with the parameter inference problem for stochastic chemical reaction networks. Stochastic chemical reaction networks are most frequently modelled as a continuous time discretestate Markov chain using Gillespie’s stochastic simulation algorithm. Firstly, I present a new parameter inference algorithm, SPICE, that combines Gillespie’s algorithm with the cross-entropy method. The cross-entropy method is a novel approach for global optimisation inspired from the field of rare-event probability estimation. I then present recent advances in utilising the generalised method of moments for inference, and seek to provide these approaches with a direct stochastic simulation based correction. Subsequently, I present a novel use of a recent multi-level tau-leaping approach for simulating population moments efficiently, and use this to provide a simulation based correction to the generalised method of moments. I also propose a new method for moment closures based on the use of Padé approximants. The presented algorithms are evaluated on a number of challenging case studies, including bistable systems — e.g., the Schlögl System and the Genetic Toggle Switch — and real experimental data. Experimental results are presented using each of the given algorithms. We also consider ‘realistic’ data — i.e., datasets missing model species, multiple datasets originating from experiment repetitions, and datasets containing arbitrary units (e.g., fluorescence values). The developed approaches are found to be viable alternatives to existing state-ofthe-art methods, and in certain cases are able to outperform other methods in terms of either speed, or accuracyNewcastle/Liverpool/Durham BBSRC Doctoral Training Partnership for financial suppor

Hybrid Modeling of Cancer Drug Resistance Mechanisms

Cancer is a multi-scale disease and its overwhelming complexity depends upon the multiple interwind events occurring at both molecular and cellular levels, making it very difficult for therapeutic advancements in cancer research. The resistance to cancer drugs is a significant challenge faced by scientists nowadays. The roots of the problem reside not only at the molecular level, due to multiple type of mutations in a single tumor, but also at the cellular level of drug interactions with the tumor. Tumor heterogeneity is the term used by oncologists for the involvement of multiple mutations in the development of a tumor at the sub-cellular level. The mechanisms for tumor heterogeneity are rigorously being explored as a reason for drug resistance in cancer patients. It is important to observe cell interactions not only at intra-tumoral level, but it is also essential to study the drug and tumor cell interactions at cellular level to have a complete picture of the mechanisms underlying drug resistance. The multi-scale nature of cancer drug resistance problem require modeling approaches that can capture all the multiple sub-cellular and cellular interaction factors with respect to dierent scales for time and space. Hybrid modeling offers a way to integrate both discrete and continuous dynamics to overcome this challenge. This research work is focused on the development of hybrid models to understand the drug resistance behaviors in colorectal and lung cancers. The common thing about the two types of cancer is that they both have dierent mutations at epidermal growth factor receptors (EGFRs) and they are normally treated with anti-EGFR drugs, to which they develop resistances with the passage of time. The acquiring of resistance is the sign of relapse in both kind of tumors. The most challenging task in colorectal cancer research nowadays is to understand the development of acquired resistance to anti-EGFR drugs. The key reason for this problem is the KRAS mutations appearance after the treatment with monoclonal antibodies (moAb). A hybrid model is proposed for the analysis of KRAS mutations behavior in colorectal cancer with respect to moAb treatments. The colorectal tumor hybrid model is represented as a single state automata, which shows tumor progression and evolution by means of mathematical equations for tumor sub-populations, immune system components and drugs for the treatment. The drug introduction is managed as a discrete step in this model. To evaluate the drug performance on a tumor, equations for two types of tumors cells are developed, i.e KRAS mutated and KRAS wild-type. Both tumor cell populations were treated with a combination of moAb and chemotherapy drugs. It is observed that even a minimal initial concentration of KRAS mutated cells before the treatment has the ability to make the tumor refractory to the treatment. Moreover, a small population of KRAS mutated cells has a strong influence on a large number of wild-type cells by making them resistant to chemotherapy. Patient's immune responses are specifically taken into considerations and it is found that, in case of KRAS mutations, the immune strength does not affect medication efficacy. Finally, cetuximab (moAb) and irinotecan (chemotherapy) drugs are analyzed as first-line treatment of colorectal cancer with few KRAS mutated cells. Results show that this combined treatment could be only effective for patients with high immune strengths and it should not be recommended as first-line therapy for patients with moderate immune strengths or weak immune systems because of a potential risk of relapse, with KRAS mutant cells acquired resistance involved with them. Lung cancer is more complicated then colorectal cancer because of acquiring of multiple resistances to anti-EGFR drugs. The appearance of EGFR T790M and KRAS mutations makes tumor resistant to a geftinib and AZD9291 drugs, respectively. The hybrid model for lung cancer consists of two non-resistant and resistant states of tumor. The non-resistant state is treated with geftinib drug until resistance to this drug makes tumor regrowth leading towards the resistant state. The resistant state is treated with AZD9291 drug for recovery. In this model the complete resistant state due to KRAS mutations is ignored because of the unavailability of parameter information and patient data. Each tumor state is evaluated by mathematical differential equations for tumor growth and progression. The tumor model consists of four tumor sub-population equations depending upon the type of mutations. The drug administration in this model is also managed as a discrete step for exact scheduling and dosages. The parameter values for the model are obtained by experiments performed in the laboratory. The experimental data is only available for the tumor progression along with the geftinib drug. The model is then fine tuned for obtaining the exact tumor growth patterns as observed in clinic, only for the geftinib drug. The growth rate for EGFR T790M tumor sub-population is changed to obtain the same tumor progression patterns as observed in real patients. The growth rate of mutations largely depends upon the immune system strength and by manipulating the growth rates for different tumor populations, it is possible to capture the factor of immune strength of the patient. The fine tuned model is then used to analyze the effect of AZD9291 drug on geftinib resistant state of the tumor. It is observed that AZD9291 could be the best candidate for the treatment of the EGFR T790M tumor sub-population. Hybrid modeling helps to understand the tumor drug resistance along with tumor progression due to multiple mutations, in a more realistic way and it also provides a way for personalized therapy by managing the drug administration in a strict pattern that avoid the growth of resistant sub-populations as well as target other populations at the same time. The only key to avoid relapse in cancer is the personalized therapy and the proposed hybrid models promises to do that

Formal Modelling and Simulation of Biological Systems with Spatiality

In Systems Biology, spatial modelling allows an accurate description of phenomena whose behaviour is influenced by the spatial arrangement of the elements. In this thesis, we present various modelling formalisms with spatial features, each using a different abstraction level of the real space. From the formalisms with the most abstract notion of space, to the most concrete, we formally define the MIM Calculus with compartments, the Spatial P systems, and the Spatial CLS. Each formalism is suitable for the description of different kinds of systems, which call for the use of different space modelling abstractions. We present models of various real-world systems which benefit from the ability to precisely describe space-dependent behaviours. We define the MIM Calculus, inspired by Molecular Interaction Maps, a graphical notation for bioregulatory networks. The MIM Calculus provides high-level operators with a direct biological meaning, which are used to describe the interaction capabilities of the elements of such systems. Its spatial extension includes the most abstract notion of space, namely it only allows the modelling of compartments. Such a feature allows distinguishing only the abstract position where an element is, identified by the name of the compartment. Subsequently, we propose a spatial extension to the membrane computing formalism P systems. In this case, we follow a more precise approach to spatial modelling, by embedding membranes and objects in a two-dimensional discrete space. Some objects of a Spatial P system can be declared as mutually exclusive objects, with the constraint that each position can accommodate at most one of them. The distinction between ordinary and mutually exclusive objects can be thought of as an abstraction on the size of the objects. We study the computational complexity of the formalism and the problem of efficient simulation of some kinds of models. Finally, we present the Spatial Calculus of Looping Sequences (Spatial CLS), which is an extension of the Calculus of Looping Sequences (CLS), a formalism geared towards the modelling of cellular systems. In this case, models are based on two/three dimensional continuous space, and allow an accurate description of the motion of the elements, and of their size. In particular, Spatial CLS allows the description of the space occupied by elements and membranes, which can change their sizes dynamically as the system evolves. Space conflicts which may occur can be resolved by performing a rearrangement of elements and membranes. As example applications of the calculus we present a model of cell proliferation, and a model of the quorum sensing process in Pseudomonas aeruginosa

Coordination Issues in Complex Socio-technical Systems: Self-organisation of Knowledge in MoK

The thesis proposes the Molecules of Knowledge (MoK) model for self-organisation of knowledge in knowledge-intensive socio-technical systems. The main contribution is the conception, definition, design, and implementation of the MoK model. The model is based on a chemical metaphor for self-organising coordination, in which coordination laws are interpreted as artificial chemical reactions ruling evolution of the molecules of knowledge living in the system (the information chunks), indirectly coordinating the users working with them. In turn, users may implicitly affect system behaviour with their interactions, according to the cognitive theory of behavioural implicit communication, integrated in MoK. The theory states that any interaction conveys tacit messages that can be suitably interpreted by the coordination model to better support users' workflows. Design and implementation of the MoK model required two other contributions: conception, design, and tuning of the artificial chemical reactions with custom kinetic rates, playing the role of the coordination laws, and development of an infrastructure supporting situated coordination, both in time, space, and w.r.t. the environment, along with a dedicated coordination language