Miha Moškon

Large‐scale computational models of liver metabolism: How far from the clinics?

Understanding the dynamics of human liver metabolism is fundamental for effective diagnosis and treatment of liver diseases. This knowledge can be obtained with systems biology/medicine approaches that account for the complexity of hepatic responses and their systemic consequences in other organs. Computational modeling can reveal hidden principles of the system by classification of individual components, analyzing their interactions and simulating the effects that are difficult to investigate experimentally. Herein, we review the state‐of‐the‐art computational models that describe liver dynamics from metabolic, gene regulatory, and signal transduction perspectives. We focus especially on large‐scale liver models described either by genome scale metabolic networks or an object‐oriented approach. We also discuss the benefits and limitations of each modeling approach and their value for clinical applications in diagnosis, therapy, and prevention of liver diseases as well as precision medicine in hepatology. (Hepatology 2017;66:1323‐1334).

Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data

Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the systems dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

LiverSex Computational Model: Sexual Aspects in Hepatic Metabolism and Abnormalities

The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1,000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individuals disease outcomes and treatment options. Systems approaches including mathematical modeling can aid importantly in understanding the multifactorial liver disease etiology leading toward tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present LiverSex, the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on in silico liver model SteatoNet and the object-oriented modeling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced in silico with the uniform model SteatoNet. LiverSex represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.

Computational modelling of liver metabolism and its applications in research and the clinics

Computational models of liver metabolism are gaining an increasing importance within the research community. Moreover, their first clinical applications have been reported in recent years in the context of personalised and systems medicine. Herein, we survey selected experimental models together with the computational modelling approaches that are used to describe the metabolic processes of the liver in silico. We also review the recent developments in the large-scale hepatic computational models where we focus on object-oriented models as a part of our research. The object-oriented modelling approach is beneficial in efforts to describe the interactions between the tissues, such as how metabolism of the liver interacts with metabolism of other tissues via blood. Importantly, this modelling approach can account as well for transcriptional and post-translational regulation of metabolic reactions which is a difficult task to achieve. The current and potential clinical applications of large-scale hepatic models are also discussed. We conclude with the future perspectives within the systems and translational medicine research community.

Hardware Implementation of FAST Algorithm for Mobile Applications

Simple inexpensive cameras are often built in small devices such as mobile phones or mp3 players. Besides the usual image recording, other ways of their use have been proposed which usually involve intensive image processing. In such processing, corner detection is often found as a preliminary operation. Many corner detection algorithms have been introduced, but due to their computational complexity very few are suitable for real-time applications. One of novel approaches to corner detection is the so called FAST algorithm which is specially optimized for speed. However, on simple and slow devices even this algorithm can be too slow and energy consuming when executed on the in-built processor. In this paper we present hardware implementation of FAST algorithm, capable of processing images at constant speed of one pixel per clock. The results showed that nearly forty times faster corner detection could be achieved on mobile object detection and localization application, if the existing software detector is replaced by our hardware module.

Systematic approach to computational design of gene regulatory networks with information processing capabilities

We present several measures that can be used in de novo computational design of biological systems with information processing capabilities. Their main purpose is to objectively evaluate the behavior and identify the biological information processing structures with the best dynamical properties. They can be used to define constraints that allow one to simplify the design of more complex biological systems. These measures can be applied to existent computational design approaches in synthetic biology, i.e., rational and automatic design approaches. We demonstrate their use on a) the computational models of several basic information processing structures implemented with gene regulatory networks and b) on a modular design of a synchronous toggle switch.

Modelling and Analysing the Information Processing Capabilities of Simple Biological Systems

Abstract Biological systems that present basic logic primitives for information processing have already been realized. Models for simulating their dynamics have also been implemented. However there is a lack of metrics that would objectively evaluate the information processing capabilities of these primitives and possibilities of their interconnectivity. With the introduction of such processing and performance descriptive quantities complex biological systems capable of information processing could be built more straightforwardly. That would bring us closer to the realization of a biological computer.

Semi-quantitative Modelling of Gene Regulatory Processes with Unknown Parameter Values Using Fuzzy Logic and Petri Nets

Petri nets are a well-established modelling framework in life sciences and have been widely applied to systems and synthetic biology in recent years. With the various extensions they serve as graphical and simulation interface for both qualitative and quantitative modelling approaches. In terms of quantitative approaches, Stochastic and Continuous Petri nets are extensively used for modelling biological system’s dynamics if underlying kinetic data are known. However, these are often only vaguely defined or even missing. In this paper we present a fuzzy approach, which can be used to model biological processes with unknown kinetic data in order to still obtain quantitatively relevant simulation results. We define fuzzy firing rate functions, which can be used in Continuous Petri nets and Address for correspondence: jure.bordon@fri.uni-lj.si ∗The research was partially supported by the scientific-research programme Pervasive Computing (P20359, financed by the Slovenian Research Agency in the years from 2009 to 2017), by the basic research and application project Designed cellular logic (J1-6740, financed by the Slovenian Research Agency in the years from 2014 to 2017). Results presented here are in scope of PhD thesis that is being prepared by Jure Bordon, University of Ljubljana, Faculty of Computer and Information science. Corresponding author

Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks

BackgroundData-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models.ResultsWe propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small.ConclusionsWith the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology.

Computational design of synchronous sequential structures in biological systems

Abstract Numerous applications of synthetic biology require the implementation of scalable and robust biological circuits with information processing capabilities. Basic logic structures, such as logic gates, have already been implemented in prokaryotic as well as in eukaryotic cells. Biological memory structures have also been implemented either in vitro or in vivo . However, these implementations are still in their infancy compared to their electronic equivalents. Their response is mainly asynchronous. We may learn from electronic computer systems that robust and scalable computing devices can be implemented only with edge-triggered synchronous sequential structures. Implementation of such structures, however, has yet to be performed in the synthetic biological systems even on the conceptual level. Herein we describe the computational design and analysis of edge-triggered D flip-flop in master–slave configuration based on transcriptional logic. We assess the robustness of the proposed structure with its global sensitivity as well as parameter sweep analysis. Furthermore, we describe the design of a robust Johnson counter , which can count up to 2 n cellular events using a sequence of n flip-flops. Changing the state of the counter is edge-triggered either with synchronization, i.e. clock signal, or with a pulse, which corresponds to the occurrence of observed event within the cellular environment. To the best of our knowledge this represents the design of the first biological synchronous sequential structure on such level of complexity.