When the optimal is not the best: parameter estimation in complex biological models

Background: The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor conditions may result in biologically implausible values. Results: We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit. Conclusions: The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system, and point to the need of a theory that addresses this problem more generally

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

This is a post-peer-review, pre-copyedit version of an article published in Cluster Computing. The final authenticated version is available online at: https://doi.org/10.1007/s10586-017-0860-1[Abstract] Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation)Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad; TIN2013-42148-PMinisterio de Economía y Competitividad; TIN2016-75845-PXunta de Galicia ; R2016/045Xunta de Galicia; GRC2013/05

Brukarens roll i välfärdsforskning och utvecklingsarbete

Tekstene er fra forelesninger samt fra doktorantkurset "Brukarmedverkan i forskning och utvecklingsarbete inom hälso- och sjukvård, socialt arbete och omsorg". Kurset ble avholdt våren 2009.Fra omslag: På 1980-talet blev ”brukare” ett modeord i offentlig förvaltning och förvaltningsforskning. Termen betecknar den som använder sig av välfärdsservice (jfr. engelskans service user), eller ”slutmottagare” av offentlig nyttighet eller åtgärd. Brukare av välfärdstjänster vet hur hjälp och service fungerar i praktiken och kan därför ge synnerligen viktig återkoppling enligt devisen: ”Den som har skorna på fötterna vet var de skaver”. Välfärdsorganisationer har all anledning att involvera brukare i planering och policyarbete i syfte att utveckla förmågan att göra rätt saker. Det finns inte mycket dokumentation och forskning kring brukarmedverkan i utvecklingsarbete och forskning på välfärdsområdet. I synnerhet saknas kunskap om hur välfärdstjänster tas emot och realiseras i brukarens livssammanhang. En ambition i doktorandkursen ”brukarmedverkan i forskning och utvecklingsarbete inom hälso- och sjukvård, socialt arbete och omsorg” var att samla och presentera kunskaper på området. Kursen genomfördes våren 2009 i ett unikt samarbete mellan Karlstads Universitet, Sheffield University i England, Högskolan i Hedmark i Norge, Hälsohögskolan i Jönköping och Högskolan i Borås/FoU Sjuhärad Välfärd. Texterna i denna bok härrör från kursens föreläsningar och paperarbeten. De ger många exempel på hur brukare kan involveras i forskning och utvecklingsarbete, och presenterar en rad praktiska metoder för brukarsamverkan. Boken rekommenderas till välfärdens politiker och yrkespersoner, till studenter som förbereder sig för välfärdens yrken liksom till forskare och utvecklingsarbetare som vill utveckla samarbete med brukare och brukarorganisationer. Den vänder sig givetvis även till brukare och brukarorganisationer som vill engagera sig i forskning och utvecklingsarbete

Intercultural moments in translating and humanising the socio-legal system

This paper seeks to address the question how people go about intercultural differences in an institutional setting which aims to mediate between the socio-legal system and the ‘outsiders’ of the system, i.e. ordinary citizens, through an investigation of professional interactions between a legal advisor and her clients of Eastern European backgrounds in London. Drawing data from a linguistic ethnography, the analysis foregrounds the practice of resemiotisation and calibration. The second aim is to extend the notion of ‘intercultural moments’ and to explore its analytical benefits in understanding fleeting and seemingly mundane moments in encounters

On parametric sensitivity and structural robustness of cellular functions - the oscillatory metabolism of activated neutrophils

Robustness of cellular functions is a key property of living organisms. Modelling and analysis of the genetic and biochemical networks underlying specific functions will enable quantification of the robustness as well as identification of the specific mechanisms providing robustness. Studies on cellular robustness has so far largely focused on parametric sensitivities, i.e., robustness of functions (behavior) with respect to changes in model parameters. In this paper we argue that robustness analysis of cellular models also should encompass structural robustness, i.e., robustness with respect to perturbations in the model structure. This is important not only to quantify the robustness of the cell functions themselves, but equally important, to gain knowledge about the quality of the model as such. In particular, if the model displays poor robustness against structural perturbations this serves as an indication of a potentially highly uncertain model and hence care must be exercised when interpreting the obtained parametric sensitivities. We here propose a simple method for analysing structural robustness of functions related to bistability and periodic oscillations in intracellular networks. The method is applied to a model of the oscillatory metabolism of activated neutrophils (white blood cells) recently proposed in Olsen et al., Biophys J., 84:69-81, 2003. The model is found to be highly robust against parametric uncertainties, but is shown to display poor structural robustness. Indeed, attempting to divide the model into compartments, with the aim of emulating spatial distributions that exist in vivo, results in a qualitatively different model prediction

Reduction of a biochemical model with preservation of its basic dynamic properties

The complexity of full-scale metabolic models is a major obstacle for their effective use in computational systems biology. The aim of model reduction is to circumvent this problem by eliminating parts of a model that are unimportant for the properties of interest. The choice of reduction method is influenced both by the type of model complexity and by the objective of the reduction; therefore, no single method is superior in all cases. In this study we present a comparative study of two different methods applied to a 20D model of yeast glycolytic oscillations. Our objective is to obtain biochemically meaningful reduced models, which reproduce the dynamic properties of the 20D model. The first method uses lumping and subsequent constrained parameter optimization. The second method is a novel approach that eliminates variables not essential for the dynamics. The applications of the two methods result in models of eight (lumping), six (elimination) and three (lumping followed by elimination) dimensions. All models have similar dynamic properties and pin-point the same interactions as being crucial for generation of the oscillations. The advantage of the novel method is that it is algorithmic, and does not require input in the form of biochemical knowledge. The lumping approach, however, is better at preserving biochemical properties, as we show through extensive analyses of the models

A systems biology analysis connects insulin receptor signaling with glucose transporter translocation in rat adipocytes

Type 2 diabetes is characterized by insulin resistance, which arises from malfunctions in the intracellular insulin signaling network. Knowledge of the insulin signaling network is fragmented, and because of the complexity of this network, little consensus has emerged for the structure and importance of the different branches of the network. To help overcome this complexity, systems biology mathematical models have been generated for predicting both the activation of the insulin receptor (IR) and the redistribution of glucose transporter 4 (GLUT4) to the plasma membrane. Although the insulin signal transduction between IR and GLUT4 has been thoroughly studied with modeling and time-resolved data in human cells, comparable analyses in cells from commonly used model organisms such as rats and mice are lacking. Here, we combined existing data and models for rat adipocytes with new data collected for the signaling network between IR and GLUT4 to create a model also for their interconnections. To describe all data (>140 data points), the model needed three distinct pathways from IR to GLUT4: (i) via protein kinase B (PKB) and Akt substrate of 160 kDa (AS160), (ii) via an AS160-independent pathway from PKB, and (iii) via an additional pathway from IR, e.g. affecting the membrane constitution. The developed combined model could describe data not used for training the model and was used to generate predictions of the relative contributions of the pathways from IR to translocation of GLUT4. The combined model provides a systems-level understanding of insulin signaling in rat adipocytes, which, when combined with corresponding models for human adipocytes, may contribute to model-based drug development for diabetes

A multi-scale in silico mouse model for diet-induced insulin resistance

Insulin resistance causes compensatory insulin production, which in humans can eventually progress to β-cell failure and type 2 diabetes (T2D). This disease progression involves multi-scale processes, ranging from intracellular signaling to organ and whole-body level regulations, on timescales from minutes to years. T2D progression is commonly studied using overfed and genetically modified rodents. Available multi-scale data from rodents is too complex to fully comprehend using traditional analysis, not based on mathematical modelling. To help resolve these issues, we here present an in silico mouse model, featuring 38 ordinary differential equations and 78 parameters. This is the first mathematical model that simultaneously explains (chi-square cost=28.1 <51 =cut-off, p = 0.05) multi-scale mouse insulin resistance data on all three levels – cells, organs, body – ranging from minutes to months. The model predicts new independent multi-scale simulations, on e.g., weight and meal response changes, which are corroborated by our own new experimental data. The thus validated model provides insights and non-trivial predictions regarding complex non-measured processes, such as the relation between insulin resistance and insulin-dependent glucose uptake for adipose tissue. Finally, we add a β-cell failure module to the in silico mouse model to simulate different human-like scenarios of progression towards T2D. In summary, our in silico mouse model is an extendable and interactive knowledge-base for the study of T2D, which could help simulate treatment scenarios in rodents and translate results to the human situation