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The Mathematical Microscope by Johnny T. Ottesen Department of Science, Systems and Models Roskilde University, Denmark Copenhagen, IMFUFA, RUC REx workshop, 2009 Methodology for developing individual and patient specific models Data, pre-knowledge and structure Canonical models based on physiology Parameter identification and estimation Validation and analysis of models Suggestion and identification of biomarkers Integration of models and their interactions Data, pre-knowledge and structure All reliable models in physiology are based on solid knowledge and adequate data. Such knowledge and a huge data material related to diabetes do exist. Statistical methods such as approximated entropy (regularity statistics known from nonlinear dynamics) and generalized principal component analysis may reveal further information, which forthcoming models have to encompass. Canonical models based on physiology Models should be developed so they incorporate the responsible mechanisms for the modelled phenomena. In order to identify and estimate patient specific parameters in an effective and reliable way the number of parameters has to be kept as low as possible, thus all unimportant factors and elements should be excluded, i.e. the so-called principle of parsimony have to be obeyed. The models Should be based on first principles (conservation laws etc) whenever possible and the parameters shall have a physiological interpretations. Such models are denoted canonical models. Parameter identification and estimation The parameters have to be estimated by statistically founded algorithms (Extended Kalman filter, NelderMead algorithm combined with the simplex methods, Multidirectional Search, Particle filter/Sequential Monte Carlo (SMC) methods, generic algorithms, etc). Not all the parameters will necessarily be identificable due to limitation in available data. Thus the estimation process has to be an iterative procedure coupled with sensitivity analysis or generalised sensitivity analysis combined with subset selection strategies for instance. Validation and analysis of models An important part of the validation process (i.e. lack of falsification) is to compare model results with data (ideally with data independent of the data set used to estimate parameters). Model reduction, analysis of variations of submechanisms, analysis of stability and bifurcation, analysing possible limit cycle behaviour etc. are all supplementary validation methods. If a model fails to be validated it needs to be adjusted which often gives rise to new insights into the underlying physiology. Suggestions and identification of biomarkers When well validated models with patient specific estimated parameters exist the identification of potential biomarkers become achievable. Different groups of patients, i.e. pathological subjects versus non-pathological subjects can be examined. Some of the parameters for two different groups have to vary which suggest biomarkers. To determine whether there is a ‘real’ difference between values of the parameters (i.e. the biomarkers) within two groups or whether suggested biomarkers can identify variant causes of the illness (diagnosed by symptoms), statistical tests has to be performed. The biomarkers will for sure give rise to a classification of variants of the illness because they are born to agree with data from clinical diagnoses. Integration of models and their interactions It must be analysed how systems of coupled and integrated models behave compared to the isolated canonical (sub-)models (e.g. an insulin-glucose model coupled to a cardiovascular model or an exocytose model coupled to a insulin-glucose model at system level). In cases where biomarkers have to be adjusted, we expect that the adjustment is merely refinements of the original validated biomarker. Modeling point 1 Modeling may make the inaccessible accessible! When William Harvey discovered the circulation of blood in the cardiovascular system, he used mathematical modeling as a tool and as the argument. He obtained a contradiction (or a grotesque consequence), whereby he falsified the existing ancient paradigm. Harvey made the invisible capillary visible by use of a model (46 years before they became visible to the human eye by help of the light microscope). Modeling point 2 Modeling is an outstanding, a superb and a unrivalled way to obtain knowledge, insight and understanding in science. Parts which can’t be isolated experimentally may be studied (separately) by modeling. Change in heart rate for a normal young subject For fitting the heart rate curve one need 7 pieces of lines which demand 14 parameters in a maximum likelihood estimate (a least square formulation) Muscle sympathetic stimulation, central command or vestibular effect Conceptual Model Impulse Function: muscle sympathetic stimulation, central command or vestibular effect Sequential component model - suitable for sub-model validations and effective calculations Model compared to data Heart rate model predictions (blue trace) plotted against measured data (green trace). Left panel shows results from a healthy young subject, middle shows results from a healthy elderly subject, and right shows results from a hypertensive elderly subject. Young Subject Pressure Data (blue) and Mean Pressure (red) vs. Time Nerve Firing vs. Pressure (hysteresis loop is wide and closed) Parasympathetic (blue) and sympathetic (red) tones vs. Time: (significant dynamics in both tones) HR Data (blue) and HR Model (red) vs. Time Notice that the model gives access to the sympathetic and the parasympathetic tones (nerve activities) as functions of time. ealthy Elderly Subject Reduced pressure dynamics and lower resting state once regulated Loop very narrow with decreased slope; indicates reduced dynamics Tone value dynamics are greatly minimized, particularly sympathetic tone Smaller scale on HR dynamics, higher resting state, slower regulation Loop small, indicates reduced dynamics; loop also not closed Sympathetic tone response almost null aside from anticipation impulse Slightly decreased dynamics, regulation on slowe timescale than young subject Hypertensive Elderly Subject Reduced pressure dynamics upon standing, longer timescale for regulation Young Subject Pressure Data (blue) and Mean Pressure (red) vs. Time Nerve Firing vs. Pressure (hysteresis loop is wide and closed) Parasympathetic (blue) and sympathetic (red) tones vs. Time: (significant dynamics in both tones) HR Data (blue) and HR Model (red) vs. Time New concept lthy Elderly Subject New measure New clinical method Reduced pressure dynamics and lower resting state once regulated Loop very narrow with decreased slope; indicates reduced dynamics Tone value dynamics are greatly minimized, particularly sympathetic tone Smaller scale on HR dynamics, higher resting state, slower regulation Loop small, indicates reduced dynamics; loop also not closed Sympathetic tone response almost null aside from anticipation impulse Slightly decreased dynamics, regulation on slower timescale than young subject ypertensive Elderly Subject Reduced pressure dynamics upon standing, longer timescale for regulation Modeling point 3 Modeling is the only way to strictly define concepts well and to obtain values for measureable quantities (in combination with experiments) P(t ) 1 V (t ) C Modeling is and outstanding tool for suggesting new experiments which were hardly possible without the model (and leads to parameter estimations and model validation) Modeling point 4 Mathematics is able to unfold the influence that each of the processes has on the overall dynamical behaviour of a complex system Modern experimental science - especially modern biology - is very good at separating systems, into components simple enough for their structures and functions to be studied in isolation. Mathematical modelling is the only controlled way to put the pieces back together, with equations that represent the system's components and processes, as well as the structures and interactions. Modeling point 5 Modeling is an excellent tool for design purposes Modelling is an invaluable tool • for decision support in diagnostics and therapy (theranostics) • for the development of drugs (models make it possible to target the cause of a disease directly) • for developing and constructing industrial devises and equipments Modeling (main) point 6 Patient specific parameter estimation is the future – it is possible and it is an opportunity for pharmaceutical industry and medical doctors to target causes instead of treating symptoms Complex models with inaccessible parts and processes can be used for estimating quantities / parameters describing these inaccessible parts and processes Individual / patient specific measurements are performed indirectly by help of models and biomarkers are obtained A good subject Insulin/Glucose [mM] Insulin Glucose Time [min] Schematic representation of a compartmental delay model Dg Tgh Vg Kxgl Vi Vi Kxi Vg and Vi are the distribution volumes for Glucose (G) and Insulin (I). Dg stands for the glucose bolus administered; KxgI is the second order net elimination rate of glucose per unit insulin concentration; Kxi is the first order elimination rate of insulin; Tigmax Tgh is the net difference between glucose production and glucose elimination; Tigmax is the maximal rate of second phase insulin release Thank you for your attention