Analysis of system disorders in liver metabolism with the SteatoNet model

Sistemski pristopi so ključni pri razumevanju kompleksnih bioloških sistemov, zato smo jih uporabili na primeru jeter. S sistemskimi pristopi k analizi na osnovi modela SteatoNet smo pridobili vpogled v celovit metabolizem jeter ter komunikacijo med jetri in drugimi tkivi. Različne prilagoditve modela SteatoNet aktualnim področjem raziskav s področja hepatologije so odprle vrata razumevanju bioloških procesov, ki jih še ne razumemo v celoti. Z modelom SteatoNet smo in silico testirali izbitje gena Cyp51 iz biosinteze holesterola v jetrih, kar je pokazalo, da adipozno tkivo poveča sintezo ketonskih teles in zmanjša hidrolizo trigliceridov. Pri modeliranju sinteze holesterola smo uporabili tako naše lastne eksperimentalne podatke iz meritev v jetrih miši s pogojno izbitim genom Cyp51 v jetrih kot tudi podatke iz literature. Med procesom validacije modela sinteze holesterola smo kot izjemno pomemben dejavnik opredelili tudi hormonski vpliv, zato smo model SteatoNet prilagodili razlikam med spoloma. Razlike so glede na podatke iz literature v izražanju spolnih hormonov in dinamiki rastnega hormona. Prilagojeni model smo poimenovali LiverSex in predstavlja prvi matematični model presnove jeter, s katerim lahko preiskujemo razlike med spoloma. Model LiverSex smo validirali z eksperimentalnimi podatki, pridobljenimi iz miši, na katerih smo preverjali vpliv prehrane z različnimi laboratorijskimi krmami. Z občutljivostno analizo modela LiverSex smo opredelili kritične točke v razvoju nealkoholne zamaščenosti jeter pri obeh spolih. Rezultat občutljivostne analize modela LiverSex je izpostavil, da je prav komunikacija med jetri in maščevjem ključna na prvi stopnji razvoja nealkoholne zamaščenosti jeter ter da je to možen razlog za manjšo prevalenco bolezni pri ženskah pred menopavzo. VLDL, shranjevanje trigliceridov v maščobnih kapljicah in/ali razpad maščobnih kislin v ketonska telesa verjetno predstavljajo zaščitne mehanizme, ki ženske pred menopavzo ščitijo pred razvojem nealkoholne zamaščenosti jeter. Pri razgradnji alkohola imajo glavno vlogo prav jetra, zato smo model SteatoNet prilagodili tudi vplivu alkohola na jetra. V model smo vstavili presnovo alkohola, ki inhibira dva koraka glukoneogeneze, in sicer presnovo piruvata v laktat in oksaloacetata v malat. Tako je nastal model StAlco, ki opisuje biokemijske hepatične posledice prekomernega uživanja alkohola. Model StAlco smo validirali s podatki iz literature. Modela LiverSex in StAlco sta prva računska modela za raziskovanje spolnega dimorfizma jeter in vpliva alkohola na jetra. Predstavljata začetno točko za še boljše razumevanje povezav med jetri in drugimi organi v smislu nastanka z jetri povezanih bolezni. Tovrstne prilagoditve dajejo priložnost novim aplikacijam, kot je npr. personaliziran pristop k diagnostiki in zdravljenju kompleksnih bolezni jeter.Systems approaches are crucial for understanding of complex biological systems, such as liver. With systems approaches based on the SteatoNet model, we gained novel insights into the liver metabolism and liver interactions with other tissues. Various adaptations of the SteatoNet to currently actual areas of hepatology research, have opened the door to understanding the biological processes that are not yet fully understood. With SteatoNet, we tested the in silico liver knock-out of the Cyp51 gene from cholesterol biosynthesis. Simulation results showed that adipose tissue increases the synthesis of ketone bodies and reduces the triglyceride hydrolysis. For modeling the cholesterol synthesis we used our own experimental data as well as data from literature. During the validation, hormonal effects were also identified as extremely important factors. Therefore, we have adapted the SteatoNet to gender differences. Main differences are in expression of sex hormones and in the growth hormone release. The adapted model was named LiverSex and it represents the first mathematical model of hepatic metabolism related to gender differences. The LiverSex was validated with experimental data obtained from mice in which we examined the effect of diet with different laboratory chows. With the sensitivity analysis of the LiverSex we identified critical points in the development of non-alcoholic fatty liver in both sexes. The result of a sensitivity analysis of LiverSex emphasized that communication between the liver and adipose tissue is crucial at the first stage in the development of non- alcoholic fatty liver disease, and that this is a possible reason for a lower prevalence of disease in women before menopause. VLDL, the storage of triglycerides in fat droplets, and/or the decomposition of fatty acids into the ketone body are likely the mechanisms that protect women from the development of non-alcoholic fatty liver disease before menopause. The liver has the main role in the decomposition of alcohol. We have adapted the SteatoNet to the hepatic effect of alcohol. SteatoNet was extended to StAlco model with the introduction of alcohol metabolism as well as inhibition of two gluconeogenesis steps: the reaction of pyruvate to lactate in oxaloacetate to malate. StAlco can describe the biochemical hepatic effects of excessive alcohol consumption and was validated with literature data. The LiverSex and StAlco are the first computational models for exploring influences of sexual dimorphism and the alcohol on the liver. They represent an excellent starting point for an even better understanding of liver communication with other organs, in terms of liver-related disease. Such adaptations give an opportunity to new applications, such as a personal approach to diagnose and treat complex liver related diseases

Closing the loop: A combined computational modeling and experimental approach provides novel insights into immune cell signaling systems and their global effects.

Systems biology is an approach that marries complimentary disciplines, encouraging the use of quantitative methods to help define, explain, and predict biological processes. By building computational models of biological systems, we can pose new biologically motivated questions and make falsifiable, quantitative predictions. In this thesis I will discuss the cycle of model building and experimental validation, and how it has provided insight into poorly and understood systems and allowed us to predict the effects of perturbations on these systems, which could have real and significant effects in human health and medicine. First, we model the activation of neutrophils in sepsis. By fitting a single model to two sets of data, coming from animals that survive and succumb to the same bacterial challenge, we create a realistic representation of biological variation, showing how a single network architecture can lead to different outcomes. Additionally, this method allows us to identify markers for sepsis susceptibility and identify and optimize a potential treatment option to lead to improved outcomes. Next, we model signaling downstream of the T cell receptor, and how this leads to differentiation decision making in CD4 T cells. By modeling the dynamics of this signaling network under varying antigen doses, we are able to identify network elements critical to dose discrimination, leading to the production of Treg cells following low dose stimulation and Th cells following high dose stimulation. We can then perturb these elements of the network, to potentially fine tune mature T cell populations to alter the trajectories of autoimmune disorders or cancer. Finally, we model the dynamics of IL-17 signaling. This allows us to understand how ubiquitin scaffolds form following cytokine stimulation, leading to the activation of NF-B, and how the ubiquitin editing enzyme A20 acts as a negative feedback regulator by breaking these chains. This allows us to better understand ubiquitin oligomerization as a fulcrum in the system, and how changes in A20 and ubiquitin binding proteins lead to different profiles of NF-B activation and could play a role in inflammatory disorders