[SOUND] Hello, welcome to this course that will introduce you to the emerging field of systems biology. This course can be taken as a stand-alone course. It will give you a brief overview of this new discipline in biological sciences. Or it can be taken as a part of a five course series that over the year can lead to a certificate in systems biology. So what is systems biology? Over the past 50 years or so, we have gained a really good understanding of the various parts or components of what may for cells, tissues, and organs startings from genetics and genomics, where the genes of many organisms including that humans of been fully decoded and sequenced. To biochemistry, where many proteins have been studied. For both structure and function and in depth to understand what the proteins do by themselves, and what they do by interacting with their partners. To cellamonular biology where we study how proteins are organized to form sub-cellular systems such as the mitochondria or nucleus or the cell cytoskeleton to physiology which can be it is focused on the study of functions of cells, tissues, organs, and organisms. There has been a vast amount of knowledge that has been gained or. This knowledge sort of allows us now to gain a perspective where one can start a level of genes and go to the level of functions of the cell tissue and organismal level and understand how the information in genes are decoded to form proteins. And how proteins interact with other components of the cellsus lipids and sugars and nucleotides and so on and all of these together give rise to cellular tissue in organismal functions. It is these kind of integrated study that is called systems biology. So the void itself is a marphis term, it can mean stems at various levels of biological organization. So one might have a system at the level of a cell or at the level of a tissue or organ, or the level of the whole organisms. We're going back to the other end, it may be sub cellular levels, like I said before, either mitochondria, or nucleus can also be called systems. So there isn't one fixed definition of what systems biology will be or is and one can,different people can have different perspective of how the field is growing and sort of focused on. In this course I will focus largely on systems biology at the cellular level in mostly mammalian cells because this provides us a natural segue into how cells become tissues and organs. And will allow me towards the end of this course to describe briefly a systems approach as to the study in medicine and pharmacology and therapeutics. So, there are many ways to organize this course and there is probably is not any one correct or wrong way to do this. So, I've organized this course to sort of follow the natural flow in which there really is biological disciplines sort of grew and came to be. So the course starts with sort of molecular components by chemistry and cell biology, how individual components, genes, proteins, etc., were discovered and their functions analyzed. And how these were put together to form smaller sub-cellular systems such as a signalling pathway or a metabolic pathway or the sort of a cellular machine such as the cytoskeleton or the electrical machinery. The groups of channels in the cell membrane and how these were studied both experimentally and how they can be studied computationally using dynamical model. To sort of both understand function and predict future behavior. I then move on to developing new, or describing new technologies, the omics technologies that allow us to study changes in many, many components at a time. These kinds of omic technologies, genomic and perdiomics and so on have become the hallmark or sort of the defining characteristics of the experimental approaches used in systems biology. And these technologies allow us to do survey type experiments where we can study how many components. So for in the case of gene expression, or microarray experiments, how levels of many messenger RNAs change in response to one or more perturbations. Similarly, perdiomics allows us to study how where many proteins may change in response to a certain kind of perturbation. These large numbers and the large datasets that come from these omix experiments require us to organize these data in an appropriate manner that can be used for analysis and extract knowledge. And sometimes there is large data that is now increasingly being called big data. The field is devoted to their analysis and organization of such data is called bioinformatics. These data can be analyzed using other ideas like statistical and mathematical tools. And the field of mathematics that deals with this kind of analysis of large datasets is called graph theory, or network analysis. And we will study, we will learn how network analysis has been very useful in understanding how systems are organized. Finally, I will end with some brief description of how system biology, or systems approach is going to be very useful in the field of medicine and therapeutics. As this is an online course, there aren't going to be any experiments to do, and also I will not really have you run any simulations or do any network or computations right now. Largely, this is going to be a thinking course rather than a doing course. But this what I mean is that, I want you to get a sense of how one can use quantitative reasoning to deal with large data sets. And how understanding what kinds of mathematical representation are appropriate for different kinds of biological questions and subcellular systems. And how mathematical analysis can provide more deep understanding of how, Behaviors occur and emerge, and how one can get predictive value from computational analysis. This course, of course, will be followed by three other courses, one focus on experimental technologies in systems biology, which will really be, if you want to call it, sort of a show and tell course where we'll describe to you how these various technologies are used and what they can be used for in the context of systems biology experiments. And a course on bioinformatics graph the area network analysis and the course on dynamical modeling. I hope you will find all of these courses both interesting and useful, both in understanding contemporary biology and in bringing in cutting edge technologies to your own research and caregotes. So let us go over the key objectives of this course. As you take the course, you might find that some part of the course are Simpler or more difficult depending on what the material is and what your background is. And you might be wondering why in this sort of material placed in this particular junction as I said before, there's no really right wrong way of organizing. This one can do it in several different ways. But irrespective of how the material is sequenced, overall when one puts together the material that I will present to you in this course, it should satisfy the following course objectives. One, learn about low-throughput and high throughput ways by which system components have been identified and studied. Two, learn about small-scale dynamical models using differential equations and what these models can tell us about systems behaviors. Three, learn about network analysis and how it can be used to obtain an understanding of systems-level organization. Four, learn how interaction between components lead to the emergence of systems level properties. And five, learn about the potential for systems level reasoning in medicine and pharmacology. Together I hope this will sort of take you to a understanding of biology or cell biology moving form components and interactions into groups are sets of components that are associated with a function and how these sub cellular functions give rise to cell and tissue level function.
